From e322b67c0f25358f0773f9d98297c5dd3fe71aba Mon Sep 17 00:00:00 2001
From: Jean-Luc Parouty <Jean-Luc.Parouty@grenoble-inp.fr>
Date: Sun, 28 Feb 2021 07:44:23 +0100
Subject: [PATCH] Add / Change Embedding notebooks
---
IMDB/01-One-hot-encoding.ipynb | 15 +-
IMDB/02-Embedding-Keras.ipynb | 1029 ----
IMDB/02-Embedding-Keras==done==.ipynb | 3522 -----------
IMDB/02-Keras-embedding.ipynb | 435 ++
IMDB/03-Prediction.ipynb | 242 +-
IMDB/03-Prediction==done==.ipynb | 445 --
IMDB/04-LSTM-Keras==done==.ipynb | 5169 -----------------
IMDB/04-Show-vectors.ipynb | 252 +
...4-LSTM-Keras.ipynb => 05-LSTM-Keras.ipynb} | 0
9 files changed, 773 insertions(+), 10336 deletions(-)
delete mode 100644 IMDB/02-Embedding-Keras.ipynb
delete mode 100644 IMDB/02-Embedding-Keras==done==.ipynb
create mode 100644 IMDB/02-Keras-embedding.ipynb
delete mode 100644 IMDB/03-Prediction==done==.ipynb
delete mode 100644 IMDB/04-LSTM-Keras==done==.ipynb
create mode 100644 IMDB/04-Show-vectors.ipynb
rename IMDB/{04-LSTM-Keras.ipynb => 05-LSTM-Keras.ipynb} (100%)
diff --git a/IMDB/01-One-hot-encoding.ipynb b/IMDB/01-One-hot-encoding.ipynb
index 71e1d9b..88d9b09 100644
--- a/IMDB/01-One-hot-encoding.ipynb
+++ b/IMDB/01-One-hot-encoding.ipynb
@@ -286,7 +286,7 @@
"\n",
"# ---- Add <pad>, <start> and <unknown> tags\n",
"#\n",
- "word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2} )\n",
+ "word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2, '<undef>':3,} )\n",
"\n",
"# ---- Create a reverse dictionary : {index:word}\n",
"#\n",
@@ -451,9 +451,10 @@
"def get_model(vector_size=10000):\n",
" \n",
" model = keras.Sequential()\n",
- " model.add(keras.layers.Dense(32, activation='relu', input_shape=(10000,)))\n",
- " model.add(keras.layers.Dense(32, activation='relu'))\n",
- " model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
+ " model.add(keras.layers.Input( shape=(vector_size,) ))\n",
+ " model.add(keras.layers.Dense( 32, activation='relu'))\n",
+ " model.add(keras.layers.Dense( 32, activation='relu'))\n",
+ " model.add(keras.layers.Dense( 1, activation='sigmoid'))\n",
" \n",
" model.compile(optimizer = 'rmsprop',\n",
" loss = 'binary_crossentropy',\n",
@@ -493,8 +494,8 @@
"metadata": {},
"outputs": [],
"source": [
- "os.makedirs('./run/models', mode=0o750, exist_ok=True)\n",
- "save_dir = \"./run/models/best_model.h5\"\n",
+ "os.makedirs(f'{run_dir}/models', mode=0o750, exist_ok=True)\n",
+ "save_dir = f'{run_dir}/models/best_model.h5'\n",
"savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
]
},
@@ -552,7 +553,7 @@
"metadata": {},
"outputs": [],
"source": [
- "model = keras.models.load_model('./run/models/best_model.h5')\n",
+ "model = keras.models.load_model(f'{run_dir}/models/best_model.h5')\n",
"\n",
"# ---- Evaluate\n",
"score = model.evaluate(x_test, y_test, verbose=0)\n",
diff --git a/IMDB/02-Embedding-Keras.ipynb b/IMDB/02-Embedding-Keras.ipynb
deleted file mode 100644
index ef069f1..0000000
--- a/IMDB/02-Embedding-Keras.ipynb
+++ /dev/null
@@ -1,1029 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [IMDB1] - Sentiment analysis with text embedding\n",
- "<!-- DESC --> A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \n",
- " - Understand the management of **textual data** and **sentiment analysis**\n",
- "\n",
- "Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)** \n",
- "Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
- "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Retrieve data\n",
- " - Preparing the data\n",
- " - Build a model\n",
- " - Train the model\n",
- " - Evaluate the result\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Init python stuff"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
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- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
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- "div.todo ul{\n",
- " margin: 0.2em;\n",
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- " margin-left:60px;\n",
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- "div .comment{\n",
- " font-size:0.8em;\n",
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- "metadata": {},
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- "data": {
- "text/markdown": [
- "**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
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- "text": [
- "Version : 0.6.1 DEV\n",
- "Notebook id : IMDB1\n",
- "Run time : Friday 18 December 2020, 17:52:06\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n",
- "Datasets dir : /home/pjluc/datasets/fidle\n",
- "Running mode : full\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "\n",
- "import tensorflow as tf\n",
- "import tensorflow.keras as keras\n",
- "import tensorflow.keras.datasets.imdb as imdb\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib\n",
- "\n",
- "import os,sys,h5py,json\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "datasets_dir = pwk.init('IMDB1')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Retrieve data\n",
- "\n",
- "IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) \n",
- "Note : Due to their nature, textual data can be somewhat complex.\n",
- "\n",
- "### 2.1 - Data structure : \n",
- "The dataset is composed of 2 parts: \n",
- "\n",
- " - **reviews**, this will be our **x**\n",
- " - **opinions** (positive/negative), this will be our **y**\n",
- "\n",
- "There are also a **dictionary**, because words are indexed in reviews\n",
- "\n",
- "```\n",
- "<dataset> = (<reviews>, <opinions>)\n",
- "\n",
- "with : <reviews> = [ <review1>, <review2>, ... ]\n",
- " <opinions> = [ <rate1>, <rate2>, ... ] where <ratei> = integer\n",
- "\n",
- "where : <reviewi> = [ <w1>, <w2>, ...] <wi> are the index (int) of the word in the dictionary\n",
- " <ratei> = int 0 for negative opinion, 1 for positive\n",
- "\n",
- "\n",
- "<dictionary> = [ <word1>:<w1>, <word2>:<w2>, ... ]\n",
- "\n",
- "with : <wordi> = word\n",
- " <wi> = int\n",
- "\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Get dataset\n",
- "For simplicity, we will use a pre-formatted dataset - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data) \n",
- "However, Keras offers some usefull tools for formatting textual data - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text) \n",
- "\n",
- "**Load dataset :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/pjluc/anaconda3/envs/fidle/lib/python3.7/site-packages/tensorflow_core/python/keras/datasets/imdb.py:129: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
- " x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])\n",
- "/home/pjluc/anaconda3/envs/fidle/lib/python3.7/site-packages/tensorflow_core/python/keras/datasets/imdb.py:130: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
- " x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])\n"
- ]
- }
- ],
- "source": [
- "vocab_size = 10000\n",
- "\n",
- "# ----- Retrieve x,y\n",
- "\n",
- "# Uncomment this if you want to load dataset directly from keras (small size <20M)\n",
- "#\n",
- "(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words = vocab_size,\n",
- " skip_top = 0,\n",
- " maxlen = None,\n",
- " seed = 42,\n",
- " start_char = 1,\n",
- " oov_char = 2,\n",
- " index_from = 3, )\n",
- "\n",
- "# To load a h5 version of the dataset :\n",
- "#\n",
- "# with h5py.File(f'{datasets_dir}/IMDB/origine/dataset_imdb.h5','r') as f:\n",
- "# x_train = f['x_train'][:]\n",
- "# y_train = f['y_train'][:]\n",
- "# x_test = f['x_test'][:]\n",
- "# y_test = f['y_test'][:]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**About this dataset :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Max(x_train,x_test) : 9999\n",
- " x_train : (25000,) y_train : (25000,)\n",
- " x_test : (25000,) y_test : (25000,)\n",
- "\n",
- "Review example (x_train[12]) :\n",
- "\n",
- " [1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]\n"
- ]
- }
- ],
- "source": [
- "print(\" Max(x_train,x_test) : \", pwk.rmax([x_train,x_test]) )\n",
- "print(\" x_train : {} y_train : {}\".format(x_train.shape, y_train.shape))\n",
- "print(\" x_test : {} y_test : {}\".format(x_test.shape, y_test.shape))\n",
- "\n",
- "print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.3 - Have a look for humans (optional)\n",
- "When we loaded the dataset, we asked for using \\<start\\> as 1, \\<unknown word\\> as 2 \n",
- "So, we shifted the dataset by 3 with the parameter index_from=3\n",
- "\n",
- "**Load dictionary :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "# ---- Retrieve dictionary {word:index}, and encode it in ascii\n",
- "#\n",
- "word_index = imdb.get_word_index()\n",
- "\n",
- "# ---- Shift the dictionary from +3\n",
- "#\n",
- "word_index = {w:(i+3) for w,i in word_index.items()}\n",
- "\n",
- "# ---- Add <pad>, <start> and unknown tags\n",
- "#\n",
- "word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2} )\n",
- "\n",
- "# ---- Create a reverse dictionary : {index:word}\n",
- "#\n",
- "index_word = {index:word for word,index in word_index.items()} \n",
- "\n",
- "# ---- Add a nice function to transpose :\n",
- "#\n",
- "def dataset2text(review):\n",
- " return ' '.join([index_word.get(i, '?') for i in review])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Have a look :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Dictionary size : 88587\n",
- "440 : hope\n",
- "441 : entertaining\n",
- "442 : she's\n",
- "443 : mr\n",
- "444 : overall\n",
- "445 : evil\n",
- "446 : called\n",
- "447 : loved\n",
- "448 : based\n",
- "449 : oh\n",
- "450 : several\n",
- "451 : fans\n",
- "452 : mother\n",
- "453 : drama\n",
- "454 : beginning\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Review example :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**After translation :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint\n"
- ]
- }
- ],
- "source": [
- "print('\\nDictionary size : ', len(word_index))\n",
- "for k in range(440,455):print(f'{k:2d} : {index_word[k]}' )\n",
- "pwk.subtitle('Review example :')\n",
- "print(x_train[12])\n",
- "pwk.subtitle('After translation :')\n",
- "print(dataset2text(x_train[12]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.4 - Have a look for NN"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/IMDB1-01-stats-sizes</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 1152x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "sizes=[len(i) for i in x_train]\n",
- "plt.figure(figsize=(16,6))\n",
- "plt.hist(sizes, bins=400)\n",
- "plt.gca().set(title='Distribution of reviews by size - [{:5.2f}, {:5.2f}]'.format(min(sizes),max(sizes)), \n",
- " xlabel='Size', ylabel='Density', xlim=[0,1500])\n",
- "pwk.save_fig('01-stats-sizes')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Preprocess the data (padding)\n",
- "In order to be processed by an NN, all entries must have the **same length.** \n",
- "We chose a review length of **review_len** \n",
- "We will therefore complete them with a padding (of \\<pad\\>\\) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**After padding :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[ 1 14 22 1367 53 206 159 4 636 898 74 26 11 436\n",
- " 363 108 7 14 432 14 22 9 1055 34 8599 2 5 381\n",
- " 3705 4509 14 768 47 839 25 111 1517 2579 1991 438 2663 587\n",
- " 4 280 725 6 58 11 2714 201 4 206 16 702 5 5176\n",
- " 19 480 5920 157 13 64 219 4 2 11 107 665 1212 39\n",
- " 4 206 4 65 410 16 565 5 24 43 343 17 5602 8\n",
- " 169 101 85 206 108 8 3008 14 25 215 168 18 6 2579\n",
- " 1991 438 2 11 129 1609 36 26 66 290 3303 46 5 633\n",
- " 115 4363 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0]\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**In real words :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n"
- ]
- }
- ],
- "source": [
- "review_len = 256\n",
- "\n",
- "x_train = keras.preprocessing.sequence.pad_sequences(x_train,\n",
- " value = 0,\n",
- " padding = 'post',\n",
- " maxlen = review_len)\n",
- "\n",
- "x_test = keras.preprocessing.sequence.pad_sequences(x_test,\n",
- " value = 0 ,\n",
- " padding = 'post',\n",
- " maxlen = review_len)\n",
- "\n",
- "pwk.subtitle('After padding :')\n",
- "print(x_train[12])\n",
- "pwk.subtitle('In real words :')\n",
- "print(dataset2text(x_train[12]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Save dataset and dictionary (For future use but not mandatory)**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Saved.\n"
- ]
- }
- ],
- "source": [
- "# ---- Write dataset in a h5 file, could be usefull\n",
- "#\n",
- "output_dir = './data'\n",
- "pwk.mkdir(output_dir)\n",
- "\n",
- "with h5py.File(f'{output_dir}/dataset_imdb.h5', 'w') as f:\n",
- " f.create_dataset(\"x_train\", data=x_train)\n",
- " f.create_dataset(\"y_train\", data=y_train)\n",
- " f.create_dataset(\"x_test\", data=x_test)\n",
- " f.create_dataset(\"y_test\", data=y_test)\n",
- "\n",
- "with open(f'{output_dir}/word_index.json', 'w') as fp:\n",
- " json.dump(word_index, fp)\n",
- "\n",
- "with open(f'{output_dir}/index_word.json', 'w') as fp:\n",
- " json.dump(index_word, fp)\n",
- "\n",
- "print('Saved.')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Build the model\n",
- "Few remarks :\n",
- " - We'll choose a dense vector size for the embedding output with **dense_vector_size**\n",
- " - **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly) \n",
- " In other words: we average the set of vectors/words of a sentence\n",
- " - L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling) \n",
- "Voir : [Explication plus détaillée (en)](https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer) \n",
- "ainsi que : [Sentiment detection with Keras](https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks) \n",
- "\n",
- "More documentation about this model functions :\n",
- " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
- " - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_model(dense_vector_size=32):\n",
- " \n",
- " model = keras.Sequential()\n",
- " model.add(keras.layers.Embedding(input_dim = vocab_size, \n",
- " output_dim = dense_vector_size, \n",
- " input_length = review_len))\n",
- " model.add(keras.layers.GlobalAveragePooling1D())\n",
- " model.add(keras.layers.Dense(dense_vector_size, activation='relu'))\n",
- " model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
- "\n",
- " model.compile(optimizer = 'adam',\n",
- " loss = 'binary_crossentropy',\n",
- " metrics = ['accuracy'])\n",
- " return model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Train the model\n",
- "### 5.1 - Get it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "embedding (Embedding) (None, 256, 32) 320000 \n",
- "_________________________________________________________________\n",
- "global_average_pooling1d (Gl (None, 32) 0 \n",
- "_________________________________________________________________\n",
- "dense (Dense) (None, 32) 1056 \n",
- "_________________________________________________________________\n",
- "dense_1 (Dense) (None, 1) 33 \n",
- "=================================================================\n",
- "Total params: 321,089\n",
- "Trainable params: 321,089\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model = get_model(32)\n",
- "\n",
- "model.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.2 - Add callback"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "os.makedirs('./run/models', mode=0o750, exist_ok=True)\n",
- "save_dir = \"./run/models/best_model.h5\"\n",
- "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.1 - Train it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Train on 25000 samples, validate on 25000 samples\n",
- "Epoch 1/30\n",
- "25000/25000 [==============================] - 1s 57us/sample - loss: 0.6881 - accuracy: 0.6431 - val_loss: 0.6782 - val_accuracy: 0.7408\n",
- "Epoch 2/30\n",
- "25000/25000 [==============================] - 1s 32us/sample - loss: 0.6506 - accuracy: 0.7618 - val_loss: 0.6168 - val_accuracy: 0.7650\n",
- "Epoch 3/30\n",
- "25000/25000 [==============================] - 1s 31us/sample - loss: 0.5590 - accuracy: 0.8060 - val_loss: 0.5135 - val_accuracy: 0.8203\n",
- "Epoch 4/30\n",
- "25000/25000 [==============================] - 1s 31us/sample - loss: 0.4460 - accuracy: 0.8512 - val_loss: 0.4198 - val_accuracy: 0.8487\n",
- "Epoch 5/30\n",
- "25000/25000 [==============================] - 1s 31us/sample - loss: 0.3606 - accuracy: 0.8758 - val_loss: 0.3636 - val_accuracy: 0.8609\n",
- "Epoch 6/30\n",
- "25000/25000 [==============================] - 1s 31us/sample - loss: 0.3074 - accuracy: 0.8894 - val_loss: 0.3322 - val_accuracy: 0.8676\n",
- "Epoch 7/30\n",
- "25000/25000 [==============================] - 1s 31us/sample - loss: 0.2719 - accuracy: 0.9016 - val_loss: 0.3127 - val_accuracy: 0.8734\n",
- "Epoch 8/30\n",
- "25000/25000 [==============================] - 1s 31us/sample - loss: 0.2457 - accuracy: 0.9103 - val_loss: 0.3007 - val_accuracy: 0.8765\n",
- "Epoch 9/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.2257 - accuracy: 0.9178 - val_loss: 0.2933 - val_accuracy: 0.8795\n",
- "Epoch 10/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.2083 - accuracy: 0.9249 - val_loss: 0.2888 - val_accuracy: 0.8818\n",
- "Epoch 11/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.1938 - accuracy: 0.9310 - val_loss: 0.2874 - val_accuracy: 0.8824\n",
- "Epoch 12/30\n",
- "25000/25000 [==============================] - 1s 31us/sample - loss: 0.1818 - accuracy: 0.9352 - val_loss: 0.2867 - val_accuracy: 0.8826\n",
- "Epoch 13/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.1703 - accuracy: 0.9406 - val_loss: 0.2879 - val_accuracy: 0.8828\n",
- "Epoch 14/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.1605 - accuracy: 0.9451 - val_loss: 0.2922 - val_accuracy: 0.8815\n",
- "Epoch 15/30\n",
- "25000/25000 [==============================] - 1s 29us/sample - loss: 0.1520 - accuracy: 0.9480 - val_loss: 0.2945 - val_accuracy: 0.8828\n",
- "Epoch 16/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.1435 - accuracy: 0.9524 - val_loss: 0.2986 - val_accuracy: 0.8821\n",
- "Epoch 17/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.1359 - accuracy: 0.9551 - val_loss: 0.3042 - val_accuracy: 0.8803\n",
- "Epoch 18/30\n",
- "25000/25000 [==============================] - 1s 29us/sample - loss: 0.1290 - accuracy: 0.9581 - val_loss: 0.3100 - val_accuracy: 0.8783\n",
- "Epoch 19/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.1223 - accuracy: 0.9609 - val_loss: 0.3169 - val_accuracy: 0.8772\n",
- "Epoch 20/30\n",
- "25000/25000 [==============================] - 1s 29us/sample - loss: 0.1167 - accuracy: 0.9632 - val_loss: 0.3245 - val_accuracy: 0.8747\n",
- "Epoch 21/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.1116 - accuracy: 0.9654 - val_loss: 0.3318 - val_accuracy: 0.8756\n",
- "Epoch 22/30\n",
- "25000/25000 [==============================] - 1s 29us/sample - loss: 0.1064 - accuracy: 0.9670 - val_loss: 0.3409 - val_accuracy: 0.8743\n",
- "Epoch 23/30\n",
- "25000/25000 [==============================] - 1s 29us/sample - loss: 0.1007 - accuracy: 0.9704 - val_loss: 0.3478 - val_accuracy: 0.8728\n",
- "Epoch 24/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.0963 - accuracy: 0.9718 - val_loss: 0.3577 - val_accuracy: 0.8718\n",
- "Epoch 25/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.0914 - accuracy: 0.9743 - val_loss: 0.3728 - val_accuracy: 0.8670\n",
- "Epoch 26/30\n",
- "25000/25000 [==============================] - 1s 29us/sample - loss: 0.0887 - accuracy: 0.9744 - val_loss: 0.3746 - val_accuracy: 0.8697\n",
- "Epoch 27/30\n",
- "25000/25000 [==============================] - 1s 29us/sample - loss: 0.0842 - accuracy: 0.9777 - val_loss: 0.3835 - val_accuracy: 0.8683\n",
- "Epoch 28/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.0796 - accuracy: 0.9788 - val_loss: 0.3937 - val_accuracy: 0.8670\n",
- "Epoch 29/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.0763 - accuracy: 0.9804 - val_loss: 0.4032 - val_accuracy: 0.8658\n",
- "Epoch 30/30\n",
- "25000/25000 [==============================] - 1s 30us/sample - loss: 0.0729 - accuracy: 0.9819 - val_loss: 0.4156 - val_accuracy: 0.8648\n",
- "CPU times: user 1min 42s, sys: 7.41 s, total: 1min 50s\n",
- "Wall time: 23.4 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "\n",
- "n_epochs = 30\n",
- "batch_size = 512\n",
- "\n",
- "history = model.fit(x_train,\n",
- " y_train,\n",
- " epochs = n_epochs,\n",
- " batch_size = batch_size,\n",
- " validation_data = (x_test, y_test),\n",
- " verbose = 1,\n",
- " callbacks = [savemodel_callback])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Evaluate\n",
- "### 6.1 - Training history"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history, save_as='02-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.2 - Reload and evaluate best model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 0.2867\n",
- "x_test / accuracy : 0.8826\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "#### Accuracy donut is :"
- ],
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- },
- "metadata": {},
- "output_type": "display_data"
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- "data": {
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- "<div class=\"comment\">Saved: ./run/figs/IMDB1-03-donut</div>"
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\n",
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- "data": {
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- "#### Confusion matrix is :"
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- },
- "metadata": {},
- "output_type": "display_data"
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- " <th id=\"T_2dfd32c2_4152_11eb_b4d1_310d57f4d2a4level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
- " <td id=\"T_2dfd32c2_4152_11eb_b4d1_310d57f4d2a4row1_col0\" class=\"data row1 col0\" >0.11</td>\n",
- " <td id=\"T_2dfd32c2_4152_11eb_b4d1_310d57f4d2a4row1_col1\" class=\"data row1 col1\" >0.89</td>\n",
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\n",
- "text/plain": [
- "<Figure size 576x576 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "model = keras.models.load_model('./run/models/best_model.h5')\n",
- "\n",
- "# ---- Evaluate\n",
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
- "print('x_test / accuracy : {:5.4f}'.format(score[1]))\n",
- "\n",
- "values=[score[1], 1-score[1]]\n",
- "pwk.plot_donut(values,[\"Accuracy\",\"Errors\"], title=\"#### Accuracy donut is :\", save_as='03-donut')\n",
- "\n",
- "# ---- Confusion matrix\n",
- "\n",
- "y_sigmoid = model.predict(x_test)\n",
- "\n",
- "y_pred = y_sigmoid.copy()\n",
- "y_pred[ y_sigmoid< 0.5 ] = 0\n",
- "y_pred[ y_sigmoid>=0.5 ] = 1 \n",
- "\n",
- "pwk.display_confusion_matrix(y_test,y_pred,labels=range(2))\n",
- "pwk.plot_confusion_matrix(y_test,y_pred,range(2), figsize=(8, 8),normalize=False, save_as='04-confusion-matrix')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Friday 18 December 2020, 17:52:43\n",
- "Duration is : 00:00:37 585ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/IMDB/02-Embedding-Keras==done==.ipynb b/IMDB/02-Embedding-Keras==done==.ipynb
deleted file mode 100644
index e8b58b5..0000000
--- a/IMDB/02-Embedding-Keras==done==.ipynb
+++ /dev/null
@@ -1,3522 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [IMDB1] - Sentiment analysis with text embedding\n",
- "<!-- DESC --> A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \n",
- " - Understand the management of **textual data** and **sentiment analysis**\n",
- "\n",
- "Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)** \n",
- "Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
- "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Retrieve data\n",
- " - Preparing the data\n",
- " - Build a model\n",
- " - Train the model\n",
- " - Evaluate the result\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Init python stuff"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:46:44.570724Z",
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- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
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- "text": [
- "Version : 2.0.14\n",
- "Notebook id : IMDB1\n",
- "Run time : Wednesday 10 February 2021, 10:46:47\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "\n",
- "import tensorflow as tf\n",
- "import tensorflow.keras as keras\n",
- "import tensorflow.keras.datasets.imdb as imdb\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib\n",
- "\n",
- "import os,sys,h5py,json\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "datasets_dir = pwk.init('IMDB1')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Retrieve data\n",
- "\n",
- "IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) \n",
- "Note : Due to their nature, textual data can be somewhat complex.\n",
- "\n",
- "### 2.1 - Data structure : \n",
- "The dataset is composed of 2 parts: \n",
- "\n",
- " - **reviews**, this will be our **x**\n",
- " - **opinions** (positive/negative), this will be our **y**\n",
- "\n",
- "There are also a **dictionary**, because words are indexed in reviews\n",
- "\n",
- "```\n",
- "<dataset> = (<reviews>, <opinions>)\n",
- "\n",
- "with : <reviews> = [ <review1>, <review2>, ... ]\n",
- " <opinions> = [ <rate1>, <rate2>, ... ] where <ratei> = integer\n",
- "\n",
- "where : <reviewi> = [ <w1>, <w2>, ...] <wi> are the index (int) of the word in the dictionary\n",
- " <ratei> = int 0 for negative opinion, 1 for positive\n",
- "\n",
- "\n",
- "<dictionary> = [ <word1>:<w1>, <word2>:<w2>, ... ]\n",
- "\n",
- "with : <wordi> = word\n",
- " <wi> = int\n",
- "\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Get dataset\n",
- "For simplicity, we will use a pre-formatted dataset - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data) \n",
- "However, Keras offers some usefull tools for formatting textual data - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text) \n",
- "\n",
- "**Load dataset :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:46:47.138812Z",
- "iopub.status.busy": "2021-02-10T09:46:47.138326Z",
- "iopub.status.idle": "2021-02-10T09:46:52.335843Z",
- "shell.execute_reply": "2021-02-10T09:46:52.336330Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "<string>:6: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/keras/datasets/imdb.py:159: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
- " x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/keras/datasets/imdb.py:160: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
- " x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])\n"
- ]
- }
- ],
- "source": [
- "vocab_size = 10000\n",
- "\n",
- "# ----- Retrieve x,y\n",
- "\n",
- "# Uncomment this if you want to load dataset directly from keras (small size <20M)\n",
- "#\n",
- "(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words = vocab_size,\n",
- " skip_top = 0,\n",
- " maxlen = None,\n",
- " seed = 42,\n",
- " start_char = 1,\n",
- " oov_char = 2,\n",
- " index_from = 3, )\n",
- "\n",
- "# To load a h5 version of the dataset :\n",
- "#\n",
- "# with h5py.File(f'{datasets_dir}/IMDB/origine/dataset_imdb.h5','r') as f:\n",
- "# x_train = f['x_train'][:]\n",
- "# y_train = f['y_train'][:]\n",
- "# x_test = f['x_test'][:]\n",
- "# y_test = f['y_test'][:]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**About this dataset :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:46:52.358732Z",
- "iopub.status.busy": "2021-02-10T09:46:52.348599Z",
- "iopub.status.idle": "2021-02-10T09:46:57.867248Z",
- "shell.execute_reply": "2021-02-10T09:46:57.867741Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Max(x_train,x_test) : 9999\n",
- " x_train : (25000,) y_train : (25000,)\n",
- " x_test : (25000,) y_test : (25000,)\n",
- "\n",
- "Review example (x_train[12]) :\n",
- "\n",
- " [1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]\n"
- ]
- }
- ],
- "source": [
- "print(\" Max(x_train,x_test) : \", pwk.rmax([x_train,x_test]) )\n",
- "print(\" x_train : {} y_train : {}\".format(x_train.shape, y_train.shape))\n",
- "print(\" x_test : {} y_test : {}\".format(x_test.shape, y_test.shape))\n",
- "\n",
- "print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.3 - Have a look for humans (optional)\n",
- "When we loaded the dataset, we asked for using \\<start\\> as 1, \\<unknown word\\> as 2 \n",
- "So, we shifted the dataset by 3 with the parameter index_from=3\n",
- "\n",
- "**Load dictionary :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:46:57.872464Z",
- "iopub.status.busy": "2021-02-10T09:46:57.871988Z",
- "iopub.status.idle": "2021-02-10T09:46:57.981221Z",
- "shell.execute_reply": "2021-02-10T09:46:57.981725Z"
- }
- },
- "outputs": [],
- "source": [
- "# ---- Retrieve dictionary {word:index}, and encode it in ascii\n",
- "#\n",
- "word_index = imdb.get_word_index()\n",
- "\n",
- "# ---- Shift the dictionary from +3\n",
- "#\n",
- "word_index = {w:(i+3) for w,i in word_index.items()}\n",
- "\n",
- "# ---- Add <pad>, <start> and unknown tags\n",
- "#\n",
- "word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2} )\n",
- "\n",
- "# ---- Create a reverse dictionary : {index:word}\n",
- "#\n",
- "index_word = {index:word for word,index in word_index.items()} \n",
- "\n",
- "# ---- Add a nice function to transpose :\n",
- "#\n",
- "def dataset2text(review):\n",
- " return ' '.join([index_word.get(i, '?') for i in review])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Have a look :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:46:57.986075Z",
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- "shell.execute_reply": "2021-02-10T09:46:57.992011Z"
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- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Dictionary size : 88587\n",
- "440 : hope\n",
- "441 : entertaining\n",
- "442 : she's\n",
- "443 : mr\n",
- "444 : overall\n",
- "445 : evil\n",
- "446 : called\n",
- "447 : loved\n",
- "448 : based\n",
- "449 : oh\n",
- "450 : several\n",
- "451 : fans\n",
- "452 : mother\n",
- "453 : drama\n",
- "454 : beginning\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Review example :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**After translation :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint\n"
- ]
- }
- ],
- "source": [
- "print('\\nDictionary size : ', len(word_index))\n",
- "for k in range(440,455):print(f'{k:2d} : {index_word[k]}' )\n",
- "pwk.subtitle('Review example :')\n",
- "print(x_train[12])\n",
- "pwk.subtitle('After translation :')\n",
- "print(dataset2text(x_train[12]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.4 - Have a look for NN"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:46:58.010968Z",
- "iopub.status.busy": "2021-02-10T09:46:58.006023Z",
- "iopub.status.idle": "2021-02-10T09:47:00.388957Z",
- "shell.execute_reply": "2021-02-10T09:47:00.389453Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/IMDB1-01-stats-sizes</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 1152x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "sizes=[len(i) for i in x_train]\n",
- "plt.figure(figsize=(16,6))\n",
- "plt.hist(sizes, bins=400)\n",
- "plt.gca().set(title='Distribution of reviews by size - [{:5.2f}, {:5.2f}]'.format(min(sizes),max(sizes)), \n",
- " xlabel='Size', ylabel='Density', xlim=[0,1500])\n",
- "pwk.save_fig('01-stats-sizes')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Preprocess the data (padding)\n",
- "In order to be processed by an NN, all entries must have the **same length.** \n",
- "We chose a review length of **review_len** \n",
- "We will therefore complete them with a padding (of \\<pad\\>\\) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:00.398165Z",
- "iopub.status.busy": "2021-02-10T09:47:00.397691Z",
- "iopub.status.idle": "2021-02-10T09:47:01.453550Z",
- "shell.execute_reply": "2021-02-10T09:47:01.454034Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**After padding :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[ 1 14 22 1367 53 206 159 4 636 898 74 26 11 436\n",
- " 363 108 7 14 432 14 22 9 1055 34 8599 2 5 381\n",
- " 3705 4509 14 768 47 839 25 111 1517 2579 1991 438 2663 587\n",
- " 4 280 725 6 58 11 2714 201 4 206 16 702 5 5176\n",
- " 19 480 5920 157 13 64 219 4 2 11 107 665 1212 39\n",
- " 4 206 4 65 410 16 565 5 24 43 343 17 5602 8\n",
- " 169 101 85 206 108 8 3008 14 25 215 168 18 6 2579\n",
- " 1991 438 2 11 129 1609 36 26 66 290 3303 46 5 633\n",
- " 115 4363 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0]\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**In real words :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n"
- ]
- }
- ],
- "source": [
- "review_len = 256\n",
- "\n",
- "x_train = keras.preprocessing.sequence.pad_sequences(x_train,\n",
- " value = 0,\n",
- " padding = 'post',\n",
- " maxlen = review_len)\n",
- "\n",
- "x_test = keras.preprocessing.sequence.pad_sequences(x_test,\n",
- " value = 0 ,\n",
- " padding = 'post',\n",
- " maxlen = review_len)\n",
- "\n",
- "pwk.subtitle('After padding :')\n",
- "print(x_train[12])\n",
- "pwk.subtitle('In real words :')\n",
- "print(dataset2text(x_train[12]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Save dataset and dictionary (For future use but not mandatory)**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:01.458973Z",
- "iopub.status.busy": "2021-02-10T09:47:01.458506Z",
- "iopub.status.idle": "2021-02-10T09:47:01.946304Z",
- "shell.execute_reply": "2021-02-10T09:47:01.946796Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Saved.\n"
- ]
- }
- ],
- "source": [
- "# ---- Write dataset in a h5 file, could be usefull\n",
- "#\n",
- "output_dir = './data'\n",
- "pwk.mkdir(output_dir)\n",
- "\n",
- "with h5py.File(f'{output_dir}/dataset_imdb.h5', 'w') as f:\n",
- " f.create_dataset(\"x_train\", data=x_train)\n",
- " f.create_dataset(\"y_train\", data=y_train)\n",
- " f.create_dataset(\"x_test\", data=x_test)\n",
- " f.create_dataset(\"y_test\", data=y_test)\n",
- "\n",
- "with open(f'{output_dir}/word_index.json', 'w') as fp:\n",
- " json.dump(word_index, fp)\n",
- "\n",
- "with open(f'{output_dir}/index_word.json', 'w') as fp:\n",
- " json.dump(index_word, fp)\n",
- "\n",
- "print('Saved.')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Build the model\n",
- "Few remarks :\n",
- " - We'll choose a dense vector size for the embedding output with **dense_vector_size**\n",
- " - **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly) \n",
- " In other words: we average the set of vectors/words of a sentence\n",
- " - L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling) \n",
- "Voir : [Explication plus détaillée (en)](https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer) \n",
- "ainsi que : [Sentiment detection with Keras](https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks) \n",
- "\n",
- "More documentation about this model functions :\n",
- " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
- " - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:01.951801Z",
- "iopub.status.busy": "2021-02-10T09:47:01.951325Z",
- "iopub.status.idle": "2021-02-10T09:47:01.952959Z",
- "shell.execute_reply": "2021-02-10T09:47:01.953425Z"
- }
- },
- "outputs": [],
- "source": [
- "def get_model(dense_vector_size=32):\n",
- " \n",
- " model = keras.Sequential()\n",
- " model.add(keras.layers.Embedding(input_dim = vocab_size, \n",
- " output_dim = dense_vector_size, \n",
- " input_length = review_len))\n",
- " model.add(keras.layers.GlobalAveragePooling1D())\n",
- " model.add(keras.layers.Dense(dense_vector_size, activation='relu'))\n",
- " model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
- "\n",
- " model.compile(optimizer = 'adam',\n",
- " loss = 'binary_crossentropy',\n",
- " metrics = ['accuracy'])\n",
- " return model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Train the model\n",
- "### 5.1 - Get it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:01.956429Z",
- "iopub.status.busy": "2021-02-10T09:47:01.955971Z",
- "iopub.status.idle": "2021-02-10T09:47:03.003416Z",
- "shell.execute_reply": "2021-02-10T09:47:03.002908Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "embedding (Embedding) (None, 256, 32) 320000 \n",
- "_________________________________________________________________\n",
- "global_average_pooling1d (Gl (None, 32) 0 \n",
- "_________________________________________________________________\n",
- "dense (Dense) (None, 32) 1056 \n",
- "_________________________________________________________________\n",
- "dense_1 (Dense) (None, 1) 33 \n",
- "=================================================================\n",
- "Total params: 321,089\n",
- "Trainable params: 321,089\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model = get_model(32)\n",
- "\n",
- "model.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.2 - Add callback"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:03.006971Z",
- "iopub.status.busy": "2021-02-10T09:47:03.006506Z",
- "iopub.status.idle": "2021-02-10T09:47:03.025746Z",
- "shell.execute_reply": "2021-02-10T09:47:03.026218Z"
- }
- },
- "outputs": [],
- "source": [
- "os.makedirs('./run/models', mode=0o750, exist_ok=True)\n",
- "save_dir = \"./run/models/best_model.h5\"\n",
- "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.1 - Train it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:03.030265Z",
- "iopub.status.busy": "2021-02-10T09:47:03.029797Z",
- "iopub.status.idle": "2021-02-10T09:47:27.105268Z",
- "shell.execute_reply": "2021-02-10T09:47:27.105771Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/30\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 1:01 - loss: 0.6933 - accuracy: 0.4902"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 0s - loss: 0.6932 - accuracy: 0.5039 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "12/49 [======>.......................] - ETA: 0s - loss: 0.6931 - accuracy: 0.5110"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "18/49 [==========>...................] - ETA: 0s - loss: 0.6930 - accuracy: 0.5118"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "24/49 [=============>................] - ETA: 0s - loss: 0.6929 - accuracy: 0.5110"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "30/49 [=================>............] - ETA: 0s - loss: 0.6928 - accuracy: 0.5136"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "36/49 [=====================>........] - ETA: 0s - loss: 0.6926 - accuracy: 0.5205"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 0s - loss: 0.6925 - accuracy: 0.5282"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.6922 - accuracy: 0.5358"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 3s 31ms/step - loss: 0.6921 - accuracy: 0.5383 - val_loss: 0.6828 - val_accuracy: 0.6852\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 2/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.6817 - accuracy: 0.7070"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 0s - loss: 0.6808 - accuracy: 0.6970"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 0s - loss: 0.6802 - accuracy: 0.6879"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.6791 - accuracy: 0.6897"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.6779 - accuracy: 0.6927"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 0s - loss: 0.6766 - accuracy: 0.6953"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 0s - loss: 0.6752 - accuracy: 0.6979"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 0s - loss: 0.6738 - accuracy: 0.7006"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.6722 - accuracy: 0.7035"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.6719 - accuracy: 0.7040 - val_loss: 0.6262 - val_accuracy: 0.7627\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 3/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.6266 - accuracy: 0.7480"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 0s - loss: 0.6186 - accuracy: 0.7796"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 0s - loss: 0.6158 - accuracy: 0.7807"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.6128 - accuracy: 0.7821"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.6096 - accuracy: 0.7830"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 0s - loss: 0.6064 - accuracy: 0.7841"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.5958 - accuracy: 0.7886 - val_loss: 0.5218 - val_accuracy: 0.8146\n"
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- "Epoch 4/30\n",
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.4934 - accuracy: 0.8381"
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- "49/49 [==============================] - ETA: 0s - loss: 0.4801 - accuracy: 0.8420"
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.4796 - accuracy: 0.8421 - val_loss: 0.4256 - val_accuracy: 0.8419\n"
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- "Epoch 5/30\n",
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- " 7/49 [===>..........................] - ETA: 0s - loss: 0.3973 - accuracy: 0.8659"
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- "13/49 [======>.......................] - ETA: 0s - loss: 0.3974 - accuracy: 0.8651"
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- "19/49 [==========>...................] - ETA: 0s - loss: 0.3959 - accuracy: 0.8656"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.3938 - accuracy: 0.8664"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.3844 - accuracy: 0.8694"
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.3841 - accuracy: 0.8695 - val_loss: 0.3668 - val_accuracy: 0.8599\n"
- ]
- },
- {
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- "text": [
- "Epoch 6/30\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "Epoch 7/30\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "31/49 [=================>............] - ETA: 0s - loss: 0.2835 - accuracy: 0.8993"
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- "49/49 [==============================] - ETA: 0s - loss: 0.2809 - accuracy: 0.8999"
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.2808 - accuracy: 0.8999 - val_loss: 0.3156 - val_accuracy: 0.8719\n"
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- "19/49 [==========>...................] - ETA: 0s - loss: 0.2581 - accuracy: 0.9053"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.2564 - accuracy: 0.9067"
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- "49/49 [==============================] - ETA: 0s - loss: 0.2535 - accuracy: 0.9083"
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.2534 - accuracy: 0.9083 - val_loss: 0.3022 - val_accuracy: 0.8760\n"
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- "text": [
- "Epoch 9/30\n",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.2391 - accuracy: 0.9110"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.2381 - accuracy: 0.9118"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.2347 - accuracy: 0.9142"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.2345 - accuracy: 0.9142 - val_loss: 0.2940 - val_accuracy: 0.8792\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Epoch 10/30\n",
- "\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- },
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.2110 - accuracy: 0.9256 - val_loss: 0.2896 - val_accuracy: 0.8812\n"
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- },
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- "output_type": "stream",
- "text": [
- "Epoch 11/30\n",
- "\r",
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- "13/49 [======>.......................] - ETA: 0s - loss: 0.2014 - accuracy: 0.9292"
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- "19/49 [==========>...................] - ETA: 0s - loss: 0.2003 - accuracy: 0.9293"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.1997 - accuracy: 0.9291"
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- "49/49 [==============================] - ETA: 0s - loss: 0.1989 - accuracy: 0.9295"
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.1989 - accuracy: 0.9295 - val_loss: 0.2869 - val_accuracy: 0.8822\n"
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- "Epoch 12/30\n",
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- },
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- "13/49 [======>.......................] - ETA: 0s - loss: 0.1775 - accuracy: 0.9392"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.1779 - accuracy: 0.9389"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.1779 - accuracy: 0.9390"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 0s - loss: 0.1785 - accuracy: 0.9387"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 0s - loss: 0.1796 - accuracy: 0.9382"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1800 - accuracy: 0.9379"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.1801 - accuracy: 0.9379 - val_loss: 0.2868 - val_accuracy: 0.8837\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Epoch 13/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1818 - accuracy: 0.9316"
- ]
- },
- {
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- "text": [
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- " 7/49 [===>..........................] - ETA: 0s - loss: 0.1774 - accuracy: 0.9359"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 0s - loss: 0.1785 - accuracy: 0.9369"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.1795 - accuracy: 0.9369"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1794 - accuracy: 0.9368"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 0s - loss: 0.1789 - accuracy: 0.9371"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 0s - loss: 0.1773 - accuracy: 0.9380"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1768 - accuracy: 0.9383"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.1767 - accuracy: 0.9383 - val_loss: 0.2902 - val_accuracy: 0.8819\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 14/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1752 - accuracy: 0.9375"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 0s - loss: 0.1669 - accuracy: 0.9424"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.1660 - accuracy: 0.9430"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1656 - accuracy: 0.9433"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "37/49 [=====================>........] - ETA: 0s - loss: 0.1652 - accuracy: 0.9434"
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- "49/49 [==============================] - ETA: 0s - loss: 0.1649 - accuracy: 0.9435"
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.1648 - accuracy: 0.9435 - val_loss: 0.2910 - val_accuracy: 0.8811\n"
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- "19/49 [==========>...................] - ETA: 0s - loss: 0.1542 - accuracy: 0.9502"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.1540 - accuracy: 0.9499"
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- "31/49 [=================>............] - ETA: 0s - loss: 0.1541 - accuracy: 0.9494"
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- "43/49 [=========================>....] - ETA: 0s - loss: 0.1540 - accuracy: 0.9489"
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- "49/49 [==============================] - ETA: 0s - loss: 0.1540 - accuracy: 0.9488"
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.1540 - accuracy: 0.9487 - val_loss: 0.2943 - val_accuracy: 0.8825\n"
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- },
- {
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- "text": [
- "Epoch 16/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1562 - accuracy: 0.9473"
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- },
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- " 7/49 [===>..........................] - ETA: 0s - loss: 0.1416 - accuracy: 0.9533"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 0s - loss: 0.1410 - accuracy: 0.9542"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.1404 - accuracy: 0.9546"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.1403 - accuracy: 0.9547"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 0s - loss: 0.1403 - accuracy: 0.9547"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 0s - loss: 0.1409 - accuracy: 0.9544"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 0s - loss: 0.1414 - accuracy: 0.9541"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1419 - accuracy: 0.9539"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.1419 - accuracy: 0.9539 - val_loss: 0.2984 - val_accuracy: 0.8818\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Epoch 17/30\n",
- "\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 0s - loss: 0.1447 - accuracy: 0.9542"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.1429 - accuracy: 0.9546"
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- },
- {
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.1419 - accuracy: 0.9548"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1398 - accuracy: 0.9550"
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.1397 - accuracy: 0.9550 - val_loss: 0.3030 - val_accuracy: 0.8810\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "Epoch 18/30\n",
- "\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "13/49 [======>.......................] - ETA: 0s - loss: 0.1294 - accuracy: 0.9561"
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- "19/49 [==========>...................] - ETA: 0s - loss: 0.1296 - accuracy: 0.9562"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.1296 - accuracy: 0.9564"
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- "31/49 [=================>............] - ETA: 0s - loss: 0.1297 - accuracy: 0.9566"
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- "37/49 [=====================>........] - ETA: 0s - loss: 0.1299 - accuracy: 0.9566"
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- "43/49 [=========================>....] - ETA: 0s - loss: 0.1302 - accuracy: 0.9566"
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- "49/49 [==============================] - ETA: 0s - loss: 0.1304 - accuracy: 0.9566"
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.1304 - accuracy: 0.9566 - val_loss: 0.3097 - val_accuracy: 0.8803\n"
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- "Epoch 19/30\n",
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- " 1/49 [..............................] - ETA: 0s - loss: 0.1278 - accuracy: 0.9590"
- ]
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- " 7/49 [===>..........................] - ETA: 0s - loss: 0.1235 - accuracy: 0.9626"
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- "13/49 [======>.......................] - ETA: 0s - loss: 0.1237 - accuracy: 0.9621"
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- "19/49 [==========>...................] - ETA: 0s - loss: 0.1234 - accuracy: 0.9621"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.1230 - accuracy: 0.9620"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 0s - loss: 0.1229 - accuracy: 0.9618"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 0s - loss: 0.1228 - accuracy: 0.9617"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 0s - loss: 0.1229 - accuracy: 0.9616"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1231 - accuracy: 0.9615"
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- },
- {
- "name": "stdout",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.1231 - accuracy: 0.9614 - val_loss: 0.3163 - val_accuracy: 0.8770\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 20/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1168 - accuracy: 0.9609"
- ]
- },
- {
- "name": "stdout",
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- " 7/49 [===>..........................] - ETA: 0s - loss: 0.1226 - accuracy: 0.9589"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 0s - loss: 0.1207 - accuracy: 0.9604"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.1197 - accuracy: 0.9610"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1193 - accuracy: 0.9615"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 0s - loss: 0.1190 - accuracy: 0.9620"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 0s - loss: 0.1187 - accuracy: 0.9622"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 0s - loss: 0.1186 - accuracy: 0.9623"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1184 - accuracy: 0.9624"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.1184 - accuracy: 0.9624 - val_loss: 0.3243 - val_accuracy: 0.8764\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 21/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1182 - accuracy: 0.9629"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 0s - loss: 0.1180 - accuracy: 0.9645"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.1155 - accuracy: 0.9657"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.1145 - accuracy: 0.9659"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 0s - loss: 0.1141 - accuracy: 0.9659"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "43/49 [=========================>....] - ETA: 0s - loss: 0.1136 - accuracy: 0.9658"
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- "49/49 [==============================] - ETA: 0s - loss: 0.1136 - accuracy: 0.9656"
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.1136 - accuracy: 0.9656 - val_loss: 0.3320 - val_accuracy: 0.8748\n"
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- "text": [
- "Epoch 22/30\n",
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- "19/49 [==========>...................] - ETA: 0s - loss: 0.1130 - accuracy: 0.9674"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.1120 - accuracy: 0.9672"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 0s - loss: 0.1113 - accuracy: 0.9670"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "49/49 [==============================] - ETA: 0s - loss: 0.1096 - accuracy: 0.9669"
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.1096 - accuracy: 0.9669 - val_loss: 0.3408 - val_accuracy: 0.8736\n"
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- },
- {
- "name": "stdout",
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- "text": [
- "Epoch 23/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1202 - accuracy: 0.9629"
- ]
- },
- {
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- " 7/49 [===>..........................] - ETA: 0s - loss: 0.1116 - accuracy: 0.9665"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 0s - loss: 0.1090 - accuracy: 0.9676"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.1067 - accuracy: 0.9684"
- ]
- },
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.1054 - accuracy: 0.9688"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 0s - loss: 0.1046 - accuracy: 0.9691"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 0s - loss: 0.1042 - accuracy: 0.9695"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.1039 - accuracy: 0.9695"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.1039 - accuracy: 0.9695 - val_loss: 0.3508 - val_accuracy: 0.8709\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 24/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.1081 - accuracy: 0.9668"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.0962 - accuracy: 0.9734"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.0971 - accuracy: 0.9730"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 0s - loss: 0.0971 - accuracy: 0.9729"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.0971 - accuracy: 0.9724"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.0971 - accuracy: 0.9724 - val_loss: 0.3573 - val_accuracy: 0.8711\n"
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- },
- {
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- "output_type": "stream",
- "text": [
- "Epoch 25/30\n",
- "\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "19/49 [==========>...................] - ETA: 0s - loss: 0.0879 - accuracy: 0.9770"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.0882 - accuracy: 0.9769"
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- "49/49 [==============================] - ETA: 0s - loss: 0.0897 - accuracy: 0.9757"
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.0897 - accuracy: 0.9756 - val_loss: 0.3677 - val_accuracy: 0.8694\n"
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- "13/49 [======>.......................] - ETA: 0s - loss: 0.0847 - accuracy: 0.9783"
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- "19/49 [==========>...................] - ETA: 0s - loss: 0.0842 - accuracy: 0.9779"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.0843 - accuracy: 0.9774"
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- "31/49 [=================>............] - ETA: 0s - loss: 0.0846 - accuracy: 0.9771"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.0854 - accuracy: 0.9764"
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.0855 - accuracy: 0.9764 - val_loss: 0.3756 - val_accuracy: 0.8700\n"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "Epoch 27/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.0905 - accuracy: 0.9707"
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- },
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- " 7/49 [===>..........................] - ETA: 0s - loss: 0.0879 - accuracy: 0.9758"
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 0s - loss: 0.0852 - accuracy: 0.9770"
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- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.0841 - accuracy: 0.9773"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 0s - loss: 0.0834 - accuracy: 0.9775"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 0s - loss: 0.0831 - accuracy: 0.9775"
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 0s - loss: 0.0831 - accuracy: 0.9774"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 0s - loss: 0.0831 - accuracy: 0.9773"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.0832 - accuracy: 0.9772"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.0832 - accuracy: 0.9772 - val_loss: 0.3855 - val_accuracy: 0.8676\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 28/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.0732 - accuracy: 0.9805"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.0702 - accuracy: 0.9829"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 0s - loss: 0.0724 - accuracy: 0.9821"
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- " 7/49 [===>..........................] - ETA: 0s - loss: 0.0766 - accuracy: 0.9779"
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- "13/49 [======>.......................] - ETA: 0s - loss: 0.0804 - accuracy: 0.9764"
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- "19/49 [==========>...................] - ETA: 0s - loss: 0.0803 - accuracy: 0.9764"
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- "25/49 [==============>...............] - ETA: 0s - loss: 0.0797 - accuracy: 0.9767"
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- "49/49 [==============================] - ETA: 0s - loss: 0.0788 - accuracy: 0.9775"
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- "name": "stdout",
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- "49/49 [==============================] - 1s 14ms/step - loss: 0.0788 - accuracy: 0.9776 - val_loss: 0.4090 - val_accuracy: 0.8645\n"
- ]
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- "name": "stdout",
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- "text": [
- "Epoch 30/30\n",
- "\r",
- " 1/49 [..............................] - ETA: 0s - loss: 0.0839 - accuracy: 0.9746"
- ]
- },
- {
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- " 7/49 [===>..........................] - ETA: 0s - loss: 0.0760 - accuracy: 0.9802"
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- "13/49 [======>.......................] - ETA: 0s - loss: 0.0758 - accuracy: 0.9805"
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 0s - loss: 0.0749 - accuracy: 0.9811"
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 1s 14ms/step - loss: 0.0737 - accuracy: 0.9812 - val_loss: 0.4224 - val_accuracy: 0.8634\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 41.1 s, sys: 3.59 s, total: 44.7 s\n",
- "Wall time: 24.1 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "\n",
- "n_epochs = 30\n",
- "batch_size = 512\n",
- "\n",
- "history = model.fit(x_train,\n",
- " y_train,\n",
- " epochs = n_epochs,\n",
- " batch_size = batch_size,\n",
- " validation_data = (x_test, y_test),\n",
- " verbose = 1,\n",
- " callbacks = [savemodel_callback])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Evaluate\n",
- "### 6.1 - Training history"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:27.120910Z",
- "iopub.status.busy": "2021-02-10T09:47:27.116323Z",
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- "shell.execute_reply": "2021-02-10T09:47:28.329822Z"
- }
- },
- "outputs": [
- {
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\n",
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jFvpCt4TGwYiI9H8KDK2Ii4ujoKCge34hJiZDVLR77KtzsyfqD8VFNzwurejdgcFaS0FBAXFxcW2fLCIifZa6JFoxfPhwsrOzOXz4cPf8gKqKxkGPBw7BwHSIiMDnsxwuaVyMqSQ3joiIDg1m7VFxcXEMHz481NUQEZFupMDQiujoaMaMGdN9P6CuDh66HXIPuOefuwa+cBMA3/jDJ6zfVwjA3RdN5+KTRnVfPURERNqgLolQioyESwNmNPz7TTcAEjhjSmZD8ZLNh3q2XiIiIs0oMITanPlu8SZwXRTvvALA6ZMbA8PaPQWUlFeHoHIiIiKOAkOoRUTAF25sfP7+3+FIAYOSBzA5ayAAPmv5ZFvHdqQUERHpSgoMvcHsU2HUBPe4phreeRWAMwJaGT7com4JEREJHQWG3sAYuPT6xuerPwRrOWPK0IaiT3flN6wAKSIi0tMUGHqLaXNgQIJ7XHgYDu5jaGo84zOTAaip87Fse14IKygiIuFMgaG3iIyEKbMbn29YBTQd/Pjh5oM9XCkRERFHgaE3mT6n8fFGFxgCuyVW7jxMZXVtT9dKREREgaFXmXZS4+Nt66G6ipEZiYzMcFtWVtX6WLGjm1adFBERaYUCQ2+SPhgyR7jHNdUuNABnBrQyaLaEiIiEggJDbxPYLbGhvluicRzDsu25VNfW9XStREQkzIU8MBhjIowx9xljthhjKo0x+40xjxtjEjpwjShjzN3GmNXGmDJjTLH/8Ve7s+7dYnpAt4R/HMOYwUkMS4sHoKK6jlU780NRMxERCWMhDwzAE8BPgU3AXcArwN3A340xbdbPGBMD/AP4CbAGuA/4NrAI6Hs7Nk2cAdEx7vHBfVCQhzGGMyYHdktotoSIiPSskO5WaYyZhgsJr1trLwso3w38HLgaeLGNyzwInAt81lr7QXfVtcfExMLE6bBxtXu+cRXMv5AzpmTy8sc7AVi6LZeaOh/Rkb0h74mISDgI9W+cawADPNms/NdAOXB98xcE8ndb3AP81Vr7gXGSuqOiPWrasd0SE4emMDhlAACllbWs3VMQipqJiEiYCnVgmAv4gOWBhdbaSlz3wtw2Xn8mkASsMsb8DCgBSowxh40xjxpjQtqC0mmBAx83fQp1df5uicAtr9UtISIiPSfUgWEYkG+trQpy7ACQ4R+j0JJJ/vt7gcuAbwJXAR/jxjH8tuuq2oOGjoTUDPe4ogx2bQGazpb4ZGsudT5fKGonIiJhKNSBIR4IFhYAKgPOaUl990MacK619hlr7cvW2kuBhcCNxpipLb3YGHO7MWZlB+vc/YwJuurjlOGppCXGAlBcXs36fYWhqJ2IiIShUAeGciC2hWNxAee0pMJ/v9Rau6XZsef99wtaerG19llr7ZyWjodU4DiGDS7TRBjTbG8JLeIkIiI9I9SBIQfX7RAsNGThuiuqW3l9tv8+2G/O+k7+1OOoX+hMmQ31s0r3boejxQBNxjF8tOUQPmtDUDkREQk3oQ4MK/x1mBdYaIyJA2YDbXUX1A+WHB7kWH1Z39wTOiEJxk52j62FzZ8CMGNUGinxblhHYWkVm7OLQlVDEREJI6EODC8BFjdoMdBtuLELL9QXGGOGGmMmG2MaxjRYa3cDHwHzjDEnBpwb6b9GLfCvbqt9d5t+bLdEZEQEp04a0lCsbgkREekJIQ0M1tr1wNPAl4wxrxtjbjXGPI5b+XERTRdtegzYTLPWCNzCT+XAe8aYR4wxd/lfOw941Fq7r7vfR7dpsh7DatfSwLHdElbdEiIi0s1C3cIArnXhG8A0XHi4GngK+Jy1ts15g9baT4HTgA/91/oJkADcYq19uHuq3ENGT3BdEwDFhZC9G4DZYzJIiHVLTOQWV7D9YHGoaigiImEi5IHBWltnrX3cWjvJWhtrrc2y1n7dWlva7LybrbXGWrswyDXWWWs/b60daK2Ns9aeYK19rqfeQ7eJiISpJzY+9+9eGR0ZwSkT1S0hIiI9J+SBQdoQZPdKgDOnNG5GtWTLQXVLiIhIt1Jg6O0CxzFs3wCVbumJk8ZlMCAmEoCcwnJ25x0NRe1ERCRMKDD0dgPTIWu0e1xXC1vXARATFcm88YMbTlO3hIiIdCcFhr4gyDLRAGcEdEt8uEWbUYmISPdRYOgLgqzHADB3/CBiotwf4d7DpezLL23+ShERkS6hwNAXjJ8GMf7Vs/Ny4LBrTRgQE8WccYMaTvtQW16LiEg3UWDoC6JjYPKsxucbgs+WWL6jb66CLSIivZ8CQ1/RZNXHxm6JkwJaGLblFFNRXduTtRIRkTChwNBXBA583LwWamsASImPYcxgtxpknc+yYV9hKGonIiL9nAJDXzF4GGT495CoqoAdmxoOzRqd3vB47Z6Cnq6ZiIiEAQWGvsKYZt0SjeMYZo0KCAx7FRhERKTrKTD0JS2sxzBjVDrG/3jHwWLKKmt6tl4iItLvKTD0JZNnQqRbDpp9O6G4CICkAdGMy0wGwGdhvcYxiIhIF1Ng6EsGJMC4qY3PNzW2MswcrW4JERHpPgoMfU2TVR+Dj2NYp4GPIiLSxRQY+pomAx9Xg88HwIyRaUT4BzLsPFRCSUV1CConIiL9lQJDXzNiHCQNdI9Li91YBiAhLprxQ1MAsKD1GEREpEspMPQ1EREw7cTG5wGbUTWZXqluCRER6UIKDH1RS+sxaAEnERHpJgoMfVFgYNi1GcrLXPGINCKMG8iwO+8oxeUaxyAiIl1DgaEvSh4II8e7x3V1sGUNAPGxUUwaltJw2jpNrxQRkS6iwNBXtbDq40x1S4iISDdQYOirmqzHsBKsBTSOQUREuocCQ181dgrExbvHBXmQmw24cQxR/gUZ9uWXUlRaFaoaiohIP6LA0FdFRcGU2Y3P/as+xkVHMilrYEOxlokWEZGuoMDQl01rYZlodUuIiEgXU2DoywIDw7Z1UOOmUQYGBu0rISIiXUGBoS8blAlDstzj6irYvgGAqcNTiY50f7TZhWUUHK0MVQ1FRKSfUGDo64J0S8RERTJl+MCGYnVLiIjI8VJg6OsC12NYt7zh4azRGQ2PNfBRRESOlwJDXzd5FsTEuseH9kPuAUADH0VEpGspMPR1MbEw9YTG52uXAjBpWAoxUe6P92BROXnFFaGonYiI9BMKDP3BrFMaH69dBrhxDFNHpDYWq5VBRESOgwJDfzBzXuPj7Rug7CgAs0YFdEtoHIOIiByHkAcGY0yEMeY+Y8wWY0ylMWa/MeZxY0xCO1+/0BhjW7jNafsK/UBKGoyd7B77fG5vCbQeg4iIdJ2oUFcAeAK4G3gDeByY4n9+gjHmXGutrx3XyAfuC1K+q8tq2dvNPBl2bXGP1yyFk89m0rCBxEVHUllTR25xBYeKyslMjQ9tPUVEpE8KaWAwxkwD7gJet9ZeFlC+G/g5cDXwYjsuVWat/VP31LKPmHUyvPkH93jDSqitISoqmmkj01i18zDguiUUGEREpDNC3SVxDWCAJ5uV/xooB65v74X8XRvJxhjTddXrQ4aPgfTB7nFFWcOqj03GMahbQkREOinUgWEu4AOWBxZaayuBNf7j7ZEFlALFQKkx5nVjzOQurGfvZ0zQ2RLN12Ow1vZ0zUREpB8IdWAYBuRba6uCHDsAZBhjYtq4xm7gx8AtwBWAF7gQWGaMmdGVle31AgPDmqVgLROGJhMf43qe8o9WklNYHqLKiYhIXxbqwBAPBAsLAJUB57TIWnuLtfY71tqXrLWvWmvvB84DEoGftvZaY8ztxpiVHa10rzVpBsT5P678Q5Czl8iICKaPSms4RdMrRUSkM0IdGMqB2BaOxQWc0yHW2iXAYuBsY8yAVs571lrbf6ZeRkXD9IDNqPyrPmocg4iIHK9QB4YcXLdDsNCQheuuqO7ktfcAkUBqG+f1L7NObnwcZBzDur0axyAiIh0X6sCwwl+HeYGFxpg4YDZwPN0FE4BaoPA4rtH3zJgHxv/HumsLlBxh7JBkEuPcOIbC0ir2F5SFsIIiItIXhTowvARY4N5m5bfhxi68UF9gjBlqjJlsjIkPKEsxxkQ2v6gx5mLgdODf/hkX4SMxGcZPdY+thXXLiYwwzBipbgkREem8kAYGa+164GngS/6pkLcaYx7HDVZcRNNFmx4DNtO0NeJsYLsx5mfGmHuMMf9ljPkD8Dfc6o/39sT76HVmB06vdOMYZmq7axEROQ69YWnoe3HjDW4HLsb9on8KeKgdy0JvBVYBnwOGANFANvBL4FFr7YHuqXIvN+tkeOU37vHGVVBT3WTgY/04hnBd40pERDou5IHBWluH20Pi8TbOuxm4uVnZZtzaCxIocwQMyYLcA1BdBZvXMGbGXJIHRFNSUUNxeTV7D5cyenBSqGsqIiJ9RKjHMEh3abbqY4QxzGgyvTI/BJUSEZG+SoGhvwocx7BuGVh7zDLRIiIi7aXA0F+Nmwrxie5xUT7s29F0HMO+Qnxaj0FERNpJgaG/ioyEmQETStYsZdSgRAYmuK05jlbUsDu3JESVExGRvkaBoT9rNo7BGMPMwHEMe8NrTSsREek8BYb+bNpJEOmfCLNvBxQe1jgGERHpFAWG/iw+ASYG7PC9bnmTcQzr9xZQ59M4BhERaZsCQ38XOFtizVKGpyeQluj2+iqrqmWXxjGIiEg7KDD0d4G7V25Zg6mqbNItsUbrMYiISDsoMPR3GZmQNdo9rq2BTaubbnetcQwiItIOCgzhoNlsicCZEhv2FVHna2vLDhERCXcKDOGg2aqPw1JiyUiOA6C8upbtB4tDVDEREekrFBjCweiJkJzqHh8txuzZ1mS2hKZXiohIWxQYwkFExDGrPgaOY1i1SwMfRUSkdQoM4aLZOIY54wZh/E/X7y3gSFlVSKolIiJ9gwJDuJh6AkS7fSTI2Ut6ZRFTR7huCp+Fj7fmhrByIiLS2ykwhIvYOJgyu/H52mWcOWVow9PFmw72fJ1ERKTPUGAIJ7Oarvp4xpTMhqdr9xRQXF4dgkqJiEhfoMAQTgJXfdy+nkFRdUwdXt8tYfl466EQVUxERHo7BYZwMjAdRk9wj+vqYMNKzgxoZViibgkREWmBAkO4aTZb4oyAcQyf7i6gRN0SIiIShAJDuAkMDOtXMDghmilZAwF1S4iISMsUGMLNiLGQNsg9Li+FHRubtDIs2azAICIix1JgCDfGNB38uHZpk3EMn+7Op6RC3RIiItKUAkM4aja9ckjKACYNGwhAnc/yiRZxEhGRZhQYwtGkmRA7wD0+fBAO7m/SyvDhZs2WEBGRphQYwlF0DEw7sfF5s1UfV+/Kp7SyJgQVExGR3kqBIVwFdkt8+A6ZKbFMHJoCQK26JUREeg9rYdNqeOYHUFUZsmooMISrE06DAQnuce4BWLGYM6cGzpZQt4SISEjV1cHyhfD9O+GnD8CqD+Gjf4WsOgoM4So+Ac79QuPzv7/ImZMGNzxdvSufMnVLiIj0vKpKeO9N+M6X4dkfwr6djcf+81fX4hACUSH5qdI7nPsFeO8NqCiHQ/sZunMV4zOT2XGohJo6H59sy+XcmcNDXUsRkfBQcgTe/yt88A8oO9r0WEwsnH4efPaLbnp8CHQ4MHg8nlRgKLDT6/VWBZTfAnwBKAOe9Hq9y7uqktJNEpLgM5fCP/7snv/jReaf9w12HCoB3CJOCgwiIt0sNwf+9Rp8/G+oabYOTmIynPN5OPsSSEoJTf38OtPC8ChwPdDQfu3xeO4CngTqY88XPB7PHK/Xu+m4ayjd69wvwXt/hcpyyNnHZ2v28Dv/oVU7D1NWVUNCbHRIqygi0i/t2gLvvgqrPzq2m2HQUNeacPp5EBsXmvo105nAcDrwH6/XWxFQ9g3gAHAtkAk8D3wduPW4ayjdKzHJpde3/wJA2gevMT7rOnbkHaWmzseybXmcMyMrxJUUEeknfD5Yv8IFhW3rjz0+agJccAWceDpERvZ8/VrRmcCQBfyn/onH45kKjAC+5fV6P/SXXQHMb8/FjDERwD3AV4HRwGHgZeAha21ZRytnjHkZuALYaK2d3tHXh6XzvuQG0lRVQM5erpmYy/eJB9xsCQUGEZEO8vmgKB8OZUNutrs/lA05e+FIwbHnT58LF1zuFtYL0RiFtnQmMAwAAieCng5Y4L2Asp3A59p5vSeAu4E3gMeBKf7nJxhjzrXW+tpbMWPM54DLgIq2zpUAiclwziXwz5cBmLfl35iYz2ONYcWOw5RX1RIfq/GxIiLHqChrDAOBwSAvB6qrWn9tZCTMOxvOvwyGj+mZ+h6HzvwWOABMDnh+PlACrA0oS6Udv7SNMdOAu4DXrbWXBZTvBn4OXA282J5KGWMSAS/wNPD59rxGApx3Gbz/N6iqJObQPr4wLoc3arNct8T2XM6erlYGERHALan/2u9h+3ooLur46+PiYf6FbqZa/e7BfUBnAsMHwE0ej+dOXEvD54HXvF5vYEvAeGB/O651DW6g5JPNyn8N/BA3uLJdgQH4X9z7+S4KDB2XlOJG4b7zCgBXFS7jzaQvYo1hyeZDCgwiIr46eP/v8Prv2249AEhMgcwsyBwOQ4Y33g8eClF9bzB5ZwLDY7hm/5/hftmXAo/UH/R4PIOBBbhf+m2ZC/iAJlMwrbWVxpg1/uNtMsbMA+4ErrHWlphe2v/T69W3MlRXkVp0gFOidvNJ/FhW7MijorqWATHqlhCRMHVwPzz3BOxsNvkvKtoFgPpAUH8bMsINKu9HOvwbwOv17vZ4PNOAy/1Ff/N6vfsCThmF6xZoT8vAMCDfWhssqh0ATjPGxFhrq4McB8AYE4ULJ/+y1r7crjchwSUPhLM+5+YDA18uW8knA8ZQXetj2fY8zpo2LLT1ExHpaXV17t/Ev/4RagNWvx02Cm64C8ZNgYjeNZuhu3Tqv4xer/cQ8IsWjq0AVrTzUvFAS+06lQHntBgYgPuBCcAX2/kzGxhjbgduv+OOOzr60v7r/Mth4T+guoqR5bmcXLGHZfFjWLLpoAKDiISX/btcq8Le7Y1lkZFw0dVw0VVu598w0mVtzB6PJwM4EygH3vN6vXXteFk5AQtANRMXcE5QxpjxwEPAD6y1uzpQXQCstc8Cz3o8ntAszN0bpaTCgovg328AcH3xcpYNGM2KHXlUVtcSp24JEenvamvgrb+49WnqAn6VjRwPt3wdRowNXd1CqMObT3k8njs8Hs8yj8eTFlB2ErAZeBV4G/jY4/EktONyOUCGMSY2yLEsXHdFa60LjwOFwBvGmPH1N1wQivE/H9rK6yWYC65oSM4Tq/OYV7GHqlofy3ccDnHFRES62e6t8P274O8vNIaFqGj40i3wnZ+FbViAzu1WeRVgvV5vYUDZT3BTKX+PCwxzga+141or/HWYF1hojIkDZgMr23j9KNw4iI3A9oBbFq6bYjvtG3wpgVLSXCuD3/XFy8FaFm/Sltci0k9VV8Erv4FH74MDexrLx02Fh72uC6KXrbzY0zrTvjwBeKv+ib8rYgHwG6/X+1V/2TLcMtGPt3Gtl4AHgHuBJQHlt+HGLrxQX+BvKUgB9llr67spvgEMDHJdL24MxNcB/ZbrjAuugIVvQW0Nk6rzmFO5l+U7oqisqSMuOrz/0ohIP7N9gxurkHugsSwm1rUqnHNJ2AxqbEtnAkM6kBfw/HT//RsBZUuAm9u6kLV2vTHmaeBOY8zruNaJ+pUeF9F0psVjwE3A2cBC/+vfIwhjzP8BpdbaV9t+OxLUwHS3sMj7fwPg+iPLuTduFCt25HHmFPXyiEgfZy3s2QZL3nG3wM2fJs+Gm+5xG0BJg84EhkIgI+D5AtxaCh8HlFkaBy225V5gD3A7cDGQDzyF20ui3ctCSze48EpY/E+orWFKdS4nVe5jyaZhCgwi0jdZC9m7YcUiWLHYrdgYKC4errwNzryg1+7nEEqdCQybgUs8Hs93gDrcmIYVXq+3JOCc0cCh9lzMWluH67potfvCWnsz7Wi18J87uj3nSRtSM9xfnA/+DrixDN/eNoaqmjpi1S0hIn1Fzl4XEFYscvs8BDNjLtxwd59aqrmndSYw/Ax4E8gGanFjDb5Vf9Dj8UQCZ9C0xUH6qguvxC7+J6aulqlVh5h6dA8rdx7m9MmZoa6ZiEjLcg80tiQEDmIMNCAeTjgNTj4bpp6oVoU2dGalx795PJ6v4boQAF7wer1/CjjlXFx3xLtdUD8JtbRBmDPOh0VunOt1xcv5+8aTFRhEpPfJP9TYkrBvZ/BzYuNg1ikwdwFMPynsFl86HsZarVlUv3CT1+sNdVV6p4I87AO3YPxzkh/MuowHH/wyMVHqlhCREKqtgZ2bYeNq2Liq6YqMgWJiXZfD3AXuPra9Q+z6vQ41qWjZPmlb+mA4/Tw3ABK4Iv8TVu68hNMmqZVBRHqQtW4TqE2r3W3rOqiqDH5uVDRMnwNz57sWhbgBPVvXfqjTgcHj8ZwC3AqcgFsLoRhYBfze6/Vq/EI/Yy66Ct+Sd4mwPmZW5fDiko84bdJloa6WiPR3R4/A5jWuFWHTaijKb/ncyEiYcqILCSecBvHtWXBY2qtTgcHj8fwA+DbHNmfMBr7s8Xh+5PV6HzjOuklvkpHJ0RPPImXV+wDMWPtPqmu/oG4JEelaNdWwY5MLBxtXw74drZ8/aKgbsDj1BJgyG+ITe6Sa4ajDgcHj8VyBW51xL/B94H3caopDgXOAB4FveTyeNV6vV9tN9yPJl91A7aqFROFjRkU2Gxd9yLTPLAh1tUSkr/PVue6FpR/A6g+hosU9B2FAAkyeBdNOciFhsHbR7SmdaWG4C8gF5nq93sC2oT3A7zwez9+ADcB/AQoM/YgZPJSdY+cyadcyAIa9+UuYM8PtPSEi0hHWwv6dLiQsXwhHCoKfFxEBYyc3BoTRk8J+T4dQ6UxgmAU83ywsNPB6vfkej+cV4Mbjqpn0SnFfuIHKJ1YRZ2tJrSii8v8eIO47P3UrpImItCX/ECxbCEvfh4P7gp+TkQkz5sDUk2DSTI1F6CU6ExiigFbai8B/XDMw+qFRU8fz4qyruWrNC0RiiTu4B57+PtzzPTcqWUSkudIStz7Csvfd+IRgklLctMdTPgNjJmoRpV6oM7/UdwCf83g83/Z6vcfs9eDxeCKAi4AWVs2Qvu60Ky/l5/tzuK/ADYBk86fw+5/CV+53zYciIlWVsHaZCwkbVoJ/HZcmYuNg9qlw6mdgygnqaujlOhMY/gw8CvzV4/F83ev1NqyU4fF4xgE/AaYC3+maKkpvM3pwEnWnn89zi8q5uXipK1z2AQxMgytuC23lRKTnWQuFeW4RpV1b3P2+nVBXe+y5ERFuPMIp57iwoEWU+ozOBIafAhfgdpa80OPx5OBmSWQCWUAE8KH/POmnblwwkS+vn0dGXSmfK93gCt99zQ2APE/rM4j0a9VVsGe7PyD4b8VFrb9m7GQXEubMh+SBPVJN6Vqd2Uui2uPxfBb4BvBlYBww3H94J/A74P+8Xm9Nl9VSep3BKQO4ZN5onv5kAal15ZxescsdePnXLjScfHZoKygiXcNaN1CxvuVg52bI3hW8i6G5YSPduIR5Z8MQTX/s6457LwmPx5MIpADFXq+31F8WB8Q02/K619JeEp1TXF7Nzb/4gNqKSh7Le5PpVf695SOj4N7vuz5JEel7rHULJi337/ZYmNf2a+LiYcwkGDfFtSaMnQyJyd1fVzkePbuXhD8klDYrfga4oSuuL71XSnwMV5w6lj8s3MbDgz7Hzw6/zvCqAtdv+fT34Zs/hpHjQ11NEWmvnL3+kLDIbQ/dmqEjXSgYN8Xdho6ACA1a7M+68xe65sSEgS+ePIa/rdhLURn8d8YlPFv0JvHlR6CyHJ58EL79BAzSJlUivVZejmtFWL4QDuwJfs6AeH+rgT8cjJkECUk9WUvpBdQCIMdlQEwU1545nqff2cjhqCQeGPx5nsh9DVNRBiVF8OR34L8fh6SBoa6qiNQrynchYcUi2L01+Dlx8XDCqW4MwtQTIUq/LsKdvgFy3C48cSSvL9vNwaJyNtsU3j79y1y88Jdur/rcA/Dzh+EbP9L0KZFQKjkCqz50IWH7BjdOobnoGJh1sgsJM+ZCTGyPV1N6LwUGOW7RkRHcdNZEfvjGGgB+tTuC+Td8naTnfuz+Udq9FX75v/BfD+t/KSI9xVo4uB/WLoU1S93Ux2AhITLKrYswbwHMPkXLvEuL9K+3dIkF04bxyse72JlbQlWtj98XZ3D3tf8FL/zCnbB+BTz/M7jl61ryVaS71NW51oO1S90qi3k5wc8zEW7Hx3kL4ITTIVHjEaRtCgzSJSKM4ZZzJvHdP68A4J+r93PZHWeTVVwA//izO+njf0NqOnzx5tBVVKS/KS9zSy+vXeqCeXnzSWt+JgLGT3HdDSedCSmpPVtP6fPaFRg8Hk87VuiQcDdn3CBmjkpj3d5CfNbyh4VbeeBLN7ptaz/8lzvprb+4Nea/eLPGNIh0Vv4h14KwZilsW9fyIkqxcTB9Dsw6xY1JSErp2XpKv9LeFobOtCEf34pQ0ucYY/jyOZO59/cfA7Bo00GuOG0cE264xw24Wrfcnfjem+4fuhvuhmknhqy+In1GRRlsXQ9b1sCmT916CS1JzXBjEWad4raGjo7psWpK/3bcKz32B1rpsWt97+WVfLQ1F4ATx2bw2HUnu1aFZ/4XNqxoevJp58KVX1Ufqkigmmq3FPPmT2HzGjdw2HfM5sCNRk1wsxtmnwIjxmmckLRXz670KNLczWdP4pNtufgsrN6Vz6e78zlhTAbc8z03juGlZxv7WT9+D9avhGs9MOdM/UMn4cnng/07XTjY/Cls3+g2eGpJVDRMnu0Cwsx5kDaop2oqYUyBQbrcyEFJfHbWcN5dkw3A797fws+/fDrGGDj9PJg+F/78DKxc7F5w9Aj86lFYdgpcd6drUhXp7/JyXDjY9ClsXQelrWy9Y4xrOZgy290mTNcYIOlxCgzSLa6fP5H31+dQU+djW04xH245xJlThrqDKanwtQdgzdnwp1+4QZHgxjVsXQeXfQXmXwgREaF7AyJdrbwUtqyFjath02o4fLD18wcPcxu4TZntpkBqIycJMQUG6RaDUwZw6bzRvPqJ2/b6uQ+2ctqkIUQGhoDZp8LEmfDa72DRW66sohz+9JRb1/7GeyBz+LEXF+kL6urc2IONq1xAaGscQnJqYwvClNmQPqSHKirSPgoM0m2uOm0cb6/eR3lVLdkFZby7JpuLThzZ9KT4BLjhLjj5LPjDk4075G1bD4/cAZ+/Ds67XCtESt9w+KBrQdi4ys1oqChv+dzYAW4Ww1R/K8KwURrDI72a/hWWbpMcH8OVp43juQ/c5jZ/WryNz8zIIjY6yBa4E2fAI8/A31+Ad15x/xOrrYHXn3Ob5Nx0L4ye2KP1F2nTkQLYscnf1bCq9W4GY9xshmknuqWYx052gxdF+ggFBulWX5w3mr+t2ENhaRUFR6v464o9XHnauOAnR8fAl25xK9E99wTs3e7K9++C/70X5s6HCy6HkeN7rP4iDXx1cGAv7NwMOza6oJB/qPXXpGa4cDDtJNeKoHEI0ocpMEi3iouJ4tozJ/CLf24A4KWPdnDhCSNJGtDK/6xGjIUHnoT33oC//tFNL7M+N65h+UK31e6FV7hpZWrCle5SWQG7t7hgsGOT27yptS4GcLs7Tp7lvqPTTnJjcPQdlX5CgUG63YUnjOD1ZbvIKSyntLKWPyzcyp0XTm/9RZGRcP7lcMJp8KLXrZVfb5N/lPnI8XDBFXDSGe58keNReNgfDvytB9m7Wh+kCK5VbMwkGD/NjUUYN0UrK0q/FfKVHo0xEcA9wFeB0cBh4GXgIWttWRuvjQaeAuYCo4AkIAdYDvzQWvtpe+qglR6738KNOTz2euMfx/2XzuLcmR2YAbFnO7z7Kqxc4lobAmVkwnlfcms8aG66tEddnQsEgd0LhYfbfl1KqgsH46a4+5HjNA5B+rIONX/1hsDwM+Bu4A3gn8AU4C5gCXCutc1/OzR5bQKwCPgY2AUcBUYCtwCZwAXW2vfbqoMCQ/ez1vL9V1Y1LBkdExXBT28+jQlDO7gZzuGD8K/X4aN/HbsSXmIynPN5OPsSbbIjTZWXuS6F+oCwaytUVbT+GmPczIXxU/23aS6cqotB+o++ExiMMdOA9cAb1trLAsrvAn4OXGetfbET1x0K7AP+ba29qK3zFRh6RnlVLff87iP25btloQenDOCpr5zOwITYjl/s6BF4/+/w/t+g7GjTYzGxcMb58NkvwaDM46+49C3WQn6uCwb1AeHAHlfemphYN3OhPhyMnQzxiT1SZZEQ6VOB4QfAd4D51tolAeVxQAGwqD2/8INcNxI4Amyw1p7a1vkKDD0nu6CUu377EeVVtQDMGp3OY9fNa7qgU0dUVbrWhn+95n5JBDIRbnzDnDPcinkJ2uCqX6mrcy1Oh7Lh0H53O+h/3DxEBpOaAeMCWg+Gj9F6HxJu+tTmU3MBH27MQQNrbaUxZo3/eJv8ASEV935GAN8AEoG3u7KycvyGpyfyrS/M5pGXVmKBtXsK+M17W/jqeVM7d8HYONcNseBiWLXEreGwb6c7Zn1uv4qVi114GDcZps9xt5HjtfR0X1Fe6kLBQX8oOJTtbnk5UFfbvmuYCBcIArsX0gape0GkA0IdGIYB+dbaYNuyHQBOM8bEWGur27jOFFzXRr1i4DH/TXqZUyYO4YYFE3l+0TYAXl+2m/GZyXymI4Mgm4uMhHlnuTUcNn8K77zqZlLUs77G6XFvPu/GOEw7yYWHaSdC0sDjek9ynKoqXQDIy3GrfeYdgLyDkJsNxUUdv15cfED3wlT3OC6+6+stEkZCHRjigZb2cK0MOKetwLAb+CwQA4wHrgdSgFigxf+CGGNuB26/4447OlBl6QrXnDmeHYeK+dg/CPLJt9YzalAS4zs6CLI5Y9wc+KknugWfVn/kpmTu2da0D/toMSx9393qV+Crb30YM0nTNLtDi6HgABQXdu6aqRlurYPM4ZA5wt2GjoCB6WpBEulioR7DsB4YbK09ZpcVY8zLwBVAbDtaGJq/NhFYDey21p7f1vkawxAaZVU13PPbj9hf4GbPDkkZwFO3nkFKfDfMYz96xG0jvGGlux0tbvnc+ES3+M6wUe4X0ZAsdz8goevr1V9Y6z7TwsNQdNjd1z8uOOxWROxsKIiKbvwzGOoPBfUhQa0GIsejTw16fBc4F4hv3i1hjPkImGitHdTJa/8Q+BYw3lq7s7VzFRhCZ39+KXf/rnEQ5OzR6Tx6PIMg28Pnc+McNqyEDStg55Zj13YIJiUVhgwP+B/tcBgyAjKG9I8WCZ/PjQmoqXH7eNTW+B9XNz4vKw0IBPn+QJAHRfnueGdFRropi4OHuduQrMbHGUMgoh98viK9T58a9LgCOA+Yh1t3AWiYJTEbWHwc1x7gv08DWg0MEjojMtwgyIdfcis5rtlTwG/+s4WvfraTgyDbIyICRk9wt89d40bUb17jwsOGVW5DoWCKi9xt2/qm5ZFRMGioCxCDh0FKGiQPdNsVp6S6+8Sknv2l5/O5VpWCw42/1AsDHpeWQE19EKh19+0dQNhZrYWC9H4SukT6sVAHhpeAB4B7CQgMwG24sQsv1Bf411ZIAfZZa8v9ZYOAguaLOxljMnHdGaXAxm6sv3SB+kGQf6wfBLl0NxMyUzhnRlbPVCAhCeac6W7WQvZu2LvDjcTPbceI/Lraxml9LTERkJziwkPyQEhuFiriE91YivpR+8YAxuX/hscBx+rLqiv9/8P3N/0X1geD4/wff2cMSHAzD1IHQVpGwONBkD5YoUCkjwtpYLDWrjfGPA3caYx5HTcNcgpu5cdFQOCiTY8BNwFnAwv9ZdcB9xpj3sANfKwGJvrPSwVurQ8X0rtde+Z4dhws5pNt/kGQ/1jHyIzE4x8E2VHGuM2vRoxtWl5XBwW5jVP66oPEoez29c1bX2MLRW8WFd14i44+9nlsXGMIaH7TeAKRfi3ULQzgWhf2ALcDFwP5uP0hHmptWWi/Jbi1Gi7BLQUdA+QC7wE/s9Z+3D1Vlq4WYQz3f2EWd//2I7ILyqiq9fG9V1Z13yDIjoqMbGw+nzmv6bGKMjfSP/eAWzyqpAhKjrj74iJ3X17a83VOSAr4hT7Y3dL9/+tPSXWbJEVFNQ0FWpdARFoQ8r0kegMNeuw99uWXcs9vP6K8ugcHQfaEmmo3i6CkKCBIHGl8XFnun/ZpwdL0MdY9r/+72nDMQlSMv/l/cLNwMAjiBrRQGRERoI8NehRpYmRGIt/8wmweeblxEORv/7OF27tzEGRPiI5p/IUuItIH9fH/tkl/dOqkIdwwf0LD89eW7ub99QdCWCMREVFgkF7p2vkTOGVi43peT/5jHdtyjoSuQiIiYU6BQXqlCGP45qWzGJ7uVlesqvXxrT8u49Pd+SGumYhIeFJgkF4rIS6ah6+cQ2KcG2pTXl3Ld19czgcb1D0hItLTFBikVxuZkcj/3XgqGUlxANT6LD98Yw2vfrIrxDUTEQkvCgzS640ZkswTt5zGyIzEhrJfv7eZX/17Ez5NCxYR6REKDNInDE4ZwOM3n8q0EakNZa8v3c2P3lhDdW1dCGsmIhIeFBikz0geEMNj153M6ZMaZ08s3JjDg39eQVlVD++bICISZhQYpE+JjY7kO5efxCVzRjWUrdlTwP1/WErB0coQ1kxEpH9TYJA+JzLC8F8XTOOmsyY2lO3MLeG+5z5mf34I9mwQEQkDCgzSJxljuPbMCXz9kplE+DdMyj1Swdef+5jN2b18R0gRkT5IgUH6tPNnj+B/rppDbHQkACUVNXzrj0tZ6t8mW0REuoYCg/R58yYM5sc3nNKwDXZVrY//eXkl//x0X4hrJiLSfygwSL8wOWsgT9x8GpkD3ZbOPgtP/mM9f1q8HW3hLiJy/BQYpN/ISk/gyVtOZ3xmckPZHxdt49HXP6W0UtMuRUSOhwKD9CupibH85MZTOXFsRkPZ4k0H8Ty7hI37C0NYMxGRvk2BQfqd+Ngovnf1XC46cWRDWW5xBd/4w1JeXLKdOp+6KEREOkqBQfql6MgI7rl4Bt+97MSG3S591vKHhdv47z8t5XBJRYhrKCLStygwSL925tShPHP7/CZ7UKzbW8gdzy7h4y2HQlgzEZG+RYFB+r3BKQP4yY2ncP38CUS4NZ44WlHD/7yyil/8cwNVNdq8SkSkLQoMEhYiIyK4YcFEfnzjqQxKjmso//vKvdz924/Yk3c0hLUTEen9FBgkrMwYmYb39jM5fXJmQ9mew0e567cf8veVe7Vmg4hICxQYJOwkD4jhwctP5O6LphMT5f4KVNf6+MU/N/C9V1ZRUl4d4hqKiPQ+CgwSlowxXHzSKJ76yhmMGZzUUP7x1lzueHYJ6/YWhLB2IiK9jwKDhLXRg5P42ZdP55I5oxrK8o9W8s3nl/KLf26gpEKtDSIioMAgQmx0JHdeOJ2HrzyJpAHRAFjcgMivPL2Qt1fv02JPIhL2FBhE/E6blMkvb5/PSQHLSpdU1PCzt9Zzz+8+YlN2UQhrJyISWgoMIgEykuP432vn8eDlJzIkZUBD+faDxdz3+4/5yV/XUFhaGcIaioiEhgKDSDPGGM6YMpRn71jA9fMnNMykAHhv3QG+8vQiXv1kF7V1vhDWUkSkZykwiLQgLjqSGxZM5Nd3LOD0SUMaysura/n1e5v52q8Ws2rX4RDWUESk5ygwiLQhc2A8D105h0evm8eI9ISG8v0FZTzwwnK+9/JKDh0pD2ENRUS6nwKDSDudNHYQz3x1PredO4X4mKiG8o+25nLbM4v446Jt2pdCRPotBQaRDoiOjODyU8fyG88Czp2Z1VBeXevjT4u3c9szi3jn033UaHyDiPQzCgwinZCeFMf9l87mpzefyvjM5Iby3OIKnvjHem75xQf8dflutTiISL8R8sBgjIkwxtxnjNlijKk0xuw3xjxujElox2tTjTH3GGP+5X9dhTFmqzHmWWPMiJ6ov4S3aSPS+PlXzuCei2eQ7F/0CeBwSSXedzdx41Pv89JHOymrqglhLUVEjp8J9e58xpifAXcDbwD/BKYAdwFLgHOttS227RpjLgD+AfwHeB/IB6YDXwWqgdOstZvaqoPH47EAXq/3uN6LhLeyqhr+sXIfry/bxZGypktKJ8ZFcencMXxh3miS42NCVEMRkSZMh04OZWAwxkwD1gNvWGsvCyi/C/g5cJ219sVWXj8aiLTW7mxWfi7wb+A1a+3lbdVDgUG6UmVNHe9+uo+XP9lFfknTRZ7ioiP53JxRXHbKGNIS40JUQxERoI8Fhh8A3wHmW2uXBJTHAQXAImvtRZ28dgFw2Fo7ua1zFRikO9TU+fjPumxe+ngnOYVNp11GR0ZwwQkjuOLUsQwZGB+iGopImOtQYIhq+5RuNRfwAcsDC621lcaYNf7jHWaMSQGSgA3HW0GRznKhYCSfnTWcxZsO8pcPd7Ln8FHAhYm/r9zL26v3cc6MLK4+fRzD0xNDXGMRkZaFetDjMCDfWlsV5NgBIMMY05kO3+8C0cAfWjvJGHO7MWZlJ64v0m6RERGcPT2LZ756Jg9feRITh6U0HKvzWf69NptbvYv43ssr2bi/kFCPKxIRCSbUXRI7gWhr7cggx54HbgBSrbVHOnDNy4GXgX8BF9p2vEF1SUhPstayenc+f/lwB+v2Fh5zfHLWQL508hjOmJJJZESoM72I9GN9qkuiHBjcwrG4gHPaxRhzEfACsAq4sj1hQaSnGWM4aewgTho7iA37CvnLRztYsaNxT4otB47w6OufMmTgAL4wbwwXzB5BfGyo/6qKSLgL9b9COcBUY0xskG6JLFx3RXWQ1x3DP8XydWAjcJ61tqRrqyrS9aaPTOMHI+exJ+8obyzbzX/WH2hYJTL3SAW/+tcm/rhoGxedOJJL545mcMCW2yIiPSnU7Z0r/HWYF1jonyUxG2jX+AJjzPm4dRy24NZuKOraaop0r9GDk7jvkpn88e5zuPbM8U0WgSqvquXVT3Zx8y8+4IdvfMr2g8UhrKmIhKtQB4aXAAvc26z8NiAe170AgDFmqDFmsjGmyRw0Y8x5wJvANuAz1tpjO4VF+ojUxFhuOmsSf7znM9x10XSGpzUueFrns3ywIYc7f/Mh9z//CUu35eJTr5uI9JDesNLjU8CduBaCt3ErPd4NfAScU7/SozHmOeAm4Gxr7UJ/2RzcipAG+G/cSo9NWGv/1FYdNOhReiuftSzfnsdrS3cFHSA5PD2BC08YybkzsxiYEBuCGopIH9anBj2Ca13YA9wOXIz7pf8U8FBry0L7TadxcOQTLZzTZmAQ6a0ijOGUiUM4ZeIQtuUc4bWlu1m86WBDy0J2QRm/fm8zv3t/C6dMGMx5s0cwd/wgza4QkS4X8haG3kAtDNKX5BVX8Oby3fzz0/2UV9UeczwtMZbPzhzOebOHazEoEWlN31kaurdQYJC+qLyqliWbD/Lumv1s3B98nO/0kWmcP3s4Z04ZyoCY3tCgKCK9iAJDRykwSF+3L7+Uf63Zz3vrDlBUduzCqQNiIlkwbRjnzx7BlKyBGNOhfydEpH9SYOgoBQbpL2rrfKzYcZh31+xn2fa8oLMoRmYkct6s4Zw1fRiDkrWug0gYU2DoKAUG6Y8KSyv5z7oDvLNmP9kFZUHPmTYilbOmDePMKUNJTdQsC5Ewo8DQUQoM0p9Za9mUXcS/1mSzaFMOFdV1x5wTYWDm6HTOmjaM0ydnkjygM3u+iUgfo8DQUQoMEi4qqmtZvOkgCzfmsGZ3Pr4gf/0jIwwnjc1gwbRhnDppCAmx0ceeJCL9QZ9bh0FEesiAmCjOnz2C82eP4EhZFUs2H2TRxoNs2FdIfXao81mW7zjM8h2HiY6MYN74QSyYNoyTJwwmTjMtRMKW/vaLhKmBCbFcMmc0l8wZzeGSCpZsOsiiTQfZcuBIwzk1dT4+2prLR1tziY2O5JQJgzll4hDmjB+kbguRMKMuCdQlIRLoUFE5izblsGjjQXbmBt/0NcLA1BFpnDxhMPPGD2bUoERN1RTpezSGoaMUGESC25dfyuKNOSzcmMP+FmZaAAwZOICTJwzm5AlDmDkqjZioyB6spYh0kgJDRykwiLTOWsvuvKMs3ZbLsu15bD1whJb+5YiNjuTEMRmcPNG1PqQnxbVwpoiEmAY9ikjXMsYwdkgyY4ckc+2ZEygqrWLFzjyWb89j1c58yqsb97Soqqnjk225fLItF4DxmcmcPGEIJ08czIShKUSo60KkT1ILA2phEDkeNXU+NuwrZNn2PJZtzyWnsLzFc9MSY5k3fjAnTxzMiWMyNOtCJLTUJdFRCgwiXSe7oNQfHvLYsK+QumCLPQDRkRHMHpPeMPZhcIqWqRbpYQoMHaXAINI9yiprWLnzMMu257FiRx4lFTUtnjtmcBIn+6dtThw2kMgIdV2IdDONYRCR3iEhLpoF04axYNow6nyWLQeKWLbNtT7sOXy0ybm7846yO+8of/loJynxMcwbP5h5EwYze3Q6yfFa80Ek1NTCgFoYRELhUFE5y3a48LBuTwE1db6g5xlgXGYys0anM3t0BtNHphEfq//riHQBdUl0lAKDSGhVVNeyelc+y7bnsnz7YYrKqlo8NzLCMGnYQGaPTmfWmHSmDk/Vug8inaMuCRHpWwbERHH65ExOn5yJz1q25RSzbHsun+7KZ2tOMb6A/9jU+dzum5uyi3jxwx1ER0YwbUQqs8dkMHt0OhOHpRAZERHCdyPSPykwiEivEmEMk7MGMjlrIDedNYmyqho27CtkzZ4C1uwuYFez5apr6nzu2J4CAOJjopg+MpXpI9OYNiKNicNS1AIh0gUUGESkV0uIjXYLP00YAkBxeTXr9hTw6Z581u4uILuw6ZLV5dW1Dbttgpu+OXFYCtNGpDFtRCpTR6Rq4yyRTlBgEJE+JSU+hjOnDuXMqUMBOFxSwVp/68One/LJL6lscn5NnY+N+4vYuL+ooWzUoMSGADF9RBpDBg7Q5lkibVBgEJE+bVDyAM6dOZxzZw7HWktOYTkb9heyYV8hm/YXHdMCAbD3cCl7D5fy9up9AKQnxTJtRBrTR6QydUQaY4ckaRyESDMKDCLSbxhjyEpPICs9gfNnjwCgqLSKTdlFbNhfyMZ9Rew4VHzM6pMFR6tYvOkgizcdBGBATCSTs1KZNiKVaSPSmJw1UFM5Jezpb4CI9GupibENMzAAKqtr2ZJzhI37iti4v5DN2UeabJ4FUFFdx6e78/l0dz4AEQbGDklm2og0pvq7MTKStQunhBcFBhEJK3ExUcwencHs0RmAm6a5J6+EDfsKG8Y65B9tOg7CZ2HHoRJ2HCrhryv2ADAkZQBT/S0QU4enMnpworoxpF9TYBCRsBYZYRiXmcK4zBQunTcGay15xRX+8OBCxJ68ozRf4i63uILc4go+2JADQGxUBOMyU5g4LIWJQ1OYOGwgWekJ2s5b+g0FBhGRAMYYhgyMZ8jAeM6ZkQVAaWUNm7OL2LTfjYXYeuAIVbVNl7KuqvU1LChVLz42iglDGwPExGEpDEnRjAzpmxQYRETakBgXzdzxg5k7fjAAtXU+dhwqYdP+QjbsL2JrzpFjpnMClFfVsnZPAWv9i0qBmxZaHyImDEthfGYKg5LjFCKk11NgEBHpoKjIiIbVKL90iisrLK1kW04x23KK2X7wCFtziikurz7mtcXl1azceZiVOw83lCXGRTMuM5mxQ5IZN8TdjxyUSHSkxkRI76HAICLSBdIS4zhlYhynTHQrUtaPhdh20IWIbQePsD2nmLKq2mNeW1pZc0xLRFSEYdSgJMZmuhBRHygS46J77D2JBFJgEBHpBoFjIc6c4lal9FnLwcJytuYcYdvBYnYeKmZXbgmllceGiFqfZWduCTtzS/h3QPmQlAGMy0xmfGYKE4a6W2pibA+9KwlnCgwiIj0kImBhqfoBlfUtETtzS9h1qKQhJOQeqQh6jfrZGR9vzW0oS0+KZYI/QIz3h4j0JK0TIV1LgUFEJIQCWyJOm5TZUF5aWcOu3BJ25Zaw85C735N3lFpf8wmebqXKgqN5LN2e11CWlhjrwkNmCuOHJjNhaAoZSRpcKZ0X8sBgjIkA7gG+CowGDgMvAw9Za49dBP7Y118JXAicCEzFvacx1to93VRlEZFulxgXzcxR6cwcld5QVlPnY9/hUnbmFrP9oLvtOlRyzBRPgMLSKpZvz2N5QIhIiY9hzJAkxgxOZszgJEYPTmLUoCTiorX9t7Qt5IEBeAK4G3gDeByY4n9+gjHmXGvtsX8TmvIAJwNrgZ3ApG6sq4hIyERHRjAu0w2APG+W2yujzmfZn1/K9oPF7DjkQsTOQyVU1tQd8/ri8mrW7HY7e9YzwLC0BEYPTmJMwy2ZzNR4IiPUGiGNQhoYjDHTgLuA1621lwWU7wZ+DlwNvNjGZW4Ecqy1tcaYX6DAICJhJDLCMNrfWvDZWcMBFyIOFLgQsf1QCTv8YaKi+tgQYYEDhWUcKCzjoy2HGspjoyIYNSiJMUOSGD0oiREZiYzMSGRQygCtXhmmQt3CcA0u4D7ZrPzXwA+B62kjMFhr93VLzURE+qjICMPIQUmMHJTEZ2a6Mp+1HCoqZ0/eUXblHWVPXgm7846SU1hGkGERVNX63JTQg8VNymOjIxmRntAQIEakJzIiI5Gs9AStG9HPhTowzAV8wPLAQmttpTFmjf+4iIgcpwhjGJaWwLC0BE6b3Di4sqqmjn35pez2B4g9/lthaVXQ61TV1DVsxNX8+kNT4xuCxMiMREZkJDA8PVFrR/QToQ4Mw4B8a22wb+YB4DRjTIy19tjl0rqAMeZ24PY77rijOy4vItLrxUZHNqznEOhIWRV78o6yO+8o+/JL2Zdfyv780qCrV4Jrwajv2li6LbfJsZT4GIanJ/hviQxPc48zU+OJidKAy74i1IEhHggeY6Ey4JxuCQzW2meBZz0eT5AGORGR8DUwIZbZY2KZPSajSXlxeTX7AwLE/gL3uKV1I+pfU1xezcb9RU3KIwwMGRjfECSy0hIY4X+cnhSrKaC9TKgDQzkwuIVjcQHniIhIL5ASH0PKyDSmj0xrUl5ZU0d2QIDYn19KdoFrcagOMu0TwGfhYFE5B4vKWbHjcJNj8TFRDM9IaOze8I+VGJYWT2SExkqEQqgDQw4w1RgTG6RbIgvXXdEtrQsiItJ14qIjGe9faTKQz1oOF1eQXVBGdmEZ2QWlHCgoI7ugjLziClpq3i2vrm3YzCtQVIQbi9F0rEQiI9ITiIsJ9a+0/i3Un+4K4DxgHrCkvtAYEwfMBhaHploiItIVIgJWsjxp3KAmx6pq6sgprA8SZf4g4Vopgu2vAW6PjfoxFR81OzYoOY4s/8DOYWnxZKUlkJWWwFCNlegSoQ4MLwEPAPcSEBiA23BjF16oLzDGDAVSgH3WWnVTiIj0cbHRkYwZksyYIclNyq21FJVVsT+/rKF7Y5+/uyO/pLKFq8HhkkoOl1SyJmDXT3Bz9welDGgIEcNSE/xhIl4DLzsgpIHBWrveGPM0cKcx5nXgbRpXelxE0zUYHgNuAs4GFtYXGmPmA/P9T+f47+80xhzx/4wfdONbEBGRLmaMIS0xjrTEOGaNTm9yrLyqlv0FpU0GXu7LLyWnsByfDd7BYYG84gryiiuarHIJLkwMThnA0LR4hqUmNAkVQ1PjidWy2Q1C3cIArnVhD3A7cDGQDzyF20uirWWhAc4BHm5W9v8CHiswiIj0E/GxUUwaNpBJwwY2Ka+p85F7pNw/tbOcHP8Uz5xCN1Yi2OJU4MJE/Q6gzcMEQEZyHMNS4103hz9QuBaK+LAbM2FsC4ksnNRPq/R6vaGuioiIdLHq2joOHakgxx8gAkNFawMv25ISH0NaYiypibGkJsQGf5wYS1JcdG+dItqhSoVXPBIRkbATExXZMKOiueZhIqeovOE+90h5iy0T0Li+xO68o63+/KgI0xAe0hJiyUiOIytgAashAwf0iamiCgwiIhK2WgsTNXU+8o5UkFPULEwUlnPwSDl1raWJALU+2zAgM5joyAiGproFrLL8U0az/GEiJT6m17ROKDCIiIgEER0ZQVZ6AlnpCcccq/P5OFJWTWFpFUWlVRSVVTU8LvQ/L/I/L68OPkW0Xk2dr2GqaHOJcdENy2pnpSUwdXjqMatv9hQFBhERkQ6KjIggPSmO9KS4Ns+trKnjSGkVhWVVFB6t5NCRCg74F7HKLihrcaMvgNLKGrYcOMKWA0cAOGvaMAUGERGR/iguOpLMVLfmQzBlVTXkFJazP7/UHyQaw0RlTV2Tc0cEae3oKQoMIiIiIZQQGx10x1BrLQVHq8guLG1YCXNms3UpepICg4iISC9kjCEjOY6M5Dhmjw5NN0Sg3j+PQ0REREJOgUFERETapMAgIiIibVJgEBERkTYpMIiIiEibFBhERESkTQoMIiIi0iYFBhEREWmTAoOIiIi0SYFBRERE2qTAICIiIm3SXhIBPB5PqKsgIiLSU6zX6zXtPVktDCIiItImY60NdR36JWPMSmvtnFDXo7fR5xKcPpfg9LkEp88lOH0uwXXV56IWBhEREWmTAoOIiIi0SYGh+zwb6gr0UvpcgtPnEpw+l+D0uQSnzyW4LvlcNIZBRERE2qQWBhEREWmTAoOIiIi0SYGhCxljIowx9xljthhjKo0x+40xjxtjEkJdt1AyxtgWbqWhrltPMMZ82xjzijFml/9972nj/EnGmDeNMUXGmDJjzBJjzDk9VN0e05HPxRjzSCvfo2/0YLW7lTFmojHme8aYpcaYw8aYo8aYNcaY7wT7dySMvivt/lzC5bsCDX/+LxhjNhtjio0x5f7fPz81xgxt4fxOf1+00mPXegK4G3gDeByY4n9+gjHmXGutL5SVC7ElHDvwpiYUFQmBR4FCYDUwsLUTjTHjgI+BWuDHQDFwG/CuMeZCa+173VvVHtXuzyXAfUB+s7JVXVinUPsy8F/A34AXcH9HzgZ+AFxpjDnFWlsBYfddaffnEqC/f1cAhgNDcb9zsnHfhRnA7cDVxpjZ1to86KLvi7VWty64AdMAH/Bas/K7AAtcG+o6hvCzscBzoa5HCN//2IDHG4A9rZz7MlAHzA4oSwT2AlvxD1TuD7cOfi6P+L9Ho0Nd727+TOYAKUHKf+B//3eG6XelI59LWHxX2vi8rvB/Bt/syu+LuiS6zjWAAZ5sVv5roBy4vqcr1NsYY2KMMYmhrkdPs9buas95/qbVzwMLrbVrAl5fCvwGmAjM7Y46hkJ7P5fmjDHJxph+2TpqrV1prS0Ocugl//10CMvvSrs+l+b683elDXv996nQdd8XBYauMxfXwrA8sNBaWwmsoR/95e2ky3HB6agxJs8Y85QxJiXUleplZgKxwCdBji3134f792gdrim10hjzsTHmwlBXqIcM99/n+u/1XXGafy6Bwua7YoyJM8ZkGGOGG2POA37lP/S2/75Lvi/hmLy6yzAg31pbFeTYAeA0Y0yMtba6h+vVGywHXgF2AMnARcCdwAJjzGn+lCvuOwTu+9JcfVlWD9WltzmCGwPzMVAETALuBd4yxnzZWvtcyGrWzYwxkcBDuL7nF/3FYf9daeFzgfD8rtwKPBXwfA9wvbV2if95l3xfFBi6TjwQLCwAVAacE3aBwVp7crOi540x64D/Be7x34v7fkDw71Fls3PCirX2yeZlxpjf4cY+PGGMebUfB88ngVOAB6y1W/1l+q4E/1zC9bvyJrAFNybhBFz3w6CA413yfVGXRNcpxzX5BBMXcI44P8GFp4tDXZFepP77Eex7pO9QM9baAuCXuBkWp4W2Nt3DGPN9XGvcs9baxwIOhfV3pZXPJaj+/l2x1mZba9+z1r5prX0YuAn4kTHm2/5TuuT7osDQdXKADGNMsD+QLFx3Rdi1LrTEWluD/zMLdV16kRz/fbCmwfqyYE2K4WyP/77ffY+MMY8A3wV+D3yt2eGw/a608bm0Zo//vt99V5qz1q4DPgU8/qIu+b4oMHSdFbjPc15goTEmDpgNrAxBnXot/+cynOCDlcLVelyT4alBjp3iv9f3qKkJ/vt+9T0yxjwMPAw8D9xq/XPgAoTld6Udn0tr+uV3pRUDgDT/4y75vigwdJ2XcPNe721Wfhuub+iFnq5Qb2CMSW/h0PdxY2j+3oPV6dX8/ap/B84yxsyqL/dPRb0V2E6zWTjhwBgTFWxGjTFmBHAHUIAb4NYvGGMewq0l8EfgFhtkwbdw/K6053MJw+9KZgvlZ+Ommi6Frvu+aLfKLmSMeQrXr/YGbjpL/UqPHwHnBPuC93fGmCdwCfYDYB9uUM5FuFXalgFn22NXaOtXjDE3AKP8T+8CYnArgQLstdb+MeDc8bi/uDW4lUNLcKFzBnCxtfbdnqp3d2vv52KMGQjsxg3s2kzjyPdbcd+na6y1r/RYxbuRMea/gF/g/q48iJuqHSjXWvtv/7nh9F1p1+cSTt8VAGPMG7iVHt/Hrb0QB5wEXI0bk3BW/boLXfJ9CfWKVP3pBkQC/w+3alYVrk/op0BiqOsWws/kUuBd/2dRCZTh1qV4AIgLdf166DNYiGt9CnZbGOT8KcBfcdPDyoEPgXND/T5C9bngBmr9BtesWuT/B+8g8CowL9Tvo4s/k+da+UyO+b6E0XelXZ9LOH1X/O/3SuAtYL//39cK3GyJp4CRQc4/ru+LWhhERESkTRrDICIiIm1SYBAREZE2KTCIiIhImxQYREREpE0KDCIiItImBQYRERFpkwKDiIiItEnbW4tIv+HxeB7B7TVwttfrXRja2oj0LwoMItLA4/G0ZyU3/TIWCUMKDCISzP+0cmxPT1VCRHoPBQYROYbX630k1HUQkd5FgUFEOi1wzABu58l7gcnAUeAfwANer/dQkNdNwO06+BlgEJAPvAd83+v1bg9yfiRuZ70bcNv2xuA2NFsI/KiF11wOfNN/fiXwL+D/eb3eA8fxlkXClmZJiEhXuA/4JbAWeBK3Y+stwMcej2dQ4Ikej2cusBK4HlgB/B+wFLgOWOnxeOY0Oz8GeAd4BhgBvAj8HFgFfBE4PUh9PMCfcN0nTwMbgKuA9zweT+zxvlmRcKQWBhE5hr/lIJhKr9f7wyDlFwIne73eTwOu8QSuxeGHwFf8ZQZ4HkgGrvd6vS8EnH8V8BfgTx6PZ6rX6/X5Dz0CnAv8HbjC6/VWBbwm1n+t5i4A5nq93vUB574IXIPbcv3llt67iASnFgYRCebhFm7/3cL5fwwMC36PAMXAtQH/qz8N12XxSWBYAPB6vS8BHwKTgDOgoSvCA1QAXwsMC/7XVHm93sNB6vPzwLDg92v//bwW3oOItEItDCJyDK/Xazr4kkVBrlHs8XjWAAuAKcAa4ET/4fdbuM77uLBwArAYFy5SgGVerzenA/VZGaRsv/8+tQPXERE/tTCISFfIbaG8fsBjSrP7gy2cX18+sNl9RwcqHglSVuu/j+zgtUQEBQYR6RpDWijP9N8XN7vPDHIuwNBm5x3x32d1umYi0iUUGESkKyxoXuDxeFKA2bgpjZv9xfXjHM5q4Tr15av991twoWGmx+MZdvzVFJHOUmAQka5wg8fjOaFZ2SO4Log/BwxW/Ag35fIM/zoJDfzP5wPbcIMf8Xq9dYAXGAD8svmUSI/HE9N82qaIdA8NehSRY7QyrRLgTa/Xu6ZZ2T+Bjzwez8u4cQhn+G97CJhZ4fV6rcfjuQn4N/CSx+P5K64VYRLwBdyCTzcGTKkEt0z1ycAlwDaPx/MP/3kjgPOA+4HnOvE2RaQDFBhEJJiHWzm2BzfjIdATwBu4dReuAkpxv8Qf8Hq9eYEner3eZf7Fm76LW1/hEtxKj3/GrfS4tdn51R6P5wLga8CNwE2AAXL8P/PDjr45Eek4Y217NqcTETmWtpMWCR8awyAiIiJtUmAQERGRNikwiIiISJs0hkFERETapBYGERERaZMCg4iIiLRJgUFERETapMAgIiIibVJgEBERkTYpMIiIiEib/j8MBo/z75qqCQAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history, save_as='02-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.2 - Reload and evaluate best model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:28.335516Z",
- "iopub.status.busy": "2021-02-10T09:47:28.335029Z",
- "iopub.status.idle": "2021-02-10T09:47:32.071838Z",
- "shell.execute_reply": "2021-02-10T09:47:32.072333Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 0.2868\n",
- "x_test / accuracy : 0.8837\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "#### Accuracy donut is :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/IMDB1-03-donut</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
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\n",
- "text/plain": [
- "<Figure size 576x576 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "model = keras.models.load_model('./run/models/best_model.h5')\n",
- "\n",
- "# ---- Evaluate\n",
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
- "print('x_test / accuracy : {:5.4f}'.format(score[1]))\n",
- "\n",
- "values=[score[1], 1-score[1]]\n",
- "pwk.plot_donut(values,[\"Accuracy\",\"Errors\"], title=\"#### Accuracy donut is :\", save_as='03-donut')\n",
- "\n",
- "# ---- Confusion matrix\n",
- "\n",
- "y_sigmoid = model.predict(x_test)\n",
- "\n",
- "y_pred = y_sigmoid.copy()\n",
- "y_pred[ y_sigmoid< 0.5 ] = 0\n",
- "y_pred[ y_sigmoid>=0.5 ] = 1 \n",
- "\n",
- "pwk.display_confusion_matrix(y_test,y_pred,labels=range(2))\n",
- "pwk.plot_confusion_matrix(y_test,y_pred,range(2), figsize=(8, 8),normalize=False, save_as='04-confusion-matrix')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:32.075921Z",
- "iopub.status.busy": "2021-02-10T09:47:32.075457Z",
- "iopub.status.idle": "2021-02-10T09:47:32.078267Z",
- "shell.execute_reply": "2021-02-10T09:47:32.077774Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Wednesday 10 February 2021, 10:47:32\n",
- "Duration is : 00:00:45 946ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/IMDB/02-Keras-embedding.ipynb b/IMDB/02-Keras-embedding.ipynb
new file mode 100644
index 0000000..3a0ffb8
--- /dev/null
+++ b/IMDB/02-Keras-embedding.ipynb
@@ -0,0 +1,435 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [IMDB2] - Sentiment analysis with text embedding\n",
+ "<!-- DESC --> A very classical example of word embedding with a dataset from Internet Movie Database (IMDB)\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \n",
+ " - Understand the management of **textual data** and **sentiment analysis**\n",
+ "\n",
+ "Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)** \n",
+ "Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
+ "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
+ "\n",
+ "## What we're going to do :\n",
+ "\n",
+ " - Retrieve data\n",
+ " - Preparing the data\n",
+ " - Build a model\n",
+ " - Train the model\n",
+ " - Evaluate the result\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 1 - Import and init\n",
+ "### 1.1 - Python stuff"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "import tensorflow.keras as keras\n",
+ "import tensorflow.keras.datasets.imdb as imdb\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib\n",
+ "\n",
+ "import os,sys,h5py,json\n",
+ "from importlib import reload\n",
+ "\n",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as pwk\n",
+ "\n",
+ "run_dir = './run/IMDB2'\n",
+ "datasets_dir = pwk.init('IMDB2', run_dir)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1.2 - Parameters\n",
+ "The words in the vocabulary are classified from the most frequent to the rarest. \n",
+ "`vocab_size` is the number of words we will remember in our vocabulary (the other words will be considered as unknown). \n",
+ "`hide_most_frequently` is the number of ignored words, among the most common ones \n",
+ "`review_len` is the review length \n",
+ "`dense_vector_size` is the size of the generated dense vectors \n",
+ "`output_dir` is where we will go to save our dictionaries. (./data is a good choice)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vocab_size = 10000\n",
+ "hide_most_frequently = 0\n",
+ "\n",
+ "review_len = 256\n",
+ "dense_vector_size = 16\n",
+ "\n",
+ "epochs = 30\n",
+ "batch_size = 512\n",
+ "\n",
+ "output_dir = './data'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Override parameters (batch mode) - Just forget this cell"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pwk.override('vocab_size', 'hide_most_frequently', 'review_len', 'dense_vector_size')\n",
+ "pwk.override('batch_size', 'epochs', 'output_dir')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Retrieve data\n",
+ "\n",
+ "IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) \n",
+ "Note : Due to their nature, textual data can be somewhat complex.\n",
+ "\n",
+ "For more details about the management of this dataset, see notebook [IMDB1](01-One-hot-encoding.ipynb)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2 - Get dataset"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words=vocab_size, skip_top=hide_most_frequently, seed= 42,)\n",
+ "\n",
+ "y_train = np.asarray(y_train).astype('float32')\n",
+ "y_test = np.asarray(y_test ).astype('float32')\n",
+ "\n",
+ "# ---- About\n",
+ "#\n",
+ "print(\"Max(x_train,x_test) : \", pwk.rmax([x_train,x_test]) )\n",
+ "print(\"Min(x_train,x_test) : \", pwk.rmin([x_train,x_test]) )\n",
+ "print(\"x_train : {} y_train : {}\".format(x_train.shape, y_train.shape))\n",
+ "print(\"x_test : {} y_test : {}\".format(x_test.shape, y_test.shape))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2 - Load dictionary\n",
+ "Not essential, but nice if you want to take a closer look at our reviews ;-)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ---- Retrieve dictionary {word:index}, and encode it in ascii\n",
+ "# Shift the dictionary from +3\n",
+ "# Add <pad>, <start> and <unknown> tags\n",
+ "# Create a reverse dictionary : {index:word}\n",
+ "#\n",
+ "word_index = imdb.get_word_index()\n",
+ "word_index = {w:(i+3) for w,i in word_index.items()}\n",
+ "word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2, '<undef>':3,} )\n",
+ "index_word = {index:word for word,index in word_index.items()} \n",
+ "\n",
+ "# ---- A nice function to transpose :\n",
+ "#\n",
+ "def dataset2text(review):\n",
+ " return ' '.join([index_word.get(i, '?') for i in review])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Preprocess the data (padding)\n",
+ "In order to be processed by an NN, all entries must have the **same length.** \n",
+ "We chose a review length of **review_len** \n",
+ "We will therefore complete them with a padding (of \\<pad\\>\\) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_train = keras.preprocessing.sequence.pad_sequences(x_train,\n",
+ " value = 0,\n",
+ " padding = 'post',\n",
+ " maxlen = review_len)\n",
+ "\n",
+ "x_test = keras.preprocessing.sequence.pad_sequences(x_test,\n",
+ " value = 0 ,\n",
+ " padding = 'post',\n",
+ " maxlen = review_len)\n",
+ "\n",
+ "pwk.subtitle('After padding :')\n",
+ "print(x_train[12])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Save dataset and dictionary (For future use but not mandatory)**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ---- Write dataset in a h5 file, could be usefull\n",
+ "#\n",
+ "pwk.mkdir(output_dir)\n",
+ "\n",
+ "with h5py.File(f'{output_dir}/dataset_imdb.h5', 'w') as f:\n",
+ " f.create_dataset(\"x_train\", data=x_train)\n",
+ " f.create_dataset(\"y_train\", data=y_train)\n",
+ " f.create_dataset(\"x_test\", data=x_test)\n",
+ " f.create_dataset(\"y_test\", data=y_test)\n",
+ "\n",
+ "with open(f'{output_dir}/word_index.json', 'w') as fp:\n",
+ " json.dump(word_index, fp)\n",
+ "\n",
+ "with open(f'{output_dir}/index_word.json', 'w') as fp:\n",
+ " json.dump(index_word, fp)\n",
+ "\n",
+ "print('Saved.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 4 - Build the model\n",
+ "Few remarks :\n",
+ " - We'll choose a dense vector size for the embedding output with **dense_vector_size**\n",
+ " - **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly) \n",
+ " In other words: we average the set of vectors/words of a sentence\n",
+ " - L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling) \n",
+ "Voir : [Explication plus détaillée (en)](https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer) \n",
+ "ainsi que : [Sentiment detection with Keras](https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks) \n",
+ "\n",
+ "More documentation about this model functions :\n",
+ " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
+ " - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_model(vocab_size=10000, dense_vector_size=32, review_len=256):\n",
+ " \n",
+ " model = keras.Sequential()\n",
+ " model.add(keras.layers.Input( shape=(review_len,) ))\n",
+ " model.add(keras.layers.Embedding(input_dim = vocab_size, \n",
+ " output_dim = dense_vector_size, \n",
+ " input_length = review_len))\n",
+ " model.add(keras.layers.GlobalAveragePooling1D())\n",
+ " model.add(keras.layers.Dense(dense_vector_size, activation='relu'))\n",
+ " model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
+ "\n",
+ " model.compile(optimizer = 'adam',\n",
+ " loss = 'binary_crossentropy',\n",
+ " metrics = ['accuracy'])\n",
+ " return model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 5 - Train the model\n",
+ "### 5.1 - Get it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = get_model(vocab_size, dense_vector_size, review_len)\n",
+ "\n",
+ "model.summary()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.2 - Add callback"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "os.makedirs(f'{run_dir}/models', mode=0o750, exist_ok=True)\n",
+ "save_dir = f'{run_dir}/models/best_model.h5'\n",
+ "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 5.1 - Train it"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%time\n",
+ "\n",
+ "history = model.fit(x_train,\n",
+ " y_train,\n",
+ " epochs = epochs,\n",
+ " batch_size = batch_size,\n",
+ " validation_data = (x_test, y_test),\n",
+ " verbose = 1,\n",
+ " callbacks = [savemodel_callback])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 6 - Evaluate\n",
+ "### 6.1 - Training history"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pwk.plot_history(history, save_as='02-history')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 6.2 - Reload and evaluate best model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = keras.models.load_model(f'{run_dir}/models/best_model.h5')\n",
+ "\n",
+ "# ---- Evaluate\n",
+ "score = model.evaluate(x_test, y_test, verbose=0)\n",
+ "\n",
+ "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
+ "print('x_test / accuracy : {:5.4f}'.format(score[1]))\n",
+ "\n",
+ "values=[score[1], 1-score[1]]\n",
+ "pwk.plot_donut(values,[\"Accuracy\",\"Errors\"], title=\"#### Accuracy donut is :\", save_as='03-donut')\n",
+ "\n",
+ "# ---- Confusion matrix\n",
+ "\n",
+ "y_sigmoid = model.predict(x_test)\n",
+ "\n",
+ "y_pred = y_sigmoid.copy()\n",
+ "y_pred[ y_sigmoid< 0.5 ] = 0\n",
+ "y_pred[ y_sigmoid>=0.5 ] = 1 \n",
+ "\n",
+ "pwk.display_confusion_matrix(y_test,y_pred,labels=range(2))\n",
+ "pwk.plot_confusion_matrix(y_test,y_pred,range(2), figsize=(8, 8),normalize=False, save_as='04-confusion-matrix')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pwk.end()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/IMDB/03-Prediction.ipynb b/IMDB/03-Prediction.ipynb
index 5010be9..5ed1ae0 100644
--- a/IMDB/03-Prediction.ipynb
+++ b/IMDB/03-Prediction.ipynb
@@ -6,21 +6,17 @@
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "# <!-- TITLE --> [IMDB2] - Reload and reuse a saved model\n",
+ "# <!-- TITLE --> [IMDB3] - Reload and reuse a saved model\n",
"<!-- DESC --> Retrieving a saved model to perform a sentiment analysis (movie review)\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
- " - The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \n",
+ " - The objective is to guess whether our personal film reviews are **positive or negative** based on the analysis of the text. \n",
" - For this, we will use our **previously saved model**.\n",
"\n",
- "Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)** \n",
- "Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
- "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
- "\n",
"## What we're going to do :\n",
"\n",
- " - Preparing the data\n",
+ " - Preparing our data\n",
" - Retrieve our saved model\n",
" - Evaluate the result\n"
]
@@ -34,97 +30,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "div.todo:before { content:url(data:image/svg+xml;base64,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);\n",
- " float:left;\n",
- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 0.6.1 DEV\n",
- "Notebook id : IMDB2\n",
- "Run time : Friday 18 December 2020, 18:21:49\n",
- "TensorFlow version : 2.0.0\n",
- "Keras version : 2.2.4-tf\n",
- "Datasets dir : /home/pjluc/datasets/fidle\n",
- "Running mode : full\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import numpy as np\n",
"\n",
@@ -143,7 +51,47 @@
"sys.path.append('..')\n",
"import fidle.pwk as pwk\n",
"\n",
- "datasets_dir = pwk.init('IMDB2')"
+ "run_dir = './run/IMDB2'\n",
+ "datasets_dir = pwk.init('IMDB3', run_dir)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1.2 - Parameters\n",
+ "The words in the vocabulary are classified from the most frequent to the rarest. \n",
+ "`vocab_size` is the number of words we will remember in our vocabulary (the other words will be considered as unknown). \n",
+ "`review_len` is the review length \n",
+ "`dictionaries_dir` is where we will go to save our dictionaries. (./data is a good choice)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vocab_size = 10000\n",
+ "review_len = 256\n",
+ "\n",
+ "dictionaries_dir = './data'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Override parameters (batch mode) - Just forget this cell"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pwk.override('vocab_size', 'review_len', 'dictionaries_dir')"
]
},
{
@@ -156,12 +104,13 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"reviews = [ \"This film is particularly nice, a must see.\",\n",
- " \"Some films are great classics and cannot be ignored.\",\n",
+ " \"This film is a great classic that cannot be ignored.\",\n",
+ " \"I don't remember ever having seen such a movie...\",\n",
" \"This movie is just abominable and doesn't deserve to be seen!\"]"
]
},
@@ -175,39 +124,42 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "with open('./data/word_index.json', 'r') as fp:\n",
+ "with open(f'{dictionaries_dir}/word_index.json', 'r') as fp:\n",
" word_index = json.load(fp)\n",
- " index_word = {index:word for word,index in word_index.items()} "
+ " word_index = { w:int(i) for w,i in word_index.items() }\n",
+ " print('Loaded. ', len(word_index), 'entries in word_index' )\n",
+ " index_word = { i:w for w,i in word_index.items() }\n",
+ " print('Loaded. ', len(index_word), 'entries in index_word' )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 2.3 - Clean, index and padd"
+ "### 2.3 - Clean, index and padd\n",
+ "Phases are split into words, punctuation is removed, sentence length is limited and padding is added... \n",
+ "**Note** : 1 is \"Start\" and 2 is \"unknown\""
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "max_len = 256\n",
- "vocab_size = 10000\n",
- "\n",
- "\n",
"nb_reviews = len(reviews)\n",
"x_data = []\n",
"\n",
"# ---- For all reviews\n",
"for review in reviews:\n",
+ " print('Words are : ', end='')\n",
" # ---- First index must be <start>\n",
" index_review=[1]\n",
+ " print('1 ', end='')\n",
" # ---- For all words\n",
" for w in review.split(' '):\n",
" # ---- Clean it\n",
@@ -219,11 +171,13 @@
" if w_index>vocab_size : w_index=2\n",
" # ---- Add the index if < vocab_size\n",
" index_review.append(w_index)\n",
+ " print(f'{w_index} ', end='')\n",
" # ---- Add the indexed review\n",
- " x_data.append(index_review) \n",
+ " x_data.append(index_review)\n",
+ " print()\n",
"\n",
"# ---- Padding\n",
- "x_data = keras.preprocessing.sequence.pad_sequences(x_data, value = 0, padding = 'post', maxlen = max_len)"
+ "x_data = keras.preprocessing.sequence.pad_sequences(x_data, value = 0, padding = 'post', maxlen = review_len)"
]
},
{
@@ -235,28 +189,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Text review : This film is particularly nice, a must see.\n",
- "x_train[0] : [1, 2, 22, 9, 572, 2, 6, 215, 2, 0, 0, 0, 0, 0] (...)\n",
- "Translation : <start> <unknown> film is particularly <unknown> a must <unknown> <pad> <pad> <pad> <pad> <pad> (...)\n",
- "\n",
- "Text review : Some films are great classics and cannot be ignored.\n",
- "x_train[1] : [1, 2, 108, 26, 87, 2239, 5, 566, 30, 2, 0, 0, 0, 0, 0] (...)\n",
- "Translation : <start> <unknown> films are great classics and cannot be <unknown> <pad> <pad> <pad> <pad> <pad> (...)\n",
- "\n",
- "Text review : This movie is just abominable and doesn't deserve to be seen!\n",
- "x_train[2] : [1, 2, 20, 9, 43, 2, 5, 152, 1833, 8, 30, 2, 0, 0, 0, 0, 0] (...)\n",
- "Translation : <start> <unknown> movie is just <unknown> and doesn't deserve to be <unknown> <pad> <pad> <pad> <pad> <pad> (...)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"def translate(x):\n",
" return ' '.join( [index_word.get(i,'?') for i in x] )\n",
@@ -277,11 +212,11 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "model = keras.models.load_model('./run/models/best_model.h5')"
+ "model = keras.models.load_model(f'{run_dir}/models/best_model.h5')"
]
},
{
@@ -293,7 +228,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -309,42 +244,21 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "This film is particularly nice, a must see. => POSITIVE (0.56)\n",
- "\n",
- "Some films are great classics and cannot be ignored. => POSITIVE (0.63)\n",
- "\n",
- "This movie is just abominable and doesn't deserve to be seen! => NEGATIVE (0.35)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "for i in range(nb_reviews):\n",
- " print(f'\\n{reviews[i]:<70} =>',('NEGATIVE' if y_pred[i][0]<0.5 else 'POSITIVE'),f'({y_pred[i][0]:.2f})')"
+ "for i,review in enumerate(reviews):\n",
+ " rate = y_pred[i][0]\n",
+ " opinion = 'NEGATIVE :-(' if rate<0.5 else 'POSITIVE :-)' \n",
+ " print(f'{review:<70} => {rate:.2f} - {opinion}')"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Friday 18 December 2020, 18:21:50\n",
- "Duration is : 00:00:01 555ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pwk.end()"
]
diff --git a/IMDB/03-Prediction==done==.ipynb b/IMDB/03-Prediction==done==.ipynb
deleted file mode 100644
index a602b2e..0000000
--- a/IMDB/03-Prediction==done==.ipynb
+++ /dev/null
@@ -1,445 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [IMDB2] - Reload and reuse a saved model\n",
- "<!-- DESC --> Retrieving a saved model to perform a sentiment analysis (movie review)\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \n",
- " - For this, we will use our **previously saved model**.\n",
- "\n",
- "Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)** \n",
- "Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
- "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Preparing the data\n",
- " - Retrieve our saved model\n",
- " - Evaluate the result\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Init python stuff"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:34.772909Z",
- "iopub.status.busy": "2021-02-10T09:47:34.772435Z",
- "iopub.status.idle": "2021-02-10T09:47:37.370802Z",
- "shell.execute_reply": "2021-02-10T09:47:37.371290Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<style>\n",
- "\n",
- "div.warn { \n",
- " background-color: #fcf2f2;\n",
- " border-color: #dFb5b4;\n",
- " border-left: 5px solid #dfb5b4;\n",
- " padding: 0.5em;\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;;\n",
- " }\n",
- "\n",
- "\n",
- "\n",
- "div.nota { \n",
- " background-color: #DAFFDE;\n",
- " border-left: 5px solid #92CC99;\n",
- " padding: 0.5em;\n",
- " }\n",
- "\n",
- "div.todo:before { content:url(data:image/svg+xml;base64,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);\n",
- " float:left;\n",
- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
- "}\n",
- "div.todo{\n",
- " font-weight: bold;\n",
- " font-size: 1.1em;\n",
- " margin-top:40px;\n",
- "}\n",
- "div.todo ul{\n",
- " margin: 0.2em;\n",
- "}\n",
- "div.todo li{\n",
- " margin-left:60px;\n",
- " margin-top:0;\n",
- " margin-bottom:0;\n",
- "}\n",
- "\n",
- "div .comment{\n",
- " font-size:0.8em;\n",
- " color:#696969;\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "</style>\n",
- "\n"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.14\n",
- "Notebook id : IMDB2\n",
- "Run time : Wednesday 10 February 2021, 10:47:37\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "\n",
- "import tensorflow as tf\n",
- "import tensorflow.keras as keras\n",
- "import tensorflow.keras.datasets.imdb as imdb\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib\n",
- "import pandas as pd\n",
- "\n",
- "import os,sys,h5py,json,re\n",
- "\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "datasets_dir = pwk.init('IMDB2')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 : Preparing the data\n",
- "### 2.1 - Our reviews :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:37.374552Z",
- "iopub.status.busy": "2021-02-10T09:47:37.374081Z",
- "iopub.status.idle": "2021-02-10T09:47:37.375678Z",
- "shell.execute_reply": "2021-02-10T09:47:37.376144Z"
- }
- },
- "outputs": [],
- "source": [
- "reviews = [ \"This film is particularly nice, a must see.\",\n",
- " \"Some films are great classics and cannot be ignored.\",\n",
- " \"This movie is just abominable and doesn't deserve to be seen!\"]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Retrieve dictionaries\n",
- "Note : This dictionary is generated by [01-Embedding-Keras](01-Embedding-Keras.ipynb) notebook."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:37.379502Z",
- "iopub.status.busy": "2021-02-10T09:47:37.379033Z",
- "iopub.status.idle": "2021-02-10T09:47:37.430816Z",
- "shell.execute_reply": "2021-02-10T09:47:37.430303Z"
- }
- },
- "outputs": [],
- "source": [
- "with open('./data/word_index.json', 'r') as fp:\n",
- " word_index = json.load(fp)\n",
- " index_word = {index:word for word,index in word_index.items()} "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.3 - Clean, index and padd"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:37.436960Z",
- "iopub.status.busy": "2021-02-10T09:47:37.436484Z",
- "iopub.status.idle": "2021-02-10T09:47:37.438102Z",
- "shell.execute_reply": "2021-02-10T09:47:37.438575Z"
- }
- },
- "outputs": [],
- "source": [
- "max_len = 256\n",
- "vocab_size = 10000\n",
- "\n",
- "\n",
- "nb_reviews = len(reviews)\n",
- "x_data = []\n",
- "\n",
- "# ---- For all reviews\n",
- "for review in reviews:\n",
- " # ---- First index must be <start>\n",
- " index_review=[1]\n",
- " # ---- For all words\n",
- " for w in review.split(' '):\n",
- " # ---- Clean it\n",
- " w_clean = re.sub(r\"[^a-zA-Z0-9]\", \"\", w)\n",
- " # ---- Not empty ?\n",
- " if len(w_clean)>0:\n",
- " # ---- Get the index\n",
- " w_index = word_index.get(w,2)\n",
- " if w_index>vocab_size : w_index=2\n",
- " # ---- Add the index if < vocab_size\n",
- " index_review.append(w_index)\n",
- " # ---- Add the indexed review\n",
- " x_data.append(index_review) \n",
- "\n",
- "# ---- Padding\n",
- "x_data = keras.preprocessing.sequence.pad_sequences(x_data, value = 0, padding = 'post', maxlen = max_len)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.4 - Have a look"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:37.444090Z",
- "iopub.status.busy": "2021-02-10T09:47:37.443629Z",
- "iopub.status.idle": "2021-02-10T09:47:37.447041Z",
- "shell.execute_reply": "2021-02-10T09:47:37.447515Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Text review : This film is particularly nice, a must see.\n",
- "x_train[0] : [1, 2, 22, 9, 572, 2, 6, 215, 2, 0, 0, 0, 0, 0] (...)\n",
- "Translation : <start> <unknown> film is particularly <unknown> a must <unknown> <pad> <pad> <pad> <pad> <pad> (...)\n",
- "\n",
- "Text review : Some films are great classics and cannot be ignored.\n",
- "x_train[1] : [1, 2, 108, 26, 87, 2239, 5, 566, 30, 2, 0, 0, 0, 0, 0] (...)\n",
- "Translation : <start> <unknown> films are great classics and cannot be <unknown> <pad> <pad> <pad> <pad> <pad> (...)\n",
- "\n",
- "Text review : This movie is just abominable and doesn't deserve to be seen!\n",
- "x_train[2] : [1, 2, 20, 9, 43, 2, 5, 152, 1833, 8, 30, 2, 0, 0, 0, 0, 0] (...)\n",
- "Translation : <start> <unknown> movie is just <unknown> and doesn't deserve to be <unknown> <pad> <pad> <pad> <pad> <pad> (...)\n"
- ]
- }
- ],
- "source": [
- "def translate(x):\n",
- " return ' '.join( [index_word.get(i,'?') for i in x] )\n",
- "\n",
- "for i in range(nb_reviews):\n",
- " imax=np.where(x_data[i]==0)[0][0]+5\n",
- " print(f'\\nText review :', reviews[i])\n",
- " print( f'x_train[{i:}] :', list(x_data[i][:imax]), '(...)')\n",
- " print( 'Translation :', translate(x_data[i][:imax]), '(...)')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Bring back the model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:37.450800Z",
- "iopub.status.busy": "2021-02-10T09:47:37.450034Z",
- "iopub.status.idle": "2021-02-10T09:47:38.436118Z",
- "shell.execute_reply": "2021-02-10T09:47:38.435552Z"
- }
- },
- "outputs": [],
- "source": [
- "model = keras.models.load_model('./run/models/best_model.h5')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Predict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:38.439376Z",
- "iopub.status.busy": "2021-02-10T09:47:38.438919Z",
- "iopub.status.idle": "2021-02-10T09:47:38.873021Z",
- "shell.execute_reply": "2021-02-10T09:47:38.873546Z"
- }
- },
- "outputs": [],
- "source": [
- "y_pred = model.predict(x_data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### And the winner is :"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:38.877372Z",
- "iopub.status.busy": "2021-02-10T09:47:38.876910Z",
- "iopub.status.idle": "2021-02-10T09:47:38.880363Z",
- "shell.execute_reply": "2021-02-10T09:47:38.879875Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "This film is particularly nice, a must see. => POSITIVE (0.60)\n",
- "\n",
- "Some films are great classics and cannot be ignored. => POSITIVE (0.66)\n",
- "\n",
- "This movie is just abominable and doesn't deserve to be seen! => NEGATIVE (0.38)\n"
- ]
- }
- ],
- "source": [
- "for i in range(nb_reviews):\n",
- " print(f'\\n{reviews[i]:<70} =>',('NEGATIVE' if y_pred[i][0]<0.5 else 'POSITIVE'),f'({y_pred[i][0]:.2f})')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:38.883481Z",
- "iopub.status.busy": "2021-02-10T09:47:38.883018Z",
- "iopub.status.idle": "2021-02-10T09:47:38.885320Z",
- "shell.execute_reply": "2021-02-10T09:47:38.885799Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Wednesday 10 February 2021, 10:47:38\n",
- "Duration is : 00:00:02 517ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
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diff --git a/IMDB/04-LSTM-Keras==done==.ipynb b/IMDB/04-LSTM-Keras==done==.ipynb
deleted file mode 100644
index 5dda88f..0000000
--- a/IMDB/04-LSTM-Keras==done==.ipynb
+++ /dev/null
@@ -1,5169 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
- "\n",
- "# <!-- TITLE --> [IMDB3] - Sentiment analysis with a LSTM network\n",
- "<!-- DESC --> Still the same problem, but with a network combining embedding and LSTM\n",
- "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
- "\n",
- "## Objectives :\n",
- " - The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \n",
- " - Use of a model combining embedding and LSTM\n",
- "\n",
- "Original dataset can be find **[there](http://ai.stanford.edu/~amaas/data/sentiment/)** \n",
- "Note that [IMDb.com](https://imdb.com) offers several easy-to-use [datasets](https://www.imdb.com/interfaces/) \n",
- "For simplicity's sake, we'll use the dataset directly [embedded in Keras](https://www.tensorflow.org/api_docs/python/tf/keras/datasets)\n",
- "\n",
- "## What we're going to do :\n",
- "\n",
- " - Retrieve data\n",
- " - Preparing the data\n",
- " - Build a Embedding/LSTM model\n",
- " - Train the model\n",
- " - Evaluate the result\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 1 - Init python stuff"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:40.403238Z",
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- "iopub.status.idle": "2021-02-10T09:47:42.954102Z",
- "shell.execute_reply": "2021-02-10T09:47:42.954594Z"
- }
- },
- "outputs": [
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- " margin-right:20px;\n",
- " margin-top:-20px;\n",
- " margin-bottom:20px;\n",
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- " font-weight: bold;\n",
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- "<IPython.core.display.HTML object>"
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- "metadata": {},
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- "data": {
- "text/markdown": [
- "<br>**FIDLE 2020 - Practical Work Module**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Version : 2.0.14\n",
- "Notebook id : IMDB3\n",
- "Run time : Wednesday 10 February 2021, 10:47:42\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/figs\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "\n",
- "import tensorflow as tf\n",
- "import tensorflow.keras as keras\n",
- "import tensorflow.keras.datasets.imdb as imdb\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib\n",
- "\n",
- "import os,sys,h5py,json\n",
- "from importlib import reload\n",
- "\n",
- "sys.path.append('..')\n",
- "import fidle.pwk as pwk\n",
- "\n",
- "datasets_dir = pwk.init('IMDB3')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 2 - Retrieve data\n",
- "\n",
- "IMDb dataset can bet get directly from Keras - see [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) \n",
- "Note : Due to their nature, textual data can be somewhat complex.\n",
- "\n",
- "### 2.1 - Data structure : \n",
- "The dataset is composed of 2 parts: \n",
- "\n",
- " - **reviews**, this will be our **x**\n",
- " - **opinions** (positive/negative), this will be our **y**\n",
- "\n",
- "There are also a **dictionary**, because words are indexed in reviews\n",
- "\n",
- "```\n",
- "<dataset> = (<reviews>, <opinions>)\n",
- "\n",
- "with : <reviews> = [ <review1>, <review2>, ... ]\n",
- " <opinions> = [ <rate1>, <rate2>, ... ] where <ratei> = integer\n",
- "\n",
- "where : <reviewi> = [ <w1>, <w2>, ...] <wi> are the index (int) of the word in the dictionary\n",
- " <ratei> = int 0 for negative opinion, 1 for positive\n",
- "\n",
- "\n",
- "<dictionary> = [ <word1>:<w1>, <word2>:<w2>, ... ]\n",
- "\n",
- "with : <wordi> = word\n",
- " <wi> = int\n",
- "\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.2 - Get dataset\n",
- "For simplicity, we will use a pre-formatted dataset - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data) \n",
- "However, Keras offers some usefull tools for formatting textual data - See [documentation](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text) \n",
- "\n",
- "**Load dataset :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:42.958761Z",
- "iopub.status.busy": "2021-02-10T09:47:42.958282Z",
- "iopub.status.idle": "2021-02-10T09:47:48.094478Z",
- "shell.execute_reply": "2021-02-10T09:47:48.094966Z"
- }
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "<string>:6: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/keras/datasets/imdb.py:159: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
- " x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/keras/datasets/imdb.py:160: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
- " x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])\n"
- ]
- }
- ],
- "source": [
- "vocab_size = 10000\n",
- "\n",
- "# ----- Retrieve x,y\n",
- "\n",
- "# Uncomment this if you want to load dataset directly from keras (small size <20M)\n",
- "#\n",
- "(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words = vocab_size,\n",
- " skip_top = 0,\n",
- " maxlen = None,\n",
- " seed = 42,\n",
- " start_char = 1,\n",
- " oov_char = 2,\n",
- " index_from = 3, )\n",
- "\n",
- "# To load a h5 version of the dataset :\n",
- "#\n",
- "# with h5py.File(f'{datasets_dir}/IMDB/origine/dataset_imdb.h5','r') as f:\n",
- "# x_train = f['x_train'][:]\n",
- "# y_train = f['y_train'][:]\n",
- "# x_test = f['x_test'][:]\n",
- "# y_test = f['y_test'][:]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**About this dataset :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:48.122393Z",
- "iopub.status.busy": "2021-02-10T09:47:48.107189Z",
- "iopub.status.idle": "2021-02-10T09:47:53.626466Z",
- "shell.execute_reply": "2021-02-10T09:47:53.625964Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " Max(x_train,x_test) : 9999\n",
- " x_train : (25000,) y_train : (25000,)\n",
- " x_test : (25000,) y_test : (25000,)\n",
- "\n",
- "Review example (x_train[12]) :\n",
- "\n",
- " [1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]\n"
- ]
- }
- ],
- "source": [
- "print(\" Max(x_train,x_test) : \", pwk.rmax([x_train,x_test]) )\n",
- "print(\" x_train : {} y_train : {}\".format(x_train.shape, y_train.shape))\n",
- "print(\" x_test : {} y_test : {}\".format(x_test.shape, y_test.shape))\n",
- "\n",
- "print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.3 - Have a look for humans (optional)\n",
- "When we loaded the dataset, we asked for using \\<start\\> as 1, \\<unknown word\\> as 2 \n",
- "So, we shifted the dataset by 3 with the parameter index_from=3\n",
- "\n",
- "**Load dictionary :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:53.631158Z",
- "iopub.status.busy": "2021-02-10T09:47:53.630689Z",
- "iopub.status.idle": "2021-02-10T09:47:53.707304Z",
- "shell.execute_reply": "2021-02-10T09:47:53.707801Z"
- }
- },
- "outputs": [],
- "source": [
- "# ---- Retrieve dictionary {word:index}, and encode it in ascii\n",
- "#\n",
- "word_index = imdb.get_word_index()\n",
- "\n",
- "# ---- Shift the dictionary from +3\n",
- "#\n",
- "word_index = {w:(i+3) for w,i in word_index.items()}\n",
- "\n",
- "# ---- Add <pad>, <start> and unknown tags\n",
- "#\n",
- "word_index.update( {'<pad>':0, '<start>':1, '<unknown>':2} )\n",
- "\n",
- "# ---- Create a reverse dictionary : {index:word}\n",
- "#\n",
- "index_word = {index:word for word,index in word_index.items()} \n",
- "\n",
- "# ---- Add a nice function to transpose :\n",
- "#\n",
- "def dataset2text(review):\n",
- " return ' '.join([index_word.get(i, '?') for i in review])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Have a look :**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:53.712113Z",
- "iopub.status.busy": "2021-02-10T09:47:53.711646Z",
- "iopub.status.idle": "2021-02-10T09:47:53.717597Z",
- "shell.execute_reply": "2021-02-10T09:47:53.718077Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Dictionary size : 88587\n",
- "440 : hope\n",
- "441 : entertaining\n",
- "442 : she's\n",
- "443 : mr\n",
- "444 : overall\n",
- "445 : evil\n",
- "446 : called\n",
- "447 : loved\n",
- "448 : based\n",
- "449 : oh\n",
- "450 : several\n",
- "451 : fans\n",
- "452 : mother\n",
- "453 : drama\n",
- "454 : beginning\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**Review example :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1, 14, 22, 1367, 53, 206, 159, 4, 636, 898, 74, 26, 11, 436, 363, 108, 7, 14, 432, 14, 22, 9, 1055, 34, 8599, 2, 5, 381, 3705, 4509, 14, 768, 47, 839, 25, 111, 1517, 2579, 1991, 438, 2663, 587, 4, 280, 725, 6, 58, 11, 2714, 201, 4, 206, 16, 702, 5, 5176, 19, 480, 5920, 157, 13, 64, 219, 4, 2, 11, 107, 665, 1212, 39, 4, 206, 4, 65, 410, 16, 565, 5, 24, 43, 343, 17, 5602, 8, 169, 101, 85, 206, 108, 8, 3008, 14, 25, 215, 168, 18, 6, 2579, 1991, 438, 2, 11, 129, 1609, 36, 26, 66, 290, 3303, 46, 5, 633, 115, 4363]\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**After translation :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint\n"
- ]
- }
- ],
- "source": [
- "print('\\nDictionary size : ', len(word_index))\n",
- "for k in range(440,455):print(f'{k:2d} : {index_word[k]}' )\n",
- "pwk.subtitle('Review example :')\n",
- "print(x_train[12])\n",
- "pwk.subtitle('After translation :')\n",
- "print(dataset2text(x_train[12]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2.4 - Have a look for NN"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:53.737509Z",
- "iopub.status.busy": "2021-02-10T09:47:53.737038Z",
- "iopub.status.idle": "2021-02-10T09:47:55.757897Z",
- "shell.execute_reply": "2021-02-10T09:47:55.758380Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/IMDB3-01-stats-sizes</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
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- "data": {
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\n",
- "text/plain": [
- "<Figure size 1152x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "sizes=[len(i) for i in x_train]\n",
- "plt.figure(figsize=(16,6))\n",
- "plt.hist(sizes, bins=400)\n",
- "plt.gca().set(title='Distribution of reviews by size - [{:5.2f}, {:5.2f}]'.format(min(sizes),max(sizes)), \n",
- " xlabel='Size', ylabel='Density', xlim=[0,1500])\n",
- "pwk.save_fig('01-stats-sizes')\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 3 - Preprocess the data (padding)\n",
- "In order to be processed by an NN, all entries must have the **same length.** \n",
- "We chose a review length of **review_len** \n",
- "We will therefore complete them with a padding (of \\<pad\\>\\) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:55.767143Z",
- "iopub.status.busy": "2021-02-10T09:47:55.766668Z",
- "iopub.status.idle": "2021-02-10T09:47:56.861982Z",
- "shell.execute_reply": "2021-02-10T09:47:56.861481Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "<br>**After padding :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[ 1 14 22 1367 53 206 159 4 636 898 74 26 11 436\n",
- " 363 108 7 14 432 14 22 9 1055 34 8599 2 5 381\n",
- " 3705 4509 14 768 47 839 25 111 1517 2579 1991 438 2663 587\n",
- " 4 280 725 6 58 11 2714 201 4 206 16 702 5 5176\n",
- " 19 480 5920 157 13 64 219 4 2 11 107 665 1212 39\n",
- " 4 206 4 65 410 16 565 5 24 43 343 17 5602 8\n",
- " 169 101 85 206 108 8 3008 14 25 215 168 18 6 2579\n",
- " 1991 438 2 11 129 1609 36 26 66 290 3303 46 5 633\n",
- " 115 4363 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0]\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "<br>**In real words :**"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<start> this film contains more action before the opening credits than are in entire hollywood films of this sort this film is produced by tsui <unknown> and stars jet li this team has brought you many worthy hong kong cinema productions including the once upon a time in china series the action was fast and furious with amazing wire work i only saw the <unknown> in two shots aside from the action the story itself was strong and not just used as filler to find any other action films to rival this you must look for a hong kong cinema <unknown> in your area they are really worth checking out and usually never disappoint <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad> <pad>\n"
- ]
- }
- ],
- "source": [
- "review_len = 256\n",
- "\n",
- "x_train = keras.preprocessing.sequence.pad_sequences(x_train,\n",
- " value = 0,\n",
- " padding = 'post',\n",
- " maxlen = review_len)\n",
- "\n",
- "x_test = keras.preprocessing.sequence.pad_sequences(x_test,\n",
- " value = 0 ,\n",
- " padding = 'post',\n",
- " maxlen = review_len)\n",
- "\n",
- "pwk.subtitle('After padding :')\n",
- "print(x_train[12])\n",
- "pwk.subtitle('In real words :')\n",
- "print(dataset2text(x_train[12]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "**Save dataset and dictionary (For future use but not mandatory)**"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:56.965471Z",
- "iopub.status.busy": "2021-02-10T09:47:56.866404Z",
- "iopub.status.idle": "2021-02-10T09:47:57.267049Z",
- "shell.execute_reply": "2021-02-10T09:47:57.267536Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Saved.\n"
- ]
- }
- ],
- "source": [
- "# ---- Write dataset in a h5 file, could be usefull\n",
- "#\n",
- "output_dir = './data'\n",
- "pwk.mkdir(output_dir)\n",
- "\n",
- "with h5py.File(f'{output_dir}/dataset_imdb.h5', 'w') as f:\n",
- " f.create_dataset(\"x_train\", data=x_train)\n",
- " f.create_dataset(\"y_train\", data=y_train)\n",
- " f.create_dataset(\"x_test\", data=x_test)\n",
- " f.create_dataset(\"y_test\", data=y_test)\n",
- "\n",
- "with open(f'{output_dir}/word_index.json', 'w') as fp:\n",
- " json.dump(word_index, fp)\n",
- "\n",
- "with open(f'{output_dir}/index_word.json', 'w') as fp:\n",
- " json.dump(index_word, fp)\n",
- "\n",
- "print('Saved.')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 4 - Build the model\n",
- "Few remarks :\n",
- " - We'll choose a dense vector size for the embedding output with **dense_vector_size**\n",
- " - **GlobalAveragePooling1D** do a pooling on the last dimension : (None, lx, ly) -> (None, ly) \n",
- " In other words: we average the set of vectors/words of a sentence\n",
- " - L'embedding de Keras fonctionne de manière supervisée. Il s'agit d'une couche de *vocab_size* neurones vers *n_neurons* permettant de maintenir une table de vecteurs (les poids constituent les vecteurs). Cette couche ne calcule pas de sortie a la façon des couches normales, mais renvois la valeur des vecteurs. n mots => n vecteurs (ensuite empilés par le pooling) \n",
- "Voir : [Explication plus détaillée (en)](https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer) \n",
- "ainsi que : [Sentiment detection with Keras](https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks) \n",
- "\n",
- "More documentation about this model functions :\n",
- " - [Embedding](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding)\n",
- " - [GlobalAveragePooling1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GlobalAveragePooling1D)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:57.272411Z",
- "iopub.status.busy": "2021-02-10T09:47:57.271947Z",
- "iopub.status.idle": "2021-02-10T09:47:57.273607Z",
- "shell.execute_reply": "2021-02-10T09:47:57.274077Z"
- }
- },
- "outputs": [],
- "source": [
- "def get_model(dense_vector_size=128):\n",
- " \n",
- " model = keras.Sequential()\n",
- " model.add(keras.layers.Embedding(input_dim = vocab_size, \n",
- " output_dim = dense_vector_size, \n",
- " input_length = review_len))\n",
- " model.add(keras.layers.LSTM(128, dropout=0.2, recurrent_dropout=0.2))\n",
- " model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
- "\n",
- " model.compile(optimizer = 'adam',\n",
- " loss = 'binary_crossentropy',\n",
- " metrics = ['accuracy'])\n",
- " return model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 5 - Train the model\n",
- "### 5.1 - Get it"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:57.277066Z",
- "iopub.status.busy": "2021-02-10T09:47:57.276605Z",
- "iopub.status.idle": "2021-02-10T09:47:58.288633Z",
- "shell.execute_reply": "2021-02-10T09:47:58.289117Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Model: \"sequential\"\n",
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "embedding (Embedding) (None, 256, 32) 320000 \n",
- "_________________________________________________________________\n",
- "lstm (LSTM) (None, 128) 82432 \n",
- "_________________________________________________________________\n",
- "dense (Dense) (None, 1) 129 \n",
- "=================================================================\n",
- "Total params: 402,561\n",
- "Trainable params: 402,561\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model = get_model(32)\n",
- "\n",
- "model.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.2 - Add callback"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:58.292692Z",
- "iopub.status.busy": "2021-02-10T09:47:58.292220Z",
- "iopub.status.idle": "2021-02-10T09:47:58.294168Z",
- "shell.execute_reply": "2021-02-10T09:47:58.294641Z"
- }
- },
- "outputs": [],
- "source": [
- "os.makedirs('./run/models', mode=0o750, exist_ok=True)\n",
- "save_dir = \"./run/models/best_model.h5\"\n",
- "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=save_dir, verbose=0, save_best_only=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 5.1 - Train it\n",
- "GPU : batch_size=512 : 6' 30s \n",
- "CPU : batch_size=512 : 12' 57s"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:47:58.298681Z",
- "iopub.status.busy": "2021-02-10T09:47:58.298205Z",
- "iopub.status.idle": "2021-02-10T09:53:37.415691Z",
- "shell.execute_reply": "2021-02-10T09:53:37.416178Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 4:18 - loss: 0.6935 - accuracy: 0.4883"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 29s - loss: 0.6938 - accuracy: 0.4761 "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 28s - loss: 0.6938 - accuracy: 0.4747"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.6938 - accuracy: 0.4770"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 27s - loss: 0.6938 - accuracy: 0.4784"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.6938 - accuracy: 0.4791"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.6937 - accuracy: 0.4794"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 25s - loss: 0.6937 - accuracy: 0.4803"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.6937 - accuracy: 0.4813"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 23s - loss: 0.6937 - accuracy: 0.4825"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.6937 - accuracy: 0.4836"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "12/49 [======>.......................] - ETA: 22s - loss: 0.6936 - accuracy: 0.4848"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 22s - loss: 0.6936 - accuracy: 0.4859"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "14/49 [=======>......................] - ETA: 21s - loss: 0.6936 - accuracy: 0.4869"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "15/49 [========>.....................] - ETA: 20s - loss: 0.6936 - accuracy: 0.4879"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "16/49 [========>.....................] - ETA: 20s - loss: 0.6936 - accuracy: 0.4888"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 19s - loss: 0.6935 - accuracy: 0.4896"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "18/49 [==========>...................] - ETA: 19s - loss: 0.6935 - accuracy: 0.4902"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 18s - loss: 0.6935 - accuracy: 0.4907"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "20/49 [===========>..................] - ETA: 17s - loss: 0.6935 - accuracy: 0.4912"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "23/49 [=============>................] - ETA: 16s - loss: 0.6935 - accuracy: 0.4926"
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- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "28/49 [================>.............] - ETA: 13s - loss: 0.6934 - accuracy: 0.4950"
- ]
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- "29/49 [================>.............] - ETA: 12s - loss: 0.6934 - accuracy: 0.4955"
- ]
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- "30/49 [=================>............] - ETA: 11s - loss: 0.6934 - accuracy: 0.4960"
- ]
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- "31/49 [=================>............] - ETA: 11s - loss: 0.6934 - accuracy: 0.4965"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "32/49 [==================>...........] - ETA: 10s - loss: 0.6934 - accuracy: 0.4970"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 9s - loss: 0.6934 - accuracy: 0.4975 "
- ]
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 9s - loss: 0.6933 - accuracy: 0.4980"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "35/49 [====================>.........] - ETA: 8s - loss: 0.6933 - accuracy: 0.4985"
- ]
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "36/49 [=====================>........] - ETA: 8s - loss: 0.6933 - accuracy: 0.4990"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 7s - loss: 0.6933 - accuracy: 0.4994"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 6s - loss: 0.6933 - accuracy: 0.4997"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.6933 - accuracy: 0.5001"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 5s - loss: 0.6933 - accuracy: 0.5004"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 4s - loss: 0.6932 - accuracy: 0.5007"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 4s - loss: 0.6932 - accuracy: 0.5011"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 3s - loss: 0.6932 - accuracy: 0.5014"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "44/49 [=========================>....] - ETA: 3s - loss: 0.6932 - accuracy: 0.5016"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "45/49 [==========================>...] - ETA: 2s - loss: 0.6932 - accuracy: 0.5019"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.6931 - accuracy: 0.5023"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.6931 - accuracy: 0.5026"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.6930 - accuracy: 0.5030"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.6930 - accuracy: 0.5033"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 39s 709ms/step - loss: 0.6929 - accuracy: 0.5036 - val_loss: 0.6869 - val_accuracy: 0.5288\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 2/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 31s - loss: 0.6897 - accuracy: 0.5254"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 29s - loss: 0.6889 - accuracy: 0.5254"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 28s - loss: 0.6884 - accuracy: 0.5247"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.6881 - accuracy: 0.5250"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 27s - loss: 0.6880 - accuracy: 0.5247"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.6881 - accuracy: 0.5239"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.6881 - accuracy: 0.5233"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 25s - loss: 0.6881 - accuracy: 0.5233"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.6880 - accuracy: 0.5237"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 23s - loss: 0.6879 - accuracy: 0.5239"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.6878 - accuracy: 0.5244"
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- },
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- "12/49 [======>.......................] - ETA: 22s - loss: 0.6878 - accuracy: 0.5249"
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- "13/49 [======>.......................] - ETA: 22s - loss: 0.6877 - accuracy: 0.5254"
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- "14/49 [=======>......................] - ETA: 21s - loss: 0.6876 - accuracy: 0.5258"
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- "15/49 [========>.....................] - ETA: 20s - loss: 0.6876 - accuracy: 0.5262"
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- "16/49 [========>.....................] - ETA: 20s - loss: 0.6875 - accuracy: 0.5265"
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- "17/49 [=========>....................] - ETA: 19s - loss: 0.6875 - accuracy: 0.5268"
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- "18/49 [==========>...................] - ETA: 19s - loss: 0.6874 - accuracy: 0.5271"
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- "19/49 [==========>...................] - ETA: 18s - loss: 0.6874 - accuracy: 0.5274"
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- "20/49 [===========>..................] - ETA: 17s - loss: 0.6873 - accuracy: 0.5278"
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- "21/49 [===========>..................] - ETA: 17s - loss: 0.6873 - accuracy: 0.5282"
- ]
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- "22/49 [============>.................] - ETA: 16s - loss: 0.6872 - accuracy: 0.5286"
- ]
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- "23/49 [=============>................] - ETA: 16s - loss: 0.6872 - accuracy: 0.5289"
- ]
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- "24/49 [=============>................] - ETA: 15s - loss: 0.6871 - accuracy: 0.5293"
- ]
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- "25/49 [==============>...............] - ETA: 14s - loss: 0.6871 - accuracy: 0.5296"
- ]
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- "26/49 [==============>...............] - ETA: 14s - loss: 0.6870 - accuracy: 0.5300"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "27/49 [===============>..............] - ETA: 13s - loss: 0.6870 - accuracy: 0.5303"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "28/49 [================>.............] - ETA: 12s - loss: 0.6869 - accuracy: 0.5306"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "29/49 [================>.............] - ETA: 12s - loss: 0.6869 - accuracy: 0.5309"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "30/49 [=================>............] - ETA: 11s - loss: 0.6868 - accuracy: 0.5311"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 11s - loss: 0.6867 - accuracy: 0.5314"
- ]
- },
- {
- "name": "stdout",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "32/49 [==================>...........] - ETA: 10s - loss: 0.6867 - accuracy: 0.5316"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 9s - loss: 0.6866 - accuracy: 0.5318 "
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 9s - loss: 0.6865 - accuracy: 0.5320"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "35/49 [====================>.........] - ETA: 8s - loss: 0.6864 - accuracy: 0.5322"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "36/49 [=====================>........] - ETA: 7s - loss: 0.6864 - accuracy: 0.5324"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 7s - loss: 0.6863 - accuracy: 0.5327"
- ]
- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 6s - loss: 0.6862 - accuracy: 0.5331"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.6861 - accuracy: 0.5334"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "45/49 [==========================>...] - ETA: 2s - loss: 0.6858 - accuracy: 0.5350"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.6857 - accuracy: 0.5353"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.6857 - accuracy: 0.5355"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.6856 - accuracy: 0.5357"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.6856 - accuracy: 0.5359"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 33s 680ms/step - loss: 0.6855 - accuracy: 0.5361 - val_loss: 0.6823 - val_accuracy: 0.5360\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 3/10\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 31s - loss: 0.6761 - accuracy: 0.5527"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 28s - loss: 0.6766 - accuracy: 0.5547"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 27s - loss: 0.6760 - accuracy: 0.5603"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.6759 - accuracy: 0.5608"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 26s - loss: 0.6758 - accuracy: 0.5619"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.6759 - accuracy: 0.5617"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.6758 - accuracy: 0.5617"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 25s - loss: 0.6757 - accuracy: 0.5620"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.6757 - accuracy: 0.5619"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 23s - loss: 0.6756 - accuracy: 0.5622"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.6756 - accuracy: 0.5620"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "12/49 [======>.......................] - ETA: 22s - loss: 0.6755 - accuracy: 0.5622"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 22s - loss: 0.6754 - accuracy: 0.5624"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "14/49 [=======>......................] - ETA: 21s - loss: 0.6754 - accuracy: 0.5625"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "15/49 [========>.....................] - ETA: 20s - loss: 0.6753 - accuracy: 0.5626"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "16/49 [========>.....................] - ETA: 20s - loss: 0.6752 - accuracy: 0.5628"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 19s - loss: 0.6751 - accuracy: 0.5628"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "18/49 [==========>...................] - ETA: 19s - loss: 0.6750 - accuracy: 0.5630"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 18s - loss: 0.6750 - accuracy: 0.5632"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "20/49 [===========>..................] - ETA: 17s - loss: 0.6749 - accuracy: 0.5633"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "21/49 [===========>..................] - ETA: 17s - loss: 0.6748 - accuracy: 0.5634"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "22/49 [============>.................] - ETA: 16s - loss: 0.6747 - accuracy: 0.5635"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "23/49 [=============>................] - ETA: 15s - loss: 0.6746 - accuracy: 0.5637"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "24/49 [=============>................] - ETA: 15s - loss: 0.6746 - accuracy: 0.5638"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 14s - loss: 0.6745 - accuracy: 0.5639"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "26/49 [==============>...............] - ETA: 14s - loss: 0.6744 - accuracy: 0.5640"
- ]
- },
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "27/49 [===============>..............] - ETA: 13s - loss: 0.6743 - accuracy: 0.5641"
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- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "28/49 [================>.............] - ETA: 12s - loss: 0.6743 - accuracy: 0.5642"
- ]
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- "29/49 [================>.............] - ETA: 12s - loss: 0.6742 - accuracy: 0.5643"
- ]
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- "30/49 [=================>............] - ETA: 11s - loss: 0.6741 - accuracy: 0.5645"
- ]
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- "31/49 [=================>............] - ETA: 11s - loss: 0.6740 - accuracy: 0.5645"
- ]
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- "32/49 [==================>...........] - ETA: 10s - loss: 0.6739 - accuracy: 0.5646"
- ]
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 9s - loss: 0.6738 - accuracy: 0.5647 "
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 9s - loss: 0.6737 - accuracy: 0.5648"
- ]
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "35/49 [====================>.........] - ETA: 8s - loss: 0.6736 - accuracy: 0.5649"
- ]
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- "36/49 [=====================>........] - ETA: 7s - loss: 0.6735 - accuracy: 0.5650"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 7s - loss: 0.6734 - accuracy: 0.5651"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 6s - loss: 0.6733 - accuracy: 0.5653"
- ]
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- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.6731 - accuracy: 0.5654"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 5s - loss: 0.6730 - accuracy: 0.5655"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 4s - loss: 0.6729 - accuracy: 0.5656"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 4s - loss: 0.6728 - accuracy: 0.5657"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 3s - loss: 0.6726 - accuracy: 0.5658"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "44/49 [=========================>....] - ETA: 3s - loss: 0.6725 - accuracy: 0.5659"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "45/49 [==========================>...] - ETA: 2s - loss: 0.6723 - accuracy: 0.5661"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.6722 - accuracy: 0.5662"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.6720 - accuracy: 0.5664"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.6718 - accuracy: 0.5666"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.6716 - accuracy: 0.5668"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 33s 677ms/step - loss: 0.6715 - accuracy: 0.5671 - val_loss: 0.5416 - val_accuracy: 0.7500\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 4/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.5361 - accuracy: 0.7480"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 30s - loss: 0.5282 - accuracy: 0.7505"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 28s - loss: 0.5256 - accuracy: 0.7482"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 27s - loss: 0.5268 - accuracy: 0.7445"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.5253 - accuracy: 0.7439"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.5237 - accuracy: 0.7443"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 25s - loss: 0.5214 - accuracy: 0.7456"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.5189 - accuracy: 0.7475"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 23s - loss: 0.5164 - accuracy: 0.7495"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.5142 - accuracy: 0.7516"
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- "12/49 [======>.......................] - ETA: 22s - loss: 0.5123 - accuracy: 0.7535"
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- "13/49 [======>.......................] - ETA: 22s - loss: 0.5105 - accuracy: 0.7553"
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- "14/49 [=======>......................] - ETA: 21s - loss: 0.5086 - accuracy: 0.7572"
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- "15/49 [========>.....................] - ETA: 20s - loss: 0.5066 - accuracy: 0.7590"
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- "16/49 [========>.....................] - ETA: 20s - loss: 0.5047 - accuracy: 0.7608"
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- "17/49 [=========>....................] - ETA: 19s - loss: 0.5028 - accuracy: 0.7625"
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- "18/49 [==========>...................] - ETA: 18s - loss: 0.5012 - accuracy: 0.7641"
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- "19/49 [==========>...................] - ETA: 18s - loss: 0.4998 - accuracy: 0.7654"
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- "20/49 [===========>..................] - ETA: 17s - loss: 0.4986 - accuracy: 0.7666"
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- "21/49 [===========>..................] - ETA: 17s - loss: 0.4976 - accuracy: 0.7676"
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- "22/49 [============>.................] - ETA: 16s - loss: 0.4965 - accuracy: 0.7687"
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- "23/49 [=============>................] - ETA: 15s - loss: 0.4952 - accuracy: 0.7698"
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- "24/49 [=============>................] - ETA: 15s - loss: 0.4940 - accuracy: 0.7710"
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- "25/49 [==============>...............] - ETA: 14s - loss: 0.4930 - accuracy: 0.7720"
- ]
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- "26/49 [==============>...............] - ETA: 14s - loss: 0.4920 - accuracy: 0.7730"
- ]
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- "27/49 [===============>..............] - ETA: 13s - loss: 0.4910 - accuracy: 0.7740"
- ]
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- "28/49 [================>.............] - ETA: 12s - loss: 0.4900 - accuracy: 0.7750"
- ]
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- "29/49 [================>.............] - ETA: 12s - loss: 0.4891 - accuracy: 0.7759"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "30/49 [=================>............] - ETA: 11s - loss: 0.4881 - accuracy: 0.7768"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 11s - loss: 0.4871 - accuracy: 0.7777"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "32/49 [==================>...........] - ETA: 10s - loss: 0.4861 - accuracy: 0.7786"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 9s - loss: 0.4851 - accuracy: 0.7795 "
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 9s - loss: 0.4841 - accuracy: 0.7804"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "35/49 [====================>.........] - ETA: 8s - loss: 0.4830 - accuracy: 0.7813"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "36/49 [=====================>........] - ETA: 7s - loss: 0.4821 - accuracy: 0.7821"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 7s - loss: 0.4811 - accuracy: 0.7830"
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 6s - loss: 0.4802 - accuracy: 0.7837"
- ]
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- "39/49 [======================>.......] - ETA: 6s - loss: 0.4793 - accuracy: 0.7845"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "45/49 [==========================>...] - ETA: 2s - loss: 0.4746 - accuracy: 0.7885"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.4739 - accuracy: 0.7891"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.4732 - accuracy: 0.7897"
- ]
- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.4725 - accuracy: 0.7903"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.4718 - accuracy: 0.7909"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 33s 680ms/step - loss: 0.4711 - accuracy: 0.7915 - val_loss: 0.4160 - val_accuracy: 0.8304\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 5/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.3674 - accuracy: 0.8633"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 28s - loss: 0.3710 - accuracy: 0.8618"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 28s - loss: 0.3806 - accuracy: 0.8567"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.3868 - accuracy: 0.8536"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 26s - loss: 0.3904 - accuracy: 0.8513"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.3931 - accuracy: 0.8493"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.3941 - accuracy: 0.8484"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 25s - loss: 0.3940 - accuracy: 0.8484"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.3932 - accuracy: 0.8487"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 23s - loss: 0.3920 - accuracy: 0.8492"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.3915 - accuracy: 0.8495"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "12/49 [======>.......................] - ETA: 22s - loss: 0.3910 - accuracy: 0.8498"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 22s - loss: 0.3904 - accuracy: 0.8501"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "14/49 [=======>......................] - ETA: 21s - loss: 0.3899 - accuracy: 0.8505"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "15/49 [========>.....................] - ETA: 20s - loss: 0.3895 - accuracy: 0.8507"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "16/49 [========>.....................] - ETA: 20s - loss: 0.3892 - accuracy: 0.8510"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 19s - loss: 0.3887 - accuracy: 0.8513"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "18/49 [==========>...................] - ETA: 19s - loss: 0.3881 - accuracy: 0.8517"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 18s - loss: 0.3875 - accuracy: 0.8521"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "20/49 [===========>..................] - ETA: 17s - loss: 0.3869 - accuracy: 0.8526"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "21/49 [===========>..................] - ETA: 17s - loss: 0.3865 - accuracy: 0.8530"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "22/49 [============>.................] - ETA: 16s - loss: 0.3861 - accuracy: 0.8533"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "23/49 [=============>................] - ETA: 16s - loss: 0.3857 - accuracy: 0.8537"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "24/49 [=============>................] - ETA: 15s - loss: 0.3853 - accuracy: 0.8540"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 14s - loss: 0.3850 - accuracy: 0.8543"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "26/49 [==============>...............] - ETA: 14s - loss: 0.3847 - accuracy: 0.8545"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "27/49 [===============>..............] - ETA: 13s - loss: 0.3843 - accuracy: 0.8548"
- ]
- },
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- "text": [
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- "28/49 [================>.............] - ETA: 12s - loss: 0.3842 - accuracy: 0.8550"
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- "29/49 [================>.............] - ETA: 12s - loss: 0.3841 - accuracy: 0.8552"
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- "30/49 [=================>............] - ETA: 11s - loss: 0.3840 - accuracy: 0.8553"
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- "31/49 [=================>............] - ETA: 11s - loss: 0.3844 - accuracy: 0.8552"
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- "32/49 [==================>...........] - ETA: 10s - loss: 0.3850 - accuracy: 0.8549"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 9s - loss: 0.3859 - accuracy: 0.8543 "
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- "34/49 [===================>..........] - ETA: 9s - loss: 0.3870 - accuracy: 0.8536"
- ]
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- "35/49 [====================>.........] - ETA: 8s - loss: 0.3882 - accuracy: 0.8527"
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- "36/49 [=====================>........] - ETA: 7s - loss: 0.3894 - accuracy: 0.8517"
- ]
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- "37/49 [=====================>........] - ETA: 7s - loss: 0.3908 - accuracy: 0.8506"
- ]
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- "38/49 [======================>.......] - ETA: 6s - loss: 0.3922 - accuracy: 0.8494"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.3937 - accuracy: 0.8481"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 5s - loss: 0.3952 - accuracy: 0.8467"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 4s - loss: 0.3967 - accuracy: 0.8453"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 4s - loss: 0.3983 - accuracy: 0.8438"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 3s - loss: 0.4000 - accuracy: 0.8423"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "44/49 [=========================>....] - ETA: 3s - loss: 0.4016 - accuracy: 0.8407"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "45/49 [==========================>...] - ETA: 2s - loss: 0.4033 - accuracy: 0.8391"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.4049 - accuracy: 0.8375"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.4066 - accuracy: 0.8358"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.4083 - accuracy: 0.8341"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.4100 - accuracy: 0.8325"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 33s 682ms/step - loss: 0.4116 - accuracy: 0.8309 - val_loss: 0.6712 - val_accuracy: 0.5570\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 6/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 26s - loss: 0.6444 - accuracy: 0.6113"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 29s - loss: 0.6513 - accuracy: 0.5962"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 28s - loss: 0.6526 - accuracy: 0.5904"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.6526 - accuracy: 0.5882"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 26s - loss: 0.6526 - accuracy: 0.5855"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.6525 - accuracy: 0.5838"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.6522 - accuracy: 0.5828"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 25s - loss: 0.6520 - accuracy: 0.5819"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.6520 - accuracy: 0.5813"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 23s - loss: 0.6519 - accuracy: 0.5807"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.6519 - accuracy: 0.5799"
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- },
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- "12/49 [======>.......................] - ETA: 22s - loss: 0.6519 - accuracy: 0.5792"
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- "13/49 [======>.......................] - ETA: 22s - loss: 0.6520 - accuracy: 0.5785"
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- "14/49 [=======>......................] - ETA: 21s - loss: 0.6520 - accuracy: 0.5778"
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- "15/49 [========>.....................] - ETA: 20s - loss: 0.6520 - accuracy: 0.5773"
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- "16/49 [========>.....................] - ETA: 20s - loss: 0.6520 - accuracy: 0.5771"
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- "17/49 [=========>....................] - ETA: 19s - loss: 0.6519 - accuracy: 0.5768"
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- "18/49 [==========>...................] - ETA: 18s - loss: 0.6519 - accuracy: 0.5765"
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- "19/49 [==========>...................] - ETA: 18s - loss: 0.6518 - accuracy: 0.5763"
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- "20/49 [===========>..................] - ETA: 17s - loss: 0.6517 - accuracy: 0.5763"
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- "21/49 [===========>..................] - ETA: 17s - loss: 0.6516 - accuracy: 0.5763"
- ]
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- "22/49 [============>.................] - ETA: 16s - loss: 0.6515 - accuracy: 0.5763"
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- "23/49 [=============>................] - ETA: 15s - loss: 0.6513 - accuracy: 0.5763"
- ]
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- "24/49 [=============>................] - ETA: 15s - loss: 0.6512 - accuracy: 0.5764"
- ]
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- "25/49 [==============>...............] - ETA: 14s - loss: 0.6511 - accuracy: 0.5764"
- ]
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- "26/49 [==============>...............] - ETA: 14s - loss: 0.6509 - accuracy: 0.5765"
- ]
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- "27/49 [===============>..............] - ETA: 13s - loss: 0.6508 - accuracy: 0.5765"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "28/49 [================>.............] - ETA: 12s - loss: 0.6507 - accuracy: 0.5765"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "29/49 [================>.............] - ETA: 12s - loss: 0.6506 - accuracy: 0.5765"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "30/49 [=================>............] - ETA: 11s - loss: 0.6505 - accuracy: 0.5765"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 11s - loss: 0.6504 - accuracy: 0.5765"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "32/49 [==================>...........] - ETA: 10s - loss: 0.6503 - accuracy: 0.5765"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 9s - loss: 0.6502 - accuracy: 0.5765 "
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 9s - loss: 0.6501 - accuracy: 0.5765"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "35/49 [====================>.........] - ETA: 8s - loss: 0.6500 - accuracy: 0.5766"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "36/49 [=====================>........] - ETA: 7s - loss: 0.6499 - accuracy: 0.5767"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "37/49 [=====================>........] - ETA: 7s - loss: 0.6498 - accuracy: 0.5767"
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- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 6s - loss: 0.6497 - accuracy: 0.5768"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.6496 - accuracy: 0.5768"
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- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "45/49 [==========================>...] - ETA: 2s - loss: 0.6490 - accuracy: 0.5770"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.6490 - accuracy: 0.5770"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.6489 - accuracy: 0.5770"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.6488 - accuracy: 0.5771"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.6487 - accuracy: 0.5771"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 33s 679ms/step - loss: 0.6486 - accuracy: 0.5771 - val_loss: 0.6573 - val_accuracy: 0.6009\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 7/10\n"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.6307 - accuracy: 0.5859"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 28s - loss: 0.6368 - accuracy: 0.5845"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 27s - loss: 0.6393 - accuracy: 0.5826"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.6399 - accuracy: 0.5830"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 26s - loss: 0.6396 - accuracy: 0.5850"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.6391 - accuracy: 0.5860"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.6384 - accuracy: 0.5873"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 24s - loss: 0.6381 - accuracy: 0.5883"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.6376 - accuracy: 0.5896"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 23s - loss: 0.6371 - accuracy: 0.5908"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.6368 - accuracy: 0.5919"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "12/49 [======>.......................] - ETA: 22s - loss: 0.6366 - accuracy: 0.5926"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 21s - loss: 0.6363 - accuracy: 0.5934"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "14/49 [=======>......................] - ETA: 21s - loss: 0.6361 - accuracy: 0.5941"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "15/49 [========>.....................] - ETA: 20s - loss: 0.6358 - accuracy: 0.5948"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "16/49 [========>.....................] - ETA: 20s - loss: 0.6354 - accuracy: 0.5954"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 19s - loss: 0.6351 - accuracy: 0.5962"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "18/49 [==========>...................] - ETA: 18s - loss: 0.6347 - accuracy: 0.5968"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 18s - loss: 0.6343 - accuracy: 0.5974"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "20/49 [===========>..................] - ETA: 17s - loss: 0.6340 - accuracy: 0.5980"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "21/49 [===========>..................] - ETA: 17s - loss: 0.6337 - accuracy: 0.5986"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "22/49 [============>.................] - ETA: 16s - loss: 0.6334 - accuracy: 0.5992"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "23/49 [=============>................] - ETA: 15s - loss: 0.6330 - accuracy: 0.5998"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "24/49 [=============>................] - ETA: 15s - loss: 0.6327 - accuracy: 0.6004"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 14s - loss: 0.6325 - accuracy: 0.6009"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "26/49 [==============>...............] - ETA: 13s - loss: 0.6322 - accuracy: 0.6014"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "27/49 [===============>..............] - ETA: 13s - loss: 0.6319 - accuracy: 0.6019"
- ]
- },
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- "text": [
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- "28/49 [================>.............] - ETA: 12s - loss: 0.6316 - accuracy: 0.6025"
- ]
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- "29/49 [================>.............] - ETA: 12s - loss: 0.6314 - accuracy: 0.6031"
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- "30/49 [=================>............] - ETA: 11s - loss: 0.6311 - accuracy: 0.6038"
- ]
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- "31/49 [=================>............] - ETA: 10s - loss: 0.6307 - accuracy: 0.6046"
- ]
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- "32/49 [==================>...........] - ETA: 10s - loss: 0.6303 - accuracy: 0.6056"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 9s - loss: 0.6299 - accuracy: 0.6066 "
- ]
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- "34/49 [===================>..........] - ETA: 9s - loss: 0.6293 - accuracy: 0.6078"
- ]
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- "35/49 [====================>.........] - ETA: 8s - loss: 0.6286 - accuracy: 0.6090"
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- "36/49 [=====================>........] - ETA: 7s - loss: 0.6278 - accuracy: 0.6104"
- ]
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- "37/49 [=====================>........] - ETA: 7s - loss: 0.6268 - accuracy: 0.6118"
- ]
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- "38/49 [======================>.......] - ETA: 6s - loss: 0.6257 - accuracy: 0.6133"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.6246 - accuracy: 0.6148"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 5s - loss: 0.6234 - accuracy: 0.6164"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 4s - loss: 0.6221 - accuracy: 0.6180"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 4s - loss: 0.6208 - accuracy: 0.6196"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 3s - loss: 0.6193 - accuracy: 0.6213"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "44/49 [=========================>....] - ETA: 3s - loss: 0.6179 - accuracy: 0.6230"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "45/49 [==========================>...] - ETA: 2s - loss: 0.6164 - accuracy: 0.6248"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.6148 - accuracy: 0.6265"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.6133 - accuracy: 0.6282"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.6117 - accuracy: 0.6300"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.6101 - accuracy: 0.6317"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 33s 679ms/step - loss: 0.6086 - accuracy: 0.6333 - val_loss: 0.4719 - val_accuracy: 0.8376\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 8/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.3516 - accuracy: 0.8867"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 28s - loss: 0.3432 - accuracy: 0.8877"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.3625 - accuracy: 0.8699"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 26s - loss: 0.3719 - accuracy: 0.8631"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.3794 - accuracy: 0.8579"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.3844 - accuracy: 0.8548"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 24s - loss: 0.3871 - accuracy: 0.8531"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.3884 - accuracy: 0.8520"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 23s - loss: 0.3904 - accuracy: 0.8507"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.3919 - accuracy: 0.8498"
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- "12/49 [======>.......................] - ETA: 22s - loss: 0.3933 - accuracy: 0.8491"
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- "13/49 [======>.......................] - ETA: 21s - loss: 0.3945 - accuracy: 0.8487"
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- "14/49 [=======>......................] - ETA: 21s - loss: 0.3962 - accuracy: 0.8483"
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- "15/49 [========>.....................] - ETA: 20s - loss: 0.3975 - accuracy: 0.8480"
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- "16/49 [========>.....................] - ETA: 20s - loss: 0.3984 - accuracy: 0.8480"
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- "17/49 [=========>....................] - ETA: 19s - loss: 0.3993 - accuracy: 0.8479"
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- "18/49 [==========>...................] - ETA: 18s - loss: 0.4005 - accuracy: 0.8477"
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- "19/49 [==========>...................] - ETA: 18s - loss: 0.4018 - accuracy: 0.8476"
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- "20/49 [===========>..................] - ETA: 17s - loss: 0.4032 - accuracy: 0.8473"
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- "21/49 [===========>..................] - ETA: 17s - loss: 0.4044 - accuracy: 0.8472"
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- "22/49 [============>.................] - ETA: 16s - loss: 0.4057 - accuracy: 0.8471"
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- "23/49 [=============>................] - ETA: 16s - loss: 0.4076 - accuracy: 0.8468"
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- "24/49 [=============>................] - ETA: 15s - loss: 0.4098 - accuracy: 0.8464"
- ]
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- "25/49 [==============>...............] - ETA: 14s - loss: 0.4126 - accuracy: 0.8458"
- ]
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- "26/49 [==============>...............] - ETA: 14s - loss: 0.4158 - accuracy: 0.8451"
- ]
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- "27/49 [===============>..............] - ETA: 13s - loss: 0.4192 - accuracy: 0.8442"
- ]
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- "28/49 [================>.............] - ETA: 12s - loss: 0.4227 - accuracy: 0.8432"
- ]
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- "29/49 [================>.............] - ETA: 12s - loss: 0.4261 - accuracy: 0.8422"
- ]
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- "30/49 [=================>............] - ETA: 11s - loss: 0.4295 - accuracy: 0.8410"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 11s - loss: 0.4326 - accuracy: 0.8398"
- ]
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- "32/49 [==================>...........] - ETA: 10s - loss: 0.4356 - accuracy: 0.8386"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 9s - loss: 0.4384 - accuracy: 0.8374 "
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 9s - loss: 0.4411 - accuracy: 0.8362"
- ]
- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "35/49 [====================>.........] - ETA: 8s - loss: 0.4437 - accuracy: 0.8348"
- ]
- },
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- "36/49 [=====================>........] - ETA: 8s - loss: 0.4463 - accuracy: 0.8333"
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- },
- {
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "39/49 [======================>.......] - ETA: 6s - loss: 0.4532 - accuracy: 0.8287"
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "45/49 [==========================>...] - ETA: 2s - loss: 0.4642 - accuracy: 0.8210"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.4656 - accuracy: 0.8200"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.4669 - accuracy: 0.8191"
- ]
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.4681 - accuracy: 0.8183"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.4693 - accuracy: 0.8175"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 33s 681ms/step - loss: 0.4704 - accuracy: 0.8167 - val_loss: 0.4638 - val_accuracy: 0.8236\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 9/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 31s - loss: 0.4077 - accuracy: 0.8496"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 28s - loss: 0.4098 - accuracy: 0.8501"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 28s - loss: 0.4067 - accuracy: 0.8541"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 27s - loss: 0.4052 - accuracy: 0.8551"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 26s - loss: 0.4047 - accuracy: 0.8558"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.4049 - accuracy: 0.8562"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.4043 - accuracy: 0.8571"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 24s - loss: 0.4039 - accuracy: 0.8575"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.4042 - accuracy: 0.8575"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 23s - loss: 0.4042 - accuracy: 0.8576"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.4042 - accuracy: 0.8577"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "12/49 [======>.......................] - ETA: 22s - loss: 0.4043 - accuracy: 0.8577"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "13/49 [======>.......................] - ETA: 21s - loss: 0.4043 - accuracy: 0.8578"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "14/49 [=======>......................] - ETA: 21s - loss: 0.4053 - accuracy: 0.8570"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "15/49 [========>.....................] - ETA: 20s - loss: 0.4075 - accuracy: 0.8554"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "16/49 [========>.....................] - ETA: 20s - loss: 0.4103 - accuracy: 0.8531"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "17/49 [=========>....................] - ETA: 19s - loss: 0.4138 - accuracy: 0.8504"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "18/49 [==========>...................] - ETA: 18s - loss: 0.4177 - accuracy: 0.8474"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "19/49 [==========>...................] - ETA: 18s - loss: 0.4218 - accuracy: 0.8441"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "20/49 [===========>..................] - ETA: 17s - loss: 0.4261 - accuracy: 0.8406"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "21/49 [===========>..................] - ETA: 17s - loss: 0.4304 - accuracy: 0.8370"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "22/49 [============>.................] - ETA: 16s - loss: 0.4347 - accuracy: 0.8335"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "23/49 [=============>................] - ETA: 15s - loss: 0.4389 - accuracy: 0.8299"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "24/49 [=============>................] - ETA: 15s - loss: 0.4431 - accuracy: 0.8262"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "25/49 [==============>...............] - ETA: 14s - loss: 0.4472 - accuracy: 0.8226"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "26/49 [==============>...............] - ETA: 14s - loss: 0.4511 - accuracy: 0.8189"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "27/49 [===============>..............] - ETA: 13s - loss: 0.4549 - accuracy: 0.8154"
- ]
- },
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- "text": [
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- "28/49 [================>.............] - ETA: 12s - loss: 0.4585 - accuracy: 0.8119"
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- "29/49 [================>.............] - ETA: 12s - loss: 0.4620 - accuracy: 0.8085"
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- "30/49 [=================>............] - ETA: 11s - loss: 0.4654 - accuracy: 0.8052"
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- "31/49 [=================>............] - ETA: 10s - loss: 0.4686 - accuracy: 0.8020"
- ]
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- "32/49 [==================>...........] - ETA: 10s - loss: 0.4717 - accuracy: 0.7988"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 9s - loss: 0.4747 - accuracy: 0.7957 "
- ]
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- "34/49 [===================>..........] - ETA: 9s - loss: 0.4776 - accuracy: 0.7926"
- ]
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- "35/49 [====================>.........] - ETA: 8s - loss: 0.4803 - accuracy: 0.7897"
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- "36/49 [=====================>........] - ETA: 7s - loss: 0.4830 - accuracy: 0.7868"
- ]
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- "37/49 [=====================>........] - ETA: 7s - loss: 0.4855 - accuracy: 0.7840"
- ]
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- "38/49 [======================>.......] - ETA: 6s - loss: 0.4880 - accuracy: 0.7813"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.4904 - accuracy: 0.7786"
- ]
- },
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- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "40/49 [=======================>......] - ETA: 5s - loss: 0.4927 - accuracy: 0.7761"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "41/49 [========================>.....] - ETA: 4s - loss: 0.4949 - accuracy: 0.7736"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "42/49 [========================>.....] - ETA: 4s - loss: 0.4971 - accuracy: 0.7712"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "43/49 [=========================>....] - ETA: 3s - loss: 0.4991 - accuracy: 0.7689"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "44/49 [=========================>....] - ETA: 3s - loss: 0.5011 - accuracy: 0.7667"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "45/49 [==========================>...] - ETA: 2s - loss: 0.5031 - accuracy: 0.7645"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.5050 - accuracy: 0.7624"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.5068 - accuracy: 0.7604"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.5086 - accuracy: 0.7583"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.5103 - accuracy: 0.7564"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 33s 679ms/step - loss: 0.5120 - accuracy: 0.7545 - val_loss: 0.6641 - val_accuracy: 0.5543\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 10/10\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- " 1/49 [..............................] - ETA: 29s - loss: 0.6432 - accuracy: 0.5918"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 2/49 [>.............................] - ETA: 28s - loss: 0.6417 - accuracy: 0.5894"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 3/49 [>.............................] - ETA: 28s - loss: 0.6407 - accuracy: 0.5913"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 4/49 [=>............................] - ETA: 28s - loss: 0.6400 - accuracy: 0.5918"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 5/49 [==>...........................] - ETA: 27s - loss: 0.6403 - accuracy: 0.5904"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 6/49 [==>...........................] - ETA: 26s - loss: 0.6404 - accuracy: 0.5893"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 7/49 [===>..........................] - ETA: 25s - loss: 0.6405 - accuracy: 0.5883"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 8/49 [===>..........................] - ETA: 25s - loss: 0.6406 - accuracy: 0.5873"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- " 9/49 [====>.........................] - ETA: 24s - loss: 0.6407 - accuracy: 0.5861"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "10/49 [=====>........................] - ETA: 23s - loss: 0.6409 - accuracy: 0.5851"
- ]
- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "11/49 [=====>........................] - ETA: 23s - loss: 0.6409 - accuracy: 0.5846"
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- "12/49 [======>.......................] - ETA: 22s - loss: 0.6409 - accuracy: 0.5842"
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- "13/49 [======>.......................] - ETA: 22s - loss: 0.6408 - accuracy: 0.5841"
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- "14/49 [=======>......................] - ETA: 21s - loss: 0.6406 - accuracy: 0.5843"
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- "15/49 [========>.....................] - ETA: 21s - loss: 0.6405 - accuracy: 0.5844"
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- "16/49 [========>.....................] - ETA: 20s - loss: 0.6403 - accuracy: 0.5847"
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- "17/49 [=========>....................] - ETA: 19s - loss: 0.6401 - accuracy: 0.5849"
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- "18/49 [==========>...................] - ETA: 19s - loss: 0.6399 - accuracy: 0.5852"
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- "19/49 [==========>...................] - ETA: 18s - loss: 0.6397 - accuracy: 0.5854"
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- "20/49 [===========>..................] - ETA: 17s - loss: 0.6396 - accuracy: 0.5857"
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- "21/49 [===========>..................] - ETA: 17s - loss: 0.6395 - accuracy: 0.5859"
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- "22/49 [============>.................] - ETA: 16s - loss: 0.6393 - accuracy: 0.5861"
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- "23/49 [=============>................] - ETA: 16s - loss: 0.6392 - accuracy: 0.5863"
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- "24/49 [=============>................] - ETA: 15s - loss: 0.6392 - accuracy: 0.5865"
- ]
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- "25/49 [==============>...............] - ETA: 14s - loss: 0.6391 - accuracy: 0.5867"
- ]
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- "26/49 [==============>...............] - ETA: 14s - loss: 0.6390 - accuracy: 0.5868"
- ]
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- "27/49 [===============>..............] - ETA: 13s - loss: 0.6388 - accuracy: 0.5870"
- ]
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- "28/49 [================>.............] - ETA: 13s - loss: 0.6387 - accuracy: 0.5871"
- ]
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- "29/49 [================>.............] - ETA: 12s - loss: 0.6387 - accuracy: 0.5873"
- ]
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "30/49 [=================>............] - ETA: 11s - loss: 0.6386 - accuracy: 0.5874"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "31/49 [=================>............] - ETA: 11s - loss: 0.6385 - accuracy: 0.5875"
- ]
- },
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- "32/49 [==================>...........] - ETA: 10s - loss: 0.6384 - accuracy: 0.5876"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "33/49 [===================>..........] - ETA: 9s - loss: 0.6384 - accuracy: 0.5877 "
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "34/49 [===================>..........] - ETA: 9s - loss: 0.6383 - accuracy: 0.5878"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "35/49 [====================>.........] - ETA: 8s - loss: 0.6382 - accuracy: 0.5879"
- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "36/49 [=====================>........] - ETA: 8s - loss: 0.6382 - accuracy: 0.5880"
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- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- ]
- },
- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "38/49 [======================>.......] - ETA: 6s - loss: 0.6380 - accuracy: 0.5881"
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- {
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "39/49 [======================>.......] - ETA: 6s - loss: 0.6380 - accuracy: 0.5882"
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- "output_type": "stream",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- },
- {
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- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "45/49 [==========================>...] - ETA: 2s - loss: 0.6376 - accuracy: 0.5888"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "46/49 [===========================>..] - ETA: 1s - loss: 0.6376 - accuracy: 0.5889"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "47/49 [===========================>..] - ETA: 1s - loss: 0.6375 - accuracy: 0.5890"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "48/49 [============================>.] - ETA: 0s - loss: 0.6374 - accuracy: 0.5891"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - ETA: 0s - loss: 0.6374 - accuracy: 0.5892"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
- "49/49 [==============================] - 33s 684ms/step - loss: 0.6373 - accuracy: 0.5893 - val_loss: 0.6557 - val_accuracy: 0.5687\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 10min 56s, sys: 44.7 s, total: 11min 41s\n",
- "Wall time: 5min 39s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "\n",
- "n_epochs = 10\n",
- "batch_size = 512\n",
- "\n",
- "history = model.fit(x_train,\n",
- " y_train,\n",
- " epochs = n_epochs,\n",
- " batch_size = batch_size,\n",
- " validation_data = (x_test, y_test),\n",
- " verbose = 1,\n",
- " callbacks = [savemodel_callback])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Step 6 - Evaluate\n",
- "### 6.1 - Training history"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:53:37.431774Z",
- "iopub.status.busy": "2021-02-10T09:53:37.430277Z",
- "iopub.status.idle": "2021-02-10T09:53:38.622268Z",
- "shell.execute_reply": "2021-02-10T09:53:38.622772Z"
- }
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/IMDB3-02-history_0</div>"
- ],
- "text/plain": [
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\n",
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3ctVVV/GlL3+ZM119NHf0hnZCDQTgv/+yj1///TDXnlfFO9bPoSBXfhyFECIdycxEhrnuuuv41a9+xWOPPcZ73vMe2traKC8vJycnh2effZajR4+O+DUvueQSHnroIQD279/PsWPHWLRoEYcbT1E1exa33vhh3vHGK9mxYwf19fUUFhbywQ9+kM9+9rNs3boVj1JMLMpjbnkJ5RMKImYq2rr6+NFf9/Hh7/2VX750kM7e/sGGIYQQwqXko2CGWbZsGe3t7VRWVlJRUcE//MM/8Pa3v51169axevVqFi9ePOLX9Hq9fPzjH2fFihVkZ2fzk5/8hLy8PB558nf84qc/JSc7m+lTp/KVu+9m05at3HHHHXg8HnJycvjBD34Qep1gUFFamEvriRymTSigsc3sNnamu5+fPPs6//vKYd597jyu2TCXorwh+mEIIYRwDSWFcCPj9Xo1gM/nizi/d+9elixZ4siYHFV/FPp6zf2pFVBUEtfT9u7dy/yFi/jzjlp++dJBGlu7Ix4vzs/mXedW8a4NcynKl6BCCCFcYNDKfZmZEGNTUBQOJro74w4mAHKyPLxlzWzeuHImf9lZxy9fOkhDSxcAHT0D/Pz5/Tz+ymHede483nXuPIolqHC/3h748begpQlWrIN1F8P0WU6PSgiRZBJMjHM7d+4MrdQIysvL49VXX43vBQqLoK3Z3O/uNO24R9hGOzvLw5tWz+KKFZX8dVcdD78YDio6ewf4xQsH+M2rNbxzgwkqSgokqHCtzS/C5hfM/UN74P9+BpVzTVCx7hKokMBCiEwkwcQ4t2LFCrZt2zb6F8jNh6wsa4t0v5mlyMsf1UtlZ3m4apUVVOys55cvHaSuuRMwQcVDL5qg4poNc3n3ufMoLRxi23bhjOOHzz5Xd8TcfvtzCSyEyFASTCSQ1npEzaAyglIm1dFxxhx3dQwbTAxXp5Pl8fDGVTN5w4oZPLernodfOkjtaRNUdPUN8MuXDvJ/G2t4x/q5XHteFRMkqHCPBttqodnzoeEY9PeFz8UKLNZeDDNmI4RIXxJMJEh+fj6nT5+mrKxs/AUU9mCiuxMmTRn0Uq01p0+fJj9/+NmLLI+HK1bO5LLllTy/u56HXzzAcSuo6O7z88jLh/jtxiNWUDGPiUV5Cfl2xBjUHwvfv/nzMLEMdmw06Y+dmwYPLGbMsWYsLjb3hRBpRVZzjNBgqzn6+/upra0dcUOojKAD0NwEWD9Lk6aAJ2vQy/Pz85k5cyY5Q22FHoM/oHlxTwMPvXiAY00dka+Zk8Xb183hPedXSVDhlO5OuOVacz8rG3y/NSmwoJ5u2PFq7MDCTgILIdxq0E/KEkyM0GDBxLh3zxdg72vm/oduhUuvTtpbBXQ4qDh6KjKoyMvJ4m1rZ/Pe86uZVCxBRUod3gdfv83cnzEH/vX+wa/t6TYzFluswCK4IijajNkmDbLuYpMWEUI4SZaGiiRbdW44mNj+alKDCY9SXLpsBhcvreDlvSd46MUD1JxsB6C338//vlLD7zYf5a1r5/DeC6qYXDy6glAxQvYUx3A1EPkFsOFSc+vpNgHF5hfODizqj0H9Q/DkQxJYCGdobVasFZVAjtRnDUaCCZEYKzfAr35o7u99zfQbGOWqjnh5lOLipRVcuGQ6f9t3godePMjhRlO70TsQ4PFXa/jdlqNcfc5s3ndBNWUlElQklb34smIEBZX5BbD+EnPr7bFqLIYJLCpmR6ZCxludkkid//5PePVZk7KbMQfmzIc5C8xt5jzIlRlQkGBCJEr5DNOc6MRxkwt/fTusPDclb+1RiouWVHDh4un8fX8jD71wgIMnTFDRNxDg/zYe4fdbjoWCiimlElQkRcTMxChrHfLyIwOLnRthU4zAouGYCSqefMj83K2/RAILkXgtTSaQALP0/fhhc3vpaXPO47ECjAVWkDEfZlYl/YOUG0kwIRJn1QYTTABs35iyYCJIKcUFi6Zz/sJpvHrgJL944QAHGtoA6PcH+O2mIzyzo5bvffRCZk0pTunYxoWG4+H7iVjqmZdv+lGsswUWm180Mxf2wOLE8cjAYp0tFSKBhRiLI/uHfjwQgNoac3vZFmBUzI6cwZiV+QGGBBMicVaeC3/6X3N/x6ugP+XIL3OlFOctnMa5C8rZeNAEFfvrTVDR1TvAi3sb+MDFC1I+rozW2wOnG819j8fMVCXSWYHFJiuwePXswOJ3D5vb9JnhBlkSWIjRqHk9fP+yt5mfp6MH4egBc2usO/s5gUB42fPf/mzOKY9p0haawVgAs6szKsCQYEIkTvVSKCw2jatamky0PqvKseEopTh3wTQ2zC/nly8d5KfPmU8ZwboKkUAnjptCNTCBRDIL1fLyw7MPvT2wa7NJhZwVWNTC735pbsHAYu3FJs8tgYWIR41tZmLRSli8ytyCujvh2CEruDgYDjCiV0nqgNkUsf4o/N0eYMwMz17MmQ+zqk0NURqSYEIkTnY2LF8LG583x9tfdTSYCFJKsX5+eSiYOCTBROLZ6yVGUnw5Vnn5sPYicwsGFsEZi15bzxd7YDGtMjxjIYGFGEwgEJnmmLfw7GsKikyQsWhl+FxPlwkwjhwIBxmNtYMEGMfM7e9/MeeUMqk6e4pkdnoEGBJMiMRaeW44mNjxKrztemfHY5kztRiPUgS0pr65i67eAQrz5Mc/YRpGsCw0WWIFFlteNEGtPbBorIPf/8rcJLAQgzlZb2YeAIonQNm0+J6XXwgLV5hbUE+XKdy0p0gaak1AYae1+X+p4Ri88ldzTimYNjMcYMy1ajAKisb+PSaQ/DYVibV8nZm+0wGTbzzTCqUTnR4VudlZzJ5SzJFTph9FzckzLJs12eFRZZBErORIJHtg0ddraiyGCyxmzIabPm8+CQphn5WYu3BsgWZ+ISxYbm5BvT1np0gajscOME4cN7fgyhKlTDrRniKZM9/RAEOCCZFYxaUwfwkc2G3+J9i5CS58o9OjAqB6emkomDh0QoKJhGpwKM0Rj9y8yMAimArZ/ir0doevqz8GT/wcPnWXY0MVLmIvvoyV4hirvHxYsMzcgnp7rBkMW4qk/ljsAKOxztw2Phc+P60yKkUyHwpTE2BIMCESb+W5JpgAk+pwSTBRNa2Uv+w01ddSN5FA/X1wssHcV8oUO7pVbh6cc6G59fXC7i0mLbfJSs0d2G1y5R6Ps+MUzhuuXiIZ8vJh/lJzC+rtgVorRRKsw2g4Zn5Oo4UCjOfD577wbahekvShSzAhEm/lBvjfH5n7u7fCQD9kj2xTr2Sonl4aui8rOhLohC33O2V6+nQEzM2DNRfA6vNN19aOM9DZbn4ZV8xyenTCSQMDJgURNHeRc2PJyzcr5aptAUZfr1ktFzGDcdQ01oqWohomx4MJpZQH+DTwT8Bc4BTwKPAVrXVnnK+RDXiBG4BFwABwCLhfa32/7brLgGcHeZnfa63fNprvQUSZMQemTIOmRlN4dGAXLFnj9KiomhYOJo6cbMcfCJAln0DHzg3Fl2OhFFQtMbNoAIf2SjAx3tUfCe9qW1buirqvCLl5ULXY3IL6++B4VIAR8KesjsLxYAL4NnAr8BvgHmCJdbxGKXWl1tHJokhKqVzgCeBy4CHgh5jvawEwWCXYA8CLUedqR/sNiChKmVTHX58wx9s3uiKYmFCYy5SSfJrae+gbCFB7upM5U0ucHlb6c2pZaCLNtwcTe+Ciq5wdj3BWTVTxZTrIyYWqReYWFCsVkiSOBhNKqWXALcDjWutrbedrgO8B1wEPD/MyXwauBN6otR5s1iHa37XWvxjFkEW8Vm6wBROvwPtvdsWyu6rppTS1m2r+QyfOSDCRCG4uvoyXfQr50B7nxiHcIaL40sEUx1ilcObV6Tne6zH7o38n6vyDQBfwwaGerJQqwqRIfqu1flYZcf11UEoVKaUyp5ep2yxaGW4Ve6rBNG1xgeppUjeRcCPZetyt5i4M/+KtP2a6uIrxK3pZqBiW08HEeiAAbLSf1Fr3ANusx4dyMVACbFFKfRc4A5xRSp1SSn3dqqWI5btAB9CtlNqvlPq0Ui742JxJcnJh6Tnh4+0bB782hezBhKzoSICBAThp258gXWsN8vJNK+Ogw/ucG4twVm8P1B0195UyTaLEsJwOJmYATVrr3hiP1QFTrJqIwQTnn24DrgU+B7wf+BvwBeB/oq7vx9RXfA54B/BxoBUzM/KjoQaqlLpZKbV5qGtElFW2XUOD+WiHVdlWdBw6cQYd3eJWjMzJ+nAF+eRy05wnXdmXzx2UVMe4dexgeHVSxaz0/plOIaeDiUIgViAB0GO7ZjDBlMZk4Eqt9Q+01o9qra8BngM+rJQKJUO11i9rra/RWt+vtX7SWulxHvAn4Aal1EWDvZHW+gGt9br4vi0BwArbxNKBXa6YOq6YVEhBbhYAbV19NHcM9uMn4pLuKzns5kvdhCCyXsLJJaFpxulgogsYbFF6vu2awQTb172itY6el/yZ9fXSoQZgrRa52zq8eqhrxQhNmBzONwYCsGuLs+MBPEoxr1zqJhKm/mj4froWXwbZZyYOv26W1Ynxx4lmVRnA6WCiHpPKiBVQVGJSIH1DPD9Y1XcixmNWSz4mxTGOI9bXKXFcK0bChamO6qhUhxiDTJqZmFwOE8vM/d7ucN5cjC/puCzUBZwOJjZZY9hgP2mtslgNDFejEKzqi9W/N3juZBzjCFbYNMZxrRiJlbb/tDs3ueLTXpUUYSZOJqzkAHYfb+ZP22sJ2JsASd3E+NNxxqw+A9O1d+Y8Z8eTRpwOJh4BNKaA0u5jmFqJh4InlFIVSqnFSqlQDYXWugZ4GdiglDrHdm2W9RoDwNO282XRA7BmRe6yDp8c27cjzjJ7fvjTXmc7HHK+Sj6irbbMTIye329aaQelaZrjyMl2PvvTV/jWkzt4xW/7FXF4r3ODEs6wpzhmzjOr0kRcHA0mtNY7gfuAdyulHldK3aSUugf4FvA8kQ2r7gb2EjWLgWl61QX8WSl1l1LqFuu5G4Cva61tH514Sin1W6XUV6z3+gqwE7gQuFdr7Y71i5lEqcjZCRekOuZOLcFjLQSua+6kp2/A2QGlq6YTZt8VMAFjYbGz4xmll/Y2ELBW9fz2tK3eW2Ymxp9MaVblAKdnJsDMSnwWWIYJLK4D7gXeNlwrbQCt9WvABcBL1mt9EygCbtRafzXq8seAckwA8gPgM5glqB/QWt+agO9FxBIRTDgfr+XlZDGzzPzh00DNyXZnB5SuMqGNNrC1pil0f7dnMv4sqz3NqQY40+rMoIQzpFnVqDm+N4fW2o/Zk+OeYa67AbORV6zHdmD6Rgz3Xt8AvjHiQYqxWbLG5B8H+qHuiPlEO2W6o0OqmlbKsSazVPVQ4xmWzIynTldEaLAVKKZpvURnTz97a1tDx/0qi2NFFcw7c9ycOLQX1pzvzOBEamkdWXwpKzlGxA0zEyLT5eXD4tXhYxfMTsiKjgTIgJmJ7UdPh1IcQVvsi7qk38T40dIEZ1rM/fxCmB6rrl8MRoIJkRqr3JXqkD06EiADVnJsPdx01rndeRXhAwkmxg97vcSc+eDJcm4saUiCCZEa9rqJfduhp3vwa1PAvjy0pvEM/oC01R6RQABOHA8fp+nMhD2YuPoc8z3szbOl4Gr2h4tMRWaLSHFI8eVISTAhUqNsGlTONfcH+mHvNidHw6TiPCYXm15pvQMB6ps7HR1P2mk+CX1WK/KSCeaWZk60dlFn/XfPy/Zw0xWLKc7PpiWriIZsK9gc6Idjhx0cpUgZKb4cEwkmROrYu2Fuf8W5cVikedUYZEC9hH1WYvmcMoryc7h8eSUAeyTVMb4EAnBUZibGQoIJkTorbcHEzo3mf2AHRdRNSBHmyES00Z7j3DjGwB5MrK0yRZdvXGWK7vbYUx0STGS+xjrotraBKpkIk6c6Opx0JMGESJ2qRVBs/QFvazFb/To5nOkyMzFqaV586Q9oth0JBxPnzDPBxMKKCcyZWhw1MyGdMDPeEXuzqoWm2V4a6+n343tqN2e6htraKrEkmBCp48mK3JZ8u7PdMGVFxxg0pHea4+CJNtq7TWHl5OI85paXAKCU4qpVsziSU0a3yjEXtzRB8ymnhipSIYOKL3v6/Xz1kU38dtMR/uUXr6YsoJBgQqSWPdXh8BLRGZOLyMsxy7+aO3pp6eh1dDxpQ+u0n5mwpzjWzJuCsn0SfcOKGeDJYl/etPATpLV2ZsuQ4suefj9f/dUmttWcBsyM68uvx9pUO/EkmBCptewcyLLWbx89AK2nHRtKlkdRZX0iBZmdiFtLE/RY+eXCYihNv+6hWw+HZxrOqZoS8djk4nw2zJ8qRZjjxUA/HDsUPk7TYCIUSBwJ/079yGULecua1AT7EkyI1CoshgXLw8cOz07MkxUdI9cQNSuRZvnl7r4B9hxvCR2vmTflrGuuWj0rot+ElrqJzFV7JNxLZMr0tFzm3NM3wFeiAokbL1/EBy5ekLIxSDAhUs9FG39JW+1RqE/vlRw7jzYzYDUpm1deQllJ/lnXbFhQTt3EWeETxw5Bb0+qhihSKc1THD19A3z5V5vYbgskPvqGRVx30fyUjkOCCZF69rqJPVuhP3UVx9GkCHMU0rz4costxbGm6uxZCYCcLA8bVlVzNMekcFTAb9JyIvOk8bbjwUBix9Hm0LmPvmEx778wtYEESDAhnDB9JkwzzYHo64XXdzg2lHnlJQQn6WtPd9DT73dsLGmjPr13C43sLzF4P4GrVs2KqJvofX1XUsclHHIkPXcK7e4b4Eu/jAwk/vGKxbz/wmpHxiPBhHCGPdXh4BLR/NxsKsuKAAhoOHKy3bGxpIXolRxpNjNx6kx3aOv5nCwPy2dPHvTa6umlnJ46L3Tcsn1bsocnUq2nO/zzrDwwO/Wf6EcjGEjsPBYOJG66YjHvu8CZQAIkmBBOiVgi+qr5I+UQSXWMwJkW6DJ/jMkrgEmx0wRu9VpNeFZi2exJ5OcMvTPk9HVrQ/eLaw84+nMqkuDYQdBWJ94ZsyG/wNnxxCEYSOyyBRIfu3IJ73UwkAAJJoRTFiyDgkJz//TJyKnzFKuSYCJ+0f0l0mwlhz3Fcc684Vsmr7/wHNo9ZkO44oEu6vdJ3URGqUmvFEdX7wBffHhjRCBx8xuX8J7zqxwclSHBhHBGdg4sC3/qczLVISs6RiCNiy8DWkcGE4MUX9pNKM7nxOTwipX9Lzm/QZ1IIHvx5Vx3F1929Q7wpV9uZLdtWfM/vXEJ157nfCABEkwIJ0WnOhwSPTMRkKnswUX3mEgjNY1naLNaC08ozI0IIoeSvzjcF2Vg/278Dm9QJxIoTZaFdvb288WHowKJq5bybpcEEiDBhHDSinXhafJD+6C9zZFhTC7OY2JRLmC6yDU0dzkyjrSQxm20t0S10PbEmaKZsWFd6H5VRy1bDjUNcbVIG+2t0GS1ms7OgZlznRzNoIKBxJ7acCDx8auW8u5z5w3xrNSTYEI4p2QiVC0293UAdm12ZBhKqYgiTOmEOYSIlRzp1bBqpCmOoKyqJQSswGNu/2me2yJ1ExnhiO2/4+xqE1C4TGdPP198aCN7a1tD5z7xpqW8y2WBBEgwIZy2yh0bf0kRZhza28ynOYDcPCgrd3Q4I9Hb748oWhtJMEF+AQPTTeDkAc7s2pnSrZ1FkkTUS7gvxdHZ08+dD29kb11r6Jz3TUt55wb3BRIgwYRwmr1uYtdmGBhwZBhVMjMxvIbj4fvTZ4EnfX597DrWTL/f1DrMKitiaunIlgDm2uomFnbX8+zu+oSOTzjAxc2qgoHEPnsg8eZlXOPSQAIkmBBOq5wLk61PuN2dcHC3I8OwF+MdlhUdsTWkb+fLrbb+Emurh18SepbqpaG7S3tP8PS240NcLFxP68hloS5aydHR088XHooMJD755mVcs36uY2OKhwQTwllKwSrnN/6aWVZEbrb536GpvYfWzl5HxuFq6Vx8eci2H0eMXUKHVb0kdHdJ7wkONbRJOiydNZ8Mp+wKCsPt/R1mAolXeb2+NXTuU29ZzjtcHkiABBPCDVywRDTL42FueUno+HCjtNU+S5oWX7Z09FJjtUnP9ihWzS0b+YtMmQ6lZtOvIt3H7P5mntlem8hhilSyz0rMWeiKlF17dz9f+MWr7K8Pr2q75erlvH1devy/5vy/oBCLV5mCPoATtdBY58gwIld0OLNM1dXStMeEvYX2kpmTKMjNHvmLKBUxO7G0t4G/7KxjwC89J9JSxE6hztdLtHebGYn9DeHfO7devZy3rU2PQAIkmBBukJMLS9eEjx1KddjrJmpkZiJSVwe0njb3s3PMJ/U0Yd9yfESrOKJFBBMnaOvqY+OBk2MZmnCKi5pVBQOJA7ZA4tNvXcFb0yiQAAkmhFu4INURsaJDijAj2WclplVC1tAbZLmFPquF9iiKL4NsRZhLehsAeFpSHekn4IejB8PH85wrvjzT3ce//OKVswKJq89Jn5m/IAkmhDusWB++v3+nWdmRYvPKw8HEsaYO+gb8KR+Da0UUX6bPJ6ajpzpo7jDFtMX5OSyomDD6F5u7ALJMimTWQCul/m42Hjwpxbrp5kQt9FhdbidMcmzn2zPdfXzhF69y0PbB5ba3pWcgARJMCLeYNAVmzzf3/X7YvTXlQyjMy2bGZLOTaUBrjp7qSPkYXMveYyKN6iW2Hrav4igjyzOGXU5zcmHO/NDhkt4T+AOav+x0psZHjFJNVIrDgZ1vz3T18S8/DwcSCrj9bSt4y5r0+X8rmgQTwj1WOZ/qiCjCPCFFmCERKznS5xfelkSlOIKiijABnt5Wi5bN4dLHEXvxZepTHGe6+viXX7waao6ngNvfvpI3p3EgARJMCDdZaes3sXOTyW2mmHTCHEQaNqzqG/Cz8+jp0PE5o+kvEc1WN7Gsz2wSdeRUe8RUtXC56JmJFGrr6uPzMQKJN62eldJxJIMEE8I95iwIreWnvS1y+VaKRHTClBUdRk83nLZWLWRlQfkMZ8cTpz21LfQOmKWbMyYXMn1S4dhfdH44mFjcd5IsbQLeP0lHzPTQ3wfHD4ePU9j5sq2rj8///JVQszMF/PM7VmVEIAESTAg38Xhgpa0Qc3vql4hGbPh14gwBmb6GE7Y/lOWVrtxdMZattq3CEzIrATCxLNT+PSfQz7w+M/Px7K56KdhNB7U14Lf2/5laAcUlQ1+fIK2dvXz+56+EmqcFA4k3rpqZkvdPBQkmhLs4vER0Skk+pQXmj2VX3wCNrd0pH4PrpGkb7Yj9OBJRLxE0P1w3cZ7HvEdHTz+v7JeeE67nwOZerZ29/MsvXs3oQAIkmBBus3RN+JNvbU14ej1FlFJUTZcizAhpWHzZ1tXHQWvtvkeNsoX2YGx1ExfltoTuP71dUh2uF7HtePJTHGZGIhxIeBTccU3mBRIgwYRwm/xCWLQifOxAN8xqKcKMlIbFl6/VNBFMUC2unEhRfgJTM7ZgYnZbOIDYcugUp9t7Evc+IvFSODPR2tnL537+CkdO2QOJ1VyxMvMCCZBgQriRw6kOezAhRZik5czEaxFLQhPclGjmvNBeMlktp7i4wgQqAQ1/3iE9J1yrpyvcL8XjCfe1SYKWjl7u+NkroV41wUDiDSvcsTtpMkgwIdzHvkR07zboTe2nverp4S6J436b6b5eaDJLIFEemO7+T1Va68TtxxFLdnbEksK3T+oK3X96+3HpOeFWRw5A8L/NjDmQl5+Ut2npMDMSx5rGTyABLggmlFIepdTtSql9SqkepdRxpdQ9SqmiEbxGtlLqVqXUVqVUp1Kqzbr/TzGunaCUulcpVWe9326l1CeUcqANmohtakV4On2g3wQUKTSzrIicLPO/xsm2bs5096X0/V3lRG34F/DU6aYLpMvVnu7k1BkTgBbmZbNoxsTEv4ltieiy3gYKrZ1Ia093sq+uNfHvJ8YuIsWRnHqJ5o6eswKJz70z8wMJcEEwAXwb+BawB7gF+DVwK/CkUmrY8SmlcoHfAd8EtgG3A18AngfmxLj2GeDjwCPW+70O+ICvJuS7EYnhYKojO8vDnKnFoeNxPTuRhtuO21tor5pTRnZWEn7NVYVXdGQfeZ1LllaEjmXzL5eKKL5MfL1Ec0cPn/uZPZBQfP5da7h8eeYHEuBwMKGUWob5g/641vrdWusHtdafAT4DXA5cF8fLfBm4Eniz1vqjWusHtNY+rfXtWus7o669CVgPfEZr/Rnr/d4NPA7cqZRKnx2MMl1EMLEx/Ok4RSKaV43n7ob1tuLLNKmXsO8SurY6SZs42dpqc/QgVy0rDx0+t7uenn7pOeE6SSy+PN1uAonjp80GhR6l+Jd3reayZenR4C0RnJ6ZuB6z7PY7UecfBLqADw71ZCsV8mngt1rrZ5UxVBeSD1iv+2DU+e8AOcD74x65SK7qJVBozQ60nobjh1L79rKiw0izDb4G/AG2R7TQTmB/CbuSCWYrdgD/AEv9TVRONpnZrt4B/rbvRHLeV4zOmdbwMvOcXJgxN2EvfbrdpDbsgcQX3r2GS8dRIAHOBxPrgQAQsf5Pa92DSVmsj/Ecu4uBEmCLUuq7wBngjFLqlFLq60qp7OCFVsrkHOA16/XtNlrjGO79RKpkZUVuS749tamOqogizHG8oiNiJYf7J+721rXS3WdmBaZNKAjtApsUtiWi6vC+iN4BkupwGfvmXrOrTRFtAgRnJGqjAgl72mu8cDqYmAE0aa17YzxWB0yx6hwGE6yiuQ24FvgcZnbhb5i6if+xXTsJKLBeN4L1/qeBQZNbSqmblVKbhxiLSDT7qo4U95uoKg9PcB071U6/P5DS93eFgX44afvfpcL9ewhsjVrFkdS6anuq4+AerlhRSfDdttU0cbJNuqe6Rk3iiy9DgUSzCSSyPIo7rx2fgQQ4H0wUArECCYAe2zWDCf7GnwxcqbX+gdb6Ua31NcBzwIeVUsGPD8HXGer9Bn0vqxZj3RBjEYm2bJ1ZDw6meKqtZejrE6goP4fpEwsAGAhojp0ah7MTjXUQsIKoKdOStpQukbYmesvxodhWdHBoL+Wl+aFlqBp4RmYn3CPBO4U2nenhjuhA4t1ruHjJ+AwkwPlgogvIG+SxfNs1gwmG/q9orfdFPfYz6+ulUa8z1PsN9V4i1YpLYP6y8PHOTSl9+3FfN9GQXs2q2rv72V/fCphCrNXzEthCO5aK2VBgff440wJNjVy1Kjx788yOWuk54QZaRxZfjjGYaDpjaiTqbIHEF689h4vGcSABzgcT9ZhURqw/8JWYFMhQi/yDoX+saqcG66u1pzUtmODjrFSG9f5lxEiBCIdFpDqcq5s4NB5XdKTZBl/bjzQRsP52L5gxgdKCJPfE8HiganH4+NAeLlg8jaI8k49vaOli17Hm5I5BDK+pETqsPXYKi6F89IWRp850c8fP/35WIHHh4umJGGlaczqY2GSNYYP9pFIqH1gNDFejEEykx2rLFzx3EkBrHQC2AmtiBC8brHFITYTb2JeI7t4K/alrIBXZVnucBxNpUHy55XASthwfTrU91bGH3OwsLlse/mP1J0l1OM8+KzFnQTh1OkIn27q542evUN9sJrCzPIovSSAR4nQw8QgmvXhb1PmPYeoXHgqeUEpVKKUWK6VCdQ1a6xrgZWCDUuoc27VZ1msMAE/bXveX1uveHPV+t1nXPjq2b0ckXMUs0xEToLcb9u9K2VtH9JpoPDP+pqzTrGHVa/Ytx6uTXC8RZA8mDu4FiEh1vLinge6+gdSMRcRmb1Y1yuLLk23dfO7nr9DQYgKJbI/iS+85hwskkAhxNJjQWu8E7gPerZR6XCl1k1LqHkxHzOeBh22X3w3sJWoWA9P0qgv4s1LqLqXULdZzNwBf11rbfiPyILAF+JbVsvsmpdTjwLuB/7CCE+EmSsEqZ7phTi3Np9jabbKjZ2B8Vef7/aaVdpDLV3LUN3eGftHn52SxZOakYZ6RIFWLzM8oQG0N9HSzaMYEZk8xPVJ6+v28uLdhiBcQSTfGZlWxAokvv3ctFyySQMLO6ZkJMLMCnwWWYQKL64B7gbdZqYkhaa1fAy4AXrJe65tAEXCj1vqrUdf2Ybpl3o9pmHUfsBgTkHwlId+NSDx73cT2V1PWDVMpRdW08BLRcVWEeaoe/NYn6klToCDurXIcsdU2K7Fybllob5WkKygym0YB6ADUvI5SiqvsPSe2SarDMQE/HD0QPh7hzETfgJ8vPrzxrEDivIXTEjnKjOB4MKG19mut79FaL9Ja52mtK61W1x1R192gtVZa6+divMYOrfU7tNYTtdb5Wus1WuufDPJ+rVrrT2mtZ1jvt1Rr/X097uaw08jCFZBnlmnSdCKyK2OSRewgOp6KMNOs+HKrE/USQfMj6yYArlhZiceasdh5rJl6q2BPpFjD8fCuwxPLzG0EHn35UGivjZwsjwQSQ3A8mBBiWNk5sHxt+DiFqY5xuzy0Pn2WhfoDAbbV2PtLpDiYqI7sNwEwuTif9fPDdRvP7JDZCUeMob9E3elOfvVyuI3/zW9cIoHEECSYEOnBoW6YVeM1mEijHhP769vo7DUpmSkl+aF6hZSxd8I8tDfU6MveXvvPO+oIyORn6o2y+FJrzfef2hXqfLtwxgTeutb9K5qcJMGESA/L14cL3Q7uho7UdKScPbWYbI9538bWbjp6+lPyvo5Lo5Uc9iWha5LdQjuW8hlQbKXDujpChavnLZxGaYEp4D3Z1s32I6cHewWRLKNsVvX8noZQ6syj4NarV5DlSfHPVZqRYEKkhwmTwp8sAgHYnZqWIDlZHuZMDRdh1oyH2YmAP7IuxeUzE/b9ONamOsUBJsidb5+dMHUTOVke3rAi3CPv6W2pq/URmJ40tbYFenMXxPW0zp5+7n96T+j4HevnsqBiwhDPECDBhEgnkupIjdMnw83BSidBcenQ1zuos7effXWtoeM1qS6+DIpRNwHwxpXhVMdL+07QOV5mttzg+OHwiqRplVBUMvT1lp889zrNHWYLp8nFeXz4srHv5TEeSDAh0oe9G+bOTaYXQgpU2ZpXjYu22mm0kmPHkWb8Vg/t6mmlTCwabOudJIuum7DMr5gQCkb7BgI8v0d6TqTMKFIc++tb+d3mo6Hjj1+1lKK8nESPLCNJMCHSx6wq0/MATG7a9ks7mcZdW+00WsmxtSZyy3HHzFkAWVnmfsOxiJqeiJ4T2yXVkTIjLL70BzTf+8Ou0P4ua6unjtvtxEdDggmRPpRyZOMve5rj6KkOBvzD9lJLbw3hT2Zun5nYeiiFW44PJS8fZlWHj2vCmxhfvnxGqHhvb21rqG+BSLIRLgv9/ZajHGgwG4LlZHn41JuXpb6YN41JMCHSy8rUt9YuKchh2gTTNKvfH+B4pv8xSJOZiZNt3dRazaBysz0sn52iFtqDqT67CBNgYlEe5y0oDx0/I5t/JV9XJ5ywZoE8HphdPeTlp9t7+PGz4ZmM6y+az4zJ7u766jYSTIj0sngV5FhbS9cfg1OpyUGPmyJMraOWhbp3bb19FceK2ZPJzc5ycDTE3PQr6KrV4b1N/rKzNlTnIZLE3kK7ch7kDl1L88Aze+myepXMnFzEey+oSuboMpIEEyK95OXDktXh4+2pT3VkdDDRfCrcfri4FErcuyQuYstxJ1McQfa22jX7IgqE11VPZWKRCYJPt/dGBEIiCUawudeWw6d4bnd96PiWq5c7H5imIQkmRPqJSHWkZoloxHbkmbyiI7rzpUtzxv6Ajthy3NHiy6DJU8MFwr09UHck9FB2locrbD0n/iSbfyWXvfhyiHqJvgE/3//jrtDxG5bPYLVTy4vTnAQTIv3YizBf3wE9XUl/y+g9OjJ2X7j69Ci+PHSijfZu07NhUlEe88rj6yGQdIPUTQBctSqc6nhlfyNnuvtSNarxJ2JmYvCVHI+8fIj6ZvP7oygvm5vfuHTQa8XQJJgQ6WfyVLNMFExTmt1bk/6W0yYWUJiXDUB7dz+nzvQk/T0dkSbFlxG7hDrRQnswEXUTkcHE3PISFlqdFPv9AZ7bVY9IgrZmk64DUysxSN1P3elOHrFt5HXjGxYzqdihPiUZQIIJkZ5S3A1TKTU++k2kyQZfW20pDse6XsZiDyYO7zvr4atW23tOSKojKexLQmdXh/t/2GitufeP4Y28Fs2YyNXnuPfnPR1IMCHSU3TdRCD5vR8i6iYyMZjQOi26X/b0DbD7WHPo2BX1EkGzq8KrjU41mE/JNpctqyQny/zaPdDQNj72ekm1OFIcz+9uCNXcmI28lstGXmMkwYRIT/MWhlcatLfCkQNDXp4IESs6MrEIs60Zuk3fBgoKYWKZs+MZxM5jzQxYSyvnTi2hrCTf4RHZZOdEbigV1aW1pCCHCxZNCx0/vUNmJxJumOLLjp5+7n8mciOv+bKR15hJMCHSkycLVqwPH6eggVXGLw9Nk5UcWw67bBVHtEE2/Qp6o6299l931mV+R9VU0jpqT46zZyZ+8mx4I6+yEtnIK1EkmBDpK8XdMOdMLQ5NhTa0dNHZm2E7QKZBigMim1W5M5gYfEUHmJ4YU6zZlNbOPjYdlJ4TCXOqATqtfVEKi6E8cm+NszfyWiYbeSWIBBMifS07J1xcdewQtDQNff0Y5WZnMXtKcei4prF9iKvTUMRKDnd2vjzd3sPRU6adeU6WhxVzXJiKqbIFE0cOhLdzt2R5FFesDPeckM2/Eii6WZVtdi24kVdwUfe66qlcvGR6aseXwSSYEOmroAgWrggfp2BVR1Umr+hocP/MhH1J6LJZk8jPcWGnwgmTYKr1iXig3wS6Uew7ib564CStnb2pGl1mG2Kn0N/ZNvLKzfbwSdnIK6EkmBDpLcWpDvuKjoyrm0iDNIfrUxxB84eum5hZVszSmWZjMn9A81fpOZEYR2LvFHq6vYef/FU28komCSZEerMHE3u3QV9yP+FFzExk0oqO9lboMJ/ayMuHSS7Y6yJKQOuI/hKu2I9jMFVD101AVM+Jbcczt6tqqvj9cPRg+NgWTNz/9B66+qyNvMqKeM/5spFXokkwIdLbtBkw3fql3NcL+7Yn9e3swUTNyXb8KehvkRIR9RKzzLbNLlPT2E5rp6k/KC3IiZglcp3omYkYgcIlSyvIyzb/zjUn2zmYScGpE+qPhj9MTJoSWtq85dApnt8T3l34lrfIRl7JkD3SJ3i93klABXDI5/P12s7fCLwT6AS+4/P5UrMDkxArz4UT1nr9HRsju2Mm2ITCXKaU5tN0pod+f4DjTZ3Mdcu+EGORBsWXW2vCKY4186bgcXO+u3IO5BVAbze0njbtncvKIy4pysvhoiUV/GVnHWAKMRdIv4PRi5Hi6O33c69tI68rVlTKRl5JMpqPH18HXrU/1+v13gL8N/B24DrgOa/XKzumiNSIaK39asxPgYmUkW2106z4cm21i1McYPqgVNkKAAdLddgKMZ/dVU/fgD/mdSIOMYovH3n5EA0tZiOv4vxsPnblkljPFAkwmmDiQuAvPp+v23bus0AdcAnwPuvcZ8Y4NiHiM3+ZWdkB5hNgbU1S3y4jg4noNIfL9Pb72WVroe2q/TgGM8SmX0Er55YxbUIBYDaQe3X/yVSMLDNFLQutPd3Bo3+TjbxSZTTBRCUQ+m1tzUDMAu71+Xwv+Xy+x4AnMYGFEMmXnQ0r1oWPk7xEtCoTV3Q02LYed2GaY9fxZvoGTH3KzLIiyq0/wK42zIoOAI9SXLnSvvmX9JwYlb5eqDsSOtSzF0Rs5LW4UjbySrbRBBMFgH3/5QsBDfzZdu4QJugQIjVSuEQ0eo+OtK/C72yHthZzPzsHpk4b+noHvGZPcbh5FYdd1eLw/eOHoDf2tvX29tqbD53idHuGbm+fTMcPmdUcANNn8lxNG9tqTgNmI69b3rLc3TU2GWA0wUQdYPu/hDcBZwB7Gf0kwJ4GESK5lq8DZf04H95nljomScWkQgpyTTV4W1dfqM9/2mqwfRqumGXy/S6z1e37ccRSWByuPwkEIqfhbSomFbJyzmRzmSZUkClGwLbteP+s+dz/dHgm6JoN82QjrxQYTTDxLHC11+v9lNfrvQl4B/CUz+ezr5GbD8h8nUid4lKYbxVXaQ07NyftrTxKZdYOovX2FIf7poJbOnpD6aQsj2KlG1toDyZi06/YdRMAV60K16lIz4lRsBVf/r1vIi1WR9EpJfl8+FLZyCsVRhNM3A10AN8FHsCkPO4KPuj1esuBS4G/JWB8QsTPqVRHutdNuHwlx2u2RlVLZk6iMG/EK9qdM8wOokEXL5kemu06frqT1+tbkzywDGOb9Xm8Mbxx18evWppePy9pbMTBhM/nqwGWAZ8GbgWW+3w+25oc5gD3AT9JxACFiJt9iejuLWZfhCSxN0xK+xUdESs53BdMRKQ40mEVh13EDqKxm1cB5Odmc/GS8A6Xf9pWm+yRZY6uDmg0qSG/8nAw1/yMrJ8/lYtkI6+UGVXI5vP5TgDfH+SxTcCmsQxKiFGZMQemTIOmRujuggO7YcnqpLxVdSa11Y6YmXDXSg6tdUSzqrXVaRZMTKuEohJT5NpxxvzRmz4z5qVXrZ7F09tNEPH87no+ftVS8ty4kZnb2GYlDueU0a+yrY28lstGXimUsPkfr9c7BbgY6AL+7PP5pPuKSC2lTKrjr0+Y4x2vJi2YmDO1BI8yBXN1zZ109w1QkJuG06ndnaY3B0BWdni3S5c41tTB6XaT/y7Oz2ZBxURnBzRSHo+ZnQguVz60Z9BgYvmsSVRMKqShpYvO3gH+9voJLl8ui+KGZQsmXs81K5Guv2g+FZMKnRrRuDTiNIfX6/2E1+t91ev1TradWwvsBR4D/gD8zev1ypZsIvUiumEmr99EXk4WM8uKAbMuuuZke9LeK6kabNPp0ypNzw4X2WJLcayeO4UsTxp+0oyzbkIpFdERMzhLIYZhW8nxet40ZslGXo4YTQHm+wHt8/mabee+iVkO+mNMMLEe+PjYhyfECC1aaXa9BDOlfCJ5v5AjtiNP11SHvVmVG4sv02XL8aFE100M4cqVMwmGS68dbuJkm6ywH07fwfC/6f7ccj51tWzk5YTRBBMLgB3BAyu9cSnwPz6f7yafz/d2TM3EBxIzRCFGICcXlp4TPk7iqo6MaKvt4uLLvgE/24+GP7O4esvxocxdGN6Ftf4odHUOemn5hALWWEGTBv68Q2YnhtJ76iS57abhWrfKYf45K1g9N02DzjQ3mmCiDLA3kL/Q+vob27kXMas6hEg9e6pje/KCiapMWNHh4uLLvbWt9Pab0quKSYXpmwPPL4CZ1rS71lCzb8jL7amOZ3bUSs+JIbz81Auh+zX55XzsqmUOjmZ8G00w0QzYQ79LgQCRfSU0kD+GcQkxeivWh+8f2GWWjiWBfWaipvEM/kAa/tKvd2+Pia2ZkOIIsqc6Btn0K+iCRdMpsnoj1Dd3set4SzJHlraON3Vwcnu48XL+wqVMLJKNvJwymmBiL/B2r9db5vV6J2JqKDb5fD77R7O5wIl4Xkwp5VFK3a6U2qeU6lFKHVdK3aOUiquAUyn1nFJKD3JbF3XtZUNc+7u4vnvhfhPLYO4Ccz8QgF1bkvM2RXlMtnYh7B0IUNc8+PS1K/X2wOlGc195oHyGs+OJsjUd9+MYTBybfgXl5WRx6bLwf4tnZPOvs2it+f4fd7GgJ/xnZu65ax0ckRhNMPFdoAKoxbTMng74gg96vd4s4CIi9+oYyreBbwF7gFuAX2OaYT2plIp3fE3Ah2LcDg9y/QMxrv2vON9LpIMUdcOMaF6VbkWYJ46HmyiVV5h6E5c409XHgYY2wGzUtGpuGrXQjsU+M1GzDwJDr5x/0+pwquOFPQ309A0ka2Rp6dld9WyraWJhXzjj7pm3yMERidF0wHwCs1JjN/A68Fmfz/cL2yVXYlIcfxrutZRSyzABxONa63drrR/UWn8G+AxwOXBdnMPq1Fr/IsateZDr/x7j2ufifC+RDuzBxM5Nw/7yHq20bqtt3+DLZfUSr9U0EUwaLaqcSHF+zpDXu17ZNJhgrabv7opML8WwaMZEZpWZydnuPj8v7o1rondcaO/u5/5n9jBjoI2SgLXJXnEpTJFul04abQfMBzCf7mM99ifMMtF4XA8o4DtR5x8E/gP4IPBwPC9kzWIUA+06joolK43i11rLfr+ZaHa1+eXd1my6Dx7aBwsSX5xVnc7BhItXcmytsbfQTvMUB5iGatVLYOvL5vjQHpg5b4jLFVetnsX//MUUaz69/XjEVuXj2U+e3UdrZx9r+hrDJ+cuNP/GwjGjSXMk0npM8WZEdyHrD/w26/F4VGI2H2sDOpRSjyulFg9x/Xet67uVUvuVUp9W0nc1s3g8UQ2skpPqsKc5atItmHDpBl9a6/Tccnw49uZVB4eumwC4YkUlwR5dO44209DSlaSBpY99da38fov5uV3Ya1tUKCkOx4263Z3X6z0PuAlYA0zE/CHfAvzY5/PFu2PoDKBJa90b47E64AKlVK7Wum+I16gBXsb0vvAD5wKfAq5QSl2ktd5pu7YfeALTWKveev9/xMyMrAZuHOxNlFI3Azd/4hOfiO87E85beS68+JS5v2MjXPvRhL9FxaQi8nOy6On309zRS3NHD5OL02QhU707G1bVNXeGmjUV5mazuHKiswNKlPm2uonDwwcTZSX5rKueysaDZlXLM9tr+fBl43c7bX8gwL1/2BlKf631nA4/OHf8/ru4xahmJrxe79cwf8A/igkm5mH+GP8j8KLX6/16nC9VCMQKJMBsbR68ZlBa6xu11l/UWj+itX5Ma30HcBUm5fGtqGtf1lpfo7W+X2v9pNb6fuA8TH3HDUqpi4Z4nwe01usGe1y40JLVkG3l2uuOQFPi885ZHsW88pLQ8eHGNGmr3d8HJxvMfaVgmnum0O2zEivnlpGd5fQEaoLMnh/+eWysg/bWYZ9y1apZoft/3lFLYBz3nHhy81EOWkXOBVma2V1RaQ7hqNHszfFe4E7gGGZmogoosL7eZJ3/vNfrfV8cL9cFDLYwON92zYhorV8EXgAuV0oVDHNtALjbOrx6pO8lXCy/ABavCh8naa+OqnRsq91YBzpg7k+ZHm5B7gJbIpaEZkiKA8xqmTnzw8fDLBEFOHdhOSUFJgBpbOtmx5HTwzwjMzWd6eGnz4b34PinZcWofmvCenI5TIi3TE8ky2jSHLcAjcB6n8/XZDt/BPiR1+t9AtgFfBJ4dJjXqgeWKqXyYqQ6KjEpkKFSHEM5AlyGKQYdrsH9EetrBv3mEoCpm9i12dzfsRHe8I6Ev0VattV2afHlgD8Q8QczY+olgqqXhoOIQ3th9flDXp6bncXly2fwxCaTknp6ey2r52XYv0kcfvj0Hrqs5bGzyop44wTbZ8x5MivhBqOZP1wFPBYVSIRY53+NSXsMZ5M1hg32k0qpfOv5m0cxvqAFwACmY2c814IJkkQmsRdh7tsOPYnfOClyw6+2hL9+Uri0+HJfXWvoj0b5hAIqJ2fY5sMj2PQryJ7qeGlvA509/YkelattOniSF/c2hI5vuXoF2UfDsxTMleJLNxhNMJHN8KmHLuKb9XgE03r7tqjzH8PUSjwUPKGUqlBKLVZKFdrOTVBKnbU9nFLqrZg9Q56xL/1USp3V+UYplQfcZR0+GceYRTqZMh0q55r7A/2wd1vC32JueWmo6r6uuZOe/uT0tEgoe/Gli2YmoldxZNwiq4jmVa/DwPDNqOZPLw3V5fQOBHjB9oc10/X2+7nvqd2h4ytXVpoGZkdswYTMTLjCaIKJg8DbvF5vzOda568GDg33QtZKi/uAd1vLOW9SSt2DKZx8nsgeE3djWnnbZzEuBw4opb5rLe/8pFLqp5gVG02cHaQ8pZT6rVLqK9Z7fQXYiQk87tVaJyepLpyV5CWi+TlZoU/QAQ1HTqZBEaZLZya21tj248jE6fyJZTBlmrnf3wfHh/01aXpO2HpMPL1t/Owk+quXDoaWxBbn5/CxK5eYNvB1R8wFSkXWoQjHjCaY+CWwBPit1+tdYH/A6/VWA48BS4mz2RTmD/5ngWWYwOI64F7gbVZx5FBexyxHfRvw75gg5CLgh8BqrfX+qOsfA8oxdR8/wHTarAM+oLW+Nc7xinQT0Vp7o9mvI8Gq0qluYmDAFGAGVcwa/NoU6ujp5/W6VsB0sluTicEERPabiDPV8YYVlWRZ0197als43pSczevc5FhTB4/+LRxs/eMVi81GXscOhf8fnj4LCjIsFZamRlOA+S3gzcBbgbd4vd56oAGzR0clJkB5iahlmYPRWvuBe6zbUNfdANwQdW4v8N54B661/gbwjXivFxmiejEUlZhOmG3N5pfR3AXDP28kbzG9lOf3mOln19dNnKwHv5WKmTwV8t2xtff2I6cJbry6oGICpYXu2SskoaqXwKvPmvuH9sKV7xz2KROL8jh3QTl/e92UdT2zo5aPvmGovnzpLbiR14D1A7GkciJvXmMFvfYUhywJdY3R7M3RB7wR+CKmYdRMTKfKWdbxF4ErrOuEcJ4nK3Jb8tdeTvhbpNUeHQ3uXMmxxbbl+JpMW8VhF1GEOfR25Hb2dtp/2VGXnlvex+nZXfVst1b1eJTilqtX4AnWz9S8Hr5Q6iVcY1TdYHw+X7/P57vb5/MtAEoxgUSpz+db4PP57gayvF5v6dCvIkQKrbkgfH/zi+HdMhMksq12u7ubC7m082VGbTk+lJlVkGu112k+ZW5x2DC/nIlFZramqb2HrYfje166CW7kFfTOc+dG/P8VOTMhKzncYtTttIN8Pl8HZp8Lux9gtvUe8+sLkRAr1pvGTL09pl6gtgZmVSXs5ScX5zOpKI+Wzl56+v00NHdRWebSXK59t1CXzEw0tHSFCu3ycrJYMnOiswNKpqwss5fE6zvM8eG9Jt00jOwsD29YUcnjr9QApr32+vnlyRypI35sbeQFMKUknw9dYpt96Gg3aTqArGyYNfhmaSK1ktmnNsPWdIm0lpsHq2yFmFteTPhbRHTCdHOqI2Ilhzu2Hrd/yl41ZzK52Wet+M4sI9z0K+iqleFUx99eb6S9O7N6Tuyra+EPW8I/n59401IK82yfSe39JWbNM11FhStkSNN7IeKw9uLw/U0vJDzVUWXbo8O1RZgBf9TMhDtWcthTHGsyOcURNH90dRPzppWyoGICAP3+AM/trhvmGenDHwjwvd/vCm3ktWFBORcunh55UY29v4SkONxEggkxfgRTHRBOdSSQPa/r2uWhpxpN8y6ACZPNKheH+QOabUdszaoydUmoXZUtmDh2CPoG2+/wbBE9J7ZnTs+JJzYdDc3o5WV7+OSblp3dtOyIrfhSVnK4igQTYvzIzYvsOZHgVEd1OqzoaHBf8eWBhlY6ekwnyLKSPOZMLXZ4RClQXArTraDAPwBHD8T91MuWzyDH2kl1f31bejRJG0bTmR5++lw4UPjAxQuYPilqybLWkSs5JJhwFQkmxPiyzpbqSPCqjsqyYvKyzf9Sp9t7ae2M/9Nmyrhwg6+IFtrzpmZeC+3BjLJuorQgl/MWTgsdf/HhjfzHb17jt5uOcKChjQF/4puyJdsPn95Nd5/pfTJ7SjHXnh+jOLqlCdpazP28Atek6IQhqy3E+LJ8XXhVx4la05Z3ZmIqwrM8irnlpbxe3wrA4cZ2zqnKS8hrJ4wLiy+3RO3HMW5UL4GXnzb3D8cfTAC8afXM0OZXTe09PLurnmd3mVUOedkeFs6YyJKZk1hSab5OKnbZz6GN2cjrROj4lquXh2ZeItiXhM6Zb/rHCNeIK5jwer1psHOREHHIyzepjk3Pm+PNLyYsmABTNxEMJg41trnvj2O9u/bk6OodYG9tS+jYdf9eyWRvXnVwj5kli3NWZm31VK4+ZzbPbK+lP2omoncgwM5jzew8Ft4wefrEAhNczJzE0pmTmFdeQnasP9gp1tvv5/t/3BU6fuPKmaycc9Z+jMYRKb50s3hnJkYz7+jirj1iXFt3sS2YeAGu+VDcv8SHUzUtXNB4+ITL6iYCAdd1v9xx9HSok2PVtFKz98J4UTHb7CvR3QntrXCqAcpnxPVUj1J8+q0r8L55GYdOnGFvbYu51bVysq37rOtPtHZzorU7YvZiwYyJLKmcyFIryHBi9uKXLx3kRKsZb3F+DjddOUSLcOl86WpxBRM+n8/5EFaIRFm+zhRj9vUmPNXh6rbazSfDqwZKJpibwyK7Xo6jWQkAj8fMTuzabI4P7Y07mAjKyfKwuHIiiysn8q5zzc/w6fYe9tS2sK+ulb21Leyvb4s5e7HrWDO7bLMX0yYWsKRyEktnmtRI1bTSpM5eHGvq4NexNvKKJRCAI7YiVSm+dB2pmRDjTzDVsfkFc5zAVMe88lIUZlrueFMnfQN+9zRgcmXx5TjZj2Mw0cHE+VeM+SXLSvK5eEkFFy+pAEw/Cvvsxb66VhpjzF40tnbT2NrNc7vPnr0wKZKJTC7OH/P4wGzkde8fdoY38ppp28grlpN1ZgYHTBBcNm3wa4UjJJgQ49P6SyKDiQSlOgrzspkxuYi65k4CWnP0VEeoyZDjXJbiONnWzfHT5g9EbraH5bMmOzwiB4xy06+RGGz2IpgW2VvbwoGGNvoGRjZ7sXjmJKpHOXvx15117DhqXtejFLe8xbaRVyw1UTuFjpcVP2lEggkxPkWkOo6bza8q5ybkpaumlVLXbP5IHjrR5p5gwmXFl6/VhFMcy2dPJi/HJTM4qTRvESgP6ADUHoGerpRsCV9Wks9FSyq4yDZ7cbgxOHthAox4Zi9ysz0sqJgQqruIZ/bCbOQVXr3yruiNvGKR4kvXk2BCjE/RqY5NLyQsmKieXhpatuequokGdwUTWw6FUxzjoutlLAVFMHMuHD9sAoqa12HJmpQPIyfLw6IZE1k0YyLv3GDOnW7vCdVd7Blk9qJvIMDu4y3sPh5ekTNtQkEosAjWXtiXev7or/to67I28irN50OXxlH/IM2qXE+CCTF+rbs4KakO+4qOQ25Z0aG1q9IcAa3ZduR06Pic8bAfx2CqlphgAswSUQeCiVjKSvK5cPH00P4YwdmLfbUt7KltZW9dC42tMWYv2rppbDt79mLJzEmUl+bzx63hn0Pvm5ZRkDvMn6GBAdNyPEhWcriSBBNi/FqxPimpjupp4bRGTWM7Aa2HzgenQutp6DZbfFNYbPblcNChE2dCn04nFuUyb5rze4Q4Zv4SeP735v6hkTWvSiX77MU11uxFc0dPKC2yt66V/fWtcc1egNnI64JFcRRS1tWE95OZMg1KJibguxGJJsGEGL/y8mHlBjMrAeZrAoKJspI8JhTm0tbVR1ffACdaupgxuWjMrzsm0Ss5HA5u7Ks4zpk3xflgy0n2ttqH95llkJ70WI0/uThy9mLAVnuxp7aVfXUtoT4SdoNu5BXLkajiS+FKEkyI8W3dJZHBxDUfGvNLKqWomlYaKjA83HjG+WDCZfUSEftxjOcUB8DUCvNpu70VujrMLJlLWp2PVHaWaeW9MGr2Yl9tK3us2YszXX18+NKFZ2/kNRjZdjwtSDAhxjd7qqPhmGlglYhUx/RwMHGo8Uyoat4x9bbdQh2ul+jp90dMeY+rFtqxKGWWiG77uzk+uCdtg4lYJhfnc8Hi6VxgzV6MmBRfpoX0mEsTIlmCqY6gzYnZlty+Hbkr2mq7aGZi59HToY6Mc6YWU1aSmEZIac2e6nBx3UTK9faEU3TKA3MWODseMSgJJoSI3pY8AVzVVltrqLPNTDj8qXdrjaQ4zjLf3rxKgomQowfNklkwW47nFzg7HjEoCSaEWLHBpDognOoYo5llRaG19afO9HDGWrngiDOtJhcPkFcAk5xNK2w9NI734xjMnAWQZTXtOnEcOlwwm+UGRyTFkS4kmBAiCamO7CwPc8ttO4g6OTsRneJwcOXE6fYejpxqB8xSwxWzx2EL7Vhy82D2/PDxYZmdAKT4Mo1IMCEERKY6tiShbsLJYMJFxZf2VRxLZ00if7iGReOJ1E2cLaKNtsxMuJkEE0JAZKqjPjGpjqrpLqmbcFHnS/t+HOO2hfZgpG4iUscZOGXa0pOdk7CdfUVySDAhBJhUx4r14eMEpDrsMxOOttV2yQZfWuuo/hISTESosgUTh/eB3+/cWNzAPisxq8oEFMK1JJgQImjdJeH7CUh12FtEH2vqoG/AoT8ODcfD9x0MJmpOttPS2QtAaUEO1dNdspuqW0yeam5g+p7UHnZ2PE6z95eQegnXk2BCiKCVUakOe63BKBTl5VBhdfnzBzTHTnWMdYQj13EGzlgNonLzoKw89WOw2GclVs+bQpZnHLfQHozUTYTVSBvtdCLBhBBBSUh1ON5vwl4vMX0meLJSPwaLfT8OWRI6iGqpmwBMbxTZkyOtSDAhhJ091ZHguglHVnREb/DlkL4BPzuPNYeOpVnVICKCiT3OjcNpLU3hGbX8QhMIC1eTYEIIu4hUx9ExpzqqpzscTET0mHCu8+Xu4y2hralnTi6ifIJ0MoxpVnX456+p0WwdPx5F7MexIG12UR3P5L+QEHYJTnVURa3o0FqP6fVGzCUrObYcsm05Xi0pjkFlZ0dO6Y/XVEdEvYQUX6YDCSaEiLY2cXt1TC3NpzjfLGnr7B2gsa17TK83Yi7pMRHZX0JSHEOqkrqJiDba0qwqLUgwIUS0s1Idx4a+fghKqchURyr7TXR1mtwzmDX6U53ZBr21s5eD1ved5VGsnCsttIc03ptXBQJw5ED4WIov04IEE0JEyy+ITHWMsedEtVMrOuyzEtMqwxtJpZh9VmJx5USK8qT50JDsRZhHD0C/g5vEOeFELfR0mfulk8K9N4SrSTAhRCz2VMemF8b0UlVOrehwSfFlZNdL+cMwrJKJUD7D3B/oh2MHHR1OykUvCXVwYzoRPwkmhIhl5QbIyTX3x5jqqHZqjw4XFF9Gt9CW/hJxsjevOjjOUh2yuVdakmBCiFgSmOqYNaWYnCzzv1pjazcdPf1jHV18InpMzErNe0Y53tRBU3sPAEV52SycIS2042Kvmxhv25FLG+205HgwoZTyKKVuV0rtU0r1KKWOK6XuUUoVxfn855RSepDbuhjXT1BK3auUqrPeb7dS6hNKyVyaiJKgBlY5WR5mTykOHacs1dHg/NbjW85qoe34r5z0EDEzsdt0hBwPBvrhuG1PEim+TBvZTg8A+DZwK/Ab4B5giXW8Ril1pdY6EMdrNAG3xzgfsVOOUioXeAZYA9wL7AXeAviAacBdo/sWREYKpjr6+8yW5A3HR/0Jv2p6aSjFcejEGVbOKUvgQGPo7YHTJ819j8cUYDpga43sEjoqM2abzo89XdDWAqcbYcp0p0eVfLU1JqAAs/qouHTo64VrOBpMKKWWAbcAj2utr7WdrwG+B1wHPBzHS3VqrX8Rx3U3AeuBW7XW91rnHlRK/S9wp1Lqx1rrsbU8FJkjmOrY+rI53vwCvP0fRvVS1dNKeca6n5K6CftOoeWVjmzf3O8PsONIuIPjOfMkmIibJwuqFsOereb40N7xEUzI5l5py+k5x+sBBXwn6vyDQBfwwXhfyEqXlA6TrviA9boPRp3/DpADvD/e9xPjxLrENLCyF2HWpCSYsMXEDhVf7q1toaffbLteMamQGZPjylyKoPG46ZcUX6Ytp4OJ9UAA2Gg/qbXuAbZZj8ejEugA2oAOpdTjSqnF9guUUh7gHOA16/XtNlrjiPf9xHix8tzwqo5gqmMU7MtDj57qoN8fT/ZuDFywksO+imONzEqMnD2YODhONv2K2JNDii/TidPBxAygSWvdG+OxOmCKVecwlBrgP4Ebgfdi6h/eAryqlFphu24SUGC9bgTr/U9jgpKYlFI3K6U2DzMWkWmiV3VsHl3PieL8HKZNNJtb9fsDHG/qSMToBueCNtpbZMvxsalaHO6xUHvY1MFksp6u8M+t8sCc+c6OR4yI08FEIRArkADosV0zKK31jVrrL2qtH9FaP6a1vgO4CigGvhX1XgzzfoO+l9b6Aa31WatDxDiQoFRHVXnkpl9JVe9sw6oz3X0cqG8DwKPMSg4xQoXF4f92gUDkp/ZMdPRgeNXKjNlm0z2RNpwOJrqAvEEey7ddMyJa6xeBF4DLlVLBvY6DrzPU+434vcQ4kKBUR8q2I+/rhVMnzH3lzEqO7TWnCS5mXDhjYmizMzFC46luQuol0prTwUQ9JpUR6w98JSYFMtrG9EeALEx6A6AF6CZGKsN6/zJipECEIL8AltsmpUaZ6kjZHh2NdRBcUT11enjTshSypzhkSegYRAQTGV43Ic2q0prTwcQmawwb7CeVUvnAamAsNQoLgAGgGcDqV7EV078i+rfrBmscUhMhYktAqqMqamZCJ6sRUb2zKzmiW2jLfhxjYG9edWhvZjevit6TQ6QVp4OJRwAN3BZ1/mOY+oWHgieUUhVKqcVKqULbuQlKqbO2QlRKvRW4EHgmauXGL63XvTnqKbdhAo9HR/2diMy26txwr4a6I3Bi5KmOaRMKKMozrV3au/s5dSZJBXUOF1/WN3fR2NYNQEFuFksqJ6Z8DBljWmW4cVNnOzTWOjueZGlvhaZGcz87ByrnOTocMXKOBhNa653AfcC7reWcNyml7sEUTj5PZMOquzEdK+2zGJcDB5RS31VKfVop9Uml1E+BJzBdMW+LessHgS3At6yW3TcppR4H3g38h9a6JgnfpsgE+YVRqzpGPjuhlEpN3YTDy0K31oRTHKvmlJGd5fRnljSmFFTZl4hmaN2EvVnV7GrIdkNzZjESbvi//Dbgs8AyTGBxHabV9dviaKX9OiY4eBvw75gg5CLgh8BqrfV++8VW/cWVwP2Yhln3AYsxXTi/kphvR2SsRKQ6pqVgRUfEBl+pX8mx5ZAtxVEtKY4xmz8O6iYiii+lXiIdOR7+aa39mD057hnmuhuAG6LO7cX0lhjJ+7UCn7JuQsQvmOoY6Dd7CJw4DtNHtldHVbKLMAf64aStjjjFu4X6AwG2H5UW2gkVXTeRiSKaVUm9RDpyw8yEEOkhAakO+4qOpKQ5GutMTwKAsvKUr9XfcbSZrt4BAKaW5jOzTFpoj9nchWazNjDFtV1JbniWalrLstAMIMGEECMxxlTH7KnFZHlMV8OGli46e/sTNTLD3gMjxSmO1s5evvXkjtDx2uqpDL1VjohLXj7Mqg4fH97n3FiSofkktJsGZxQUmY3pRNqRYEKIkbCv6qitgRMjq67Pzc5i9pTi0HFNY3siRxe5kiOFxZf+QICvP/4aJ61VHIV52bzvguphniXilsnNqyJSHAvCszAirch/NSFGIhGpjulJrJtwaCXHg3/ex3bbduOff+dqKmWX0MSZb6ubyLRNv2qk+DITSDAhxEjZUx1bRh5M2IswDyd6RYe9YVWKekz8eUctv3k1vKr6Q5cu5LyF01Ly3uOGfWbi8D4I+J0bS6JJ8WVGkGBCiJGypzqOHx5xqiNpbbX9flOAGZSCYOJAQxvf/f3O0PEFi6bxgYtlt8eEm1wOE8vM/d5uqDs69PXpIuA3G3wFSTCRtiSYEGKk8guj9uoY2eyEfWbiyMl2/IHh2qnE6VSDWRoKMGkKFCY3zdDa2cu//noLfQNm/LPKivjsNavwSNFl4imVmXUTJ2pNcAQwYbL5uRVpSYIJIUZjDKmO0sJcppaaJZv9/gDHmzoTM6YUttEe8Af49//dGlFw+dX3raMoT3YHTZpM3PQrYnOvhSZoEmlJggkhRiM61dE4sg1nk9JvIoXFlw/+eS87jjYDoDAFl7Nsq1REElRnYBFmxOZeUnyZziSYEGI0CorGlupIxoqOFBVf/nlHLf+38UjoWAouU2R2dTiAPdUAZ1odHU5CRM9MiLQlwYQQozWGBlZJ2aMjBT0mYhVcXi8Fl6mRk2v6MASle91Efx8ct+2tOEeCiXQmwYQQoxWR6jg0olRHdJpDaz22sQT8Ud0vEx9MtHb28v8e3RwquJw9pZg7rlktBZeplEl1E8drwG9ar1M+A4pLnB2PGBMJJoQYrYIiWL42fDyC2YnpkwopzDX77LV19dHc0Tu2sZw+aT7pAZRMhOLSIS8fqWDB5akzPQAU5WXz1fetpTDP8b0Cx5dM2vTriPSXyCQSTAgxFusuCd8fQTDhUYp508KfxMac6khy8eVZBZfvWs3MMim4TDn7zMSR/eGlwOlIth3PKBJMCDEWZ6U66uN+akLbaiexXuKZ7ZEFlx++bCHnLpCCS0dMmAxTppv7/X1w7LCz4xkLexttmZlIexJMCDEW0amOLS/E/dTqRBZh1ienx8T++taIgssLF03juouk4NJRmVA30d0JJ6waH4/HrFQRaU2CCSHGaq1tVcem+FMdVYnsNRExM5GYrcdbO3v5f7/eQr8/XHD5WSm4dN78DKibOHoQgkXHlXPNNusirUkwIcRYrTpvVKmOOVNLQn+Y65s76e4bGN37a53wmYkBf4CvPbaVJlvB5V3vWycFl24QUYSZpjMTkuLIOBJMCDFWhaNLdeTlZDFritk/QwM1J9tH9/4tTeH9DYpKoHTi6F7H5oFn9rLzWLjg8l/etYbKMtlS3BXsn+RbmqD5lKPDGRX7Sg4pvswIEkwIkQhrR9fAKiF1E/bOlzNmj3l/g6e3H+e3m46Ejj982UI2LCgf02uKBMrKgnmLw8fpmOqQmYmMI8GEEIlgT3UcOwQn40t12Ntqj7puIoEpjtfrW/ne73eFji9cPJ3rpeDSfebbijD/9Gt4+nHYtx26ErRpXDK1tUDzSXM/Ny9hNT7CWZIAFSIRCotg2Tmw/VVzvPlFuPr9wz6tetqE0P1Rz0wkqPiypcNsKR4suJwztZjPvmMVSgou3afK3m/igLkFTa2AOfNh9nyzSmLOfNPIzC3s/SVmVUO2/BnKBPJfUYhEWXexLZh4Ia5gosrWuKrm5Bn8gQBZnhFOGCZgZiLY4TJYcFmcb7YUl4JLl1qyGmbOg9qasx871WBu9nTbpCkmqJhVHQ40Jk1xZstvaVaVkeQ3hRCJsup8k+oY6A+nOspnDPmUiUV5lJXkcbq9l76BAHWnO5k9dQR7FGidkIZV9z+z5+yCy8lScOlaObnwpXvNH+ZjB83t6CGoPwJ+/9nXtzSZ27ZXwudKJkTOXsyeb2Y1kh1gyE6hGUmCCSESZdSpjlJOt5uK/MON7SMLJtqaoavD3C8ohIllIx01T28/zhObwkWcN1y+iPXzpeDS9bKzTc8Je9+J/j6oO2oFGIfM1+OHw/u22LW3we4t5hZUUGSCi9nVJriYMx+mzwRPVmLGrHXkzIQUX2YMCSaESKSIVEd8wUTVtFI2HjTBxKHGM1y2fOjZjAgNUSmOEX6qjC64vHjJdN5/oXQjTFvBbcrtW5X7/abbZHD2Ihho9HSd/fzuTnh9h7kF5ebBrKrIAGPGnHDB8Ug0NUKHVRtUWDzszJ1IHxJMCJFIEamOg3GlOqqn24owR7qiYwz1Ei0dvfzro+GCy7lTS/hnKbjMPFlZpjdF5Vw4/0pzLhCAUyesAONgOFXSEePnr6/XLD+1L0HNyjavZ0+RzJw3fCfLmqidQuVnLWNIMCFEIhUWwdJzYEf8sxP2XhOHR7qio+F4+P4I6iX6/QG+9r9baWq3F1yupSBXfiWMCx4PTJthbuutnW+1NnUVweAi+LX19NnP9w+EA5CX/mTOKQ9UzIqcwZhVbf6fCIoovpQURyaR3xxCJNq6i0cUTFRMLiQ/J4uefj8tnb00d/QwuTjOvQrsDatGMDNx/9N72BVVcDlDCi7HN6Vg8lRzW3N++Hxbi2kTbw8ymk6c/XwdMD+P9Ufhlb+Gz9uXqtrrM+bKSo5MIsGEEIm2+rzIVMepBvMLdRAepZg3rYS9ta2A6TcxeX6cwcQoVnL8adtxntwcDkJufIMUXIohTJgEE9bB8nXhc53tprAzlCI5ZOoygpt32cVaqgoyM5FhJJgQItEKi89OdbzlfUM+pXpaaSiYONzYHt8f9/ZWU5EPpkhu8vDP2VfXyr1/sBdcVvC+C6TgUoxQUQksXmVuQT3dpu9FKEUyxFLVsvJRrTwS7iXBhBDJEJ3qGC6YsBVhxt1W214vUTHb5MGH0NzRw7/ZOlzOKy/hn9+xUgouRWLkF8S3VLWvF975EefGKZJCggkhkmH1eabi3T8ARw8Mm+qoitjwqy2+96iPP8XRH9xSPFRwmcNX3isFlyLJYi1VFRlJNvoSIhkKi00Dq6BhdhKdW16Cx5ogqD3dSU/fwPDvMYLiy/uf3sPu4y0AeBR84d1ScCmESBwJJoRIlnWXhO8PE0zk52SF2ldr4Mip9uFfP87iy+iCyxsuX8y66qnDv74QQsRJggkhkiWY6oBwqmMIEc2r4uk3EdGwKvZuofvqWiIKLi9ZWsH7Lqga/rWFEGIEJJgQIlnOSnW8NOTl9rqJYYswO9vNvhxglqFOnXbWJc0dPRFbis8rL+Gf3y4Fl0KIxJNgQohkWntx+P6WF4a8tHq6rQhzuGAiYiXHrLM2YgoWXJ5u7wVMweVX37eOfCm4FEIkgQQTQiTTmvPDqY4jQ6c67G21axrb8QdiNAAKGqb48od/2h1RcHnnu9dQMalwZGMXQog4STAhRDIVFsPSNeHjIVIdk4rzmFycB0BPv5+Gls7BXzdit9BZEQ899doxfrcl/PiNb1jMWim4FEIkkePBhFLKo5S6XSm1TynVo5Q6rpS6Ryk1qnVrSqlHlVJaKbUrxmOXWY/Fuv1u7N+NEDHYV3UMk+qYF9FvYohUR0SPiXDx5d7aFr7/x92h40uXVvDe86XgUgiRXI4HE8C3gW8Be4BbgF8DtwJPKqVGND6l1NuAa4HuYS59APhQ1O2/RjZsIeJ0VqojxiZJFnuqY8i6iejul1gdLh+LLLj8jBRcCiFSwNFqLKXUMkwA8bjW+lrb+Rrge8B1wMNxvlYx4APuA94xzOV/11r/YlSDFmKkgqmOnZvM8ZYX4c3vjXlpZN3EIMFETxc0nzT3s7KgfIYUXAohHOX0zMT1mB2QvxN1/kGgC/jgCF7r3zHB0ZfiuVgpVaSUinNrRiHGaJ1tVccQDayq4lnRYZ+VmFYJ2dn8ILrg8lopuBRCpI7TwcR6IABstJ/UWvcA26zHh6WU2gB8CrhNax3PLknfBTqAbqXUfqXUp5XMBYtkWm1PdewfNNVRObmIvGzzv+Xp9l5aO3vPviiiWdVs/vjaMX5vK7j86BsWs7ZKCi6FEKnjdDAxA2jSWsf4jUkdMEUplTvUCyilsjEzGU9rrR8d5v36gSeAz2FSIR8HWjEzIz8a5n1uVkptHub1hYitqCRyVceW2LMTWR4VWYQZa3bCtpLjVHE599kKLi9bNoP3SMGlECLFnA4mCoFYgQRAj+2aodwBLAA+Odybaa1f1lpfo7W+X2v9pNb6fuA84E/ADUqpi4Z47gNa63XDvYcQg4o31WHvhBlrRYdtZuLhQ30RBZe3v22FFFwKIVLO6WCiC8gb5LF82zUxKaXmA18B/l1rfXg0A9BaB4C7rcOrR/MaQsQlOtXRFDvVUTWCmYk9/hIASgpyuEsKLoUQDnE6mKjHpDJiBRSVmBRI3xDPvwdoBn6jlJofvGEKMXOt44o4xnHE+jplBGMXYmSKSmCJPdURu4GVva32WXt09PaEghA/irqcSVaHy3OYLgWXQgiHOB1MbLLGsMF+0lplsRoYrkZhDqbuYjdwwHarxKQ+DmDqKYazwPraGOe4hRgde6pjU+wGVvPKSwgmKo43ddLb7w8/2FgL2rTZrs+eQL/K4qNXLOacKomDhRDOcTqYeATQwG1R5z+GqZV4KHhCKVWhlFqslLJ//Pos8N4Yt1PAcev+3bbXKIsegDUrcpd1+OSYvhshhrP6fNMbAgZNdRTkZlM52TSADWjN0VPtocdqd+0N3T+WM9kUXJ4nBZdCCGc5mmDVWu9USt0HfEop9TjwB2AJpgPm80Q2rLob+AhwOfCc9fw/x3pdpdR/AR1a68eiHnpKKVUPbMGkWGZgelksAO7VWm9EiGQqLoEl58CuYAOrl+BN7znrsqrppdQ2m705DjWeYeGMiZxu72HTC5uZaV1zZuJ0bpcOl0IIF3B6ZgLMrMRngWWY7pXXAfcCb7OKIxPpMaAc03XzB8BnMEtQP6C1vjXB7yVEbHGs6qiK2qOjb8DPvz22hfLOU6Hz51+6nvycrFhPF0KIlHK89Ftr7ccUUt4zzHU3ADfE+ZpzBzn/DeAbIxqgEIkWTHX4/VDzOpxuhLJpEZfY22ofbjzDD/60h721rczubw6dn7hgfsqGLIQQQ3HDzIQQ40tx1KqOGNuS21d07Dnewh+2HiNH+5kx0GZOKgXTZ531PCGEcIIEE0I4ISLVcfaqjsnFeUwoNM1ftXVuRn8rWcGjsmmQJ1vLCCHcQYIJIZyw+oLwqo5gqsNGKRUxOwGwobA7fDBjdrJHKIQQcZNgQggnxJPqsNVNlBbkcF1VTvjBCgkmhBDuIcGEEE6xpzpibPz11rVzKCnIYWJRLl96z1qKWxrCD8rMhBDCRRxfzSHEuLX6Asj6nlnVcXjfWas6KiYV8shn3siAP0BeTlbU1uNzHBiwEELEJjMTQjiluAQWrw4fx0h1ZHmUCSQGBqCxLvzADFnJIYRwDwkmhHDS+kvC92OkOkJONYB/wNyfPBXyZVMvIYR7SDAhhJPsqzoO74PTJ2NfV380fF+KL4UQLiPBhBBOik51DDY70WCrl5DiSyGEy0gwIYTT4tirI7L4UoIJIYS7SDAhhNPWXAAe63/FwVIdETMTspJDCOEuEkwI4bTiUliyOnwcneoI+KHhePi4QlZyCCHcRYIJIdxgnW1VR3Sqo6kRBvrN/QmToKgkdeMSQog4SDAhhBtEpzqaT4Ufk2ZVQgiXk2BCCDeITnXYZydkJYcQwuUkmBDCLdYOsleHrOQQQricBBNCuIU91XFobzjV0WBrWCUzE0IIF5JgQgi3KJlwdqojEIhcySHBhBDChSSYEMJNolMdLaegt8ccF0+AkomODEsIIYYiwYQQbhKd6ti1JfyYzEoIIVxKggkh3KRkQuReHX98JHxfii+FEC4lwYQQbmPfq6OpMXxfZiaEEC4lwYQQbmNPddhJMCGEcCkJJoRwm+hUR5CkOYQQLiXBhBBuZE91ABQUwYTJzoxFCCGGIcGEEG4UneqYMRuUcm48QggxBAkmhHCjkgmwaFX4WFIcQggXk2BCCLe69C3h+8vXOjcOIYQYRrbTAxBCDGLdJXBrnmmpvfJcp0cjhBCDkmBCCDeTIEIIkQYkzSGEEEKIMZFgQgghhBBjIsGEEEIIIcZEggkhhBBCjIkEE0IIIYQYEwkmhBBCCDEmEkwIIYQQYkwkmBBCCCHEmEgwIYQQQogxkWBCCCGEEGMiwYQQQgghxkT25hglr9fr9BCEEEKIVNI+n0/FekBmJoQQQggxJkpr7fQYBKCU2qy1Xuf0ODKd/Dunhvw7p4b8O6eG/DsPT2YmhBBCCDEmEkwIIYQQYkwkmHCPB5wewDgh/86pIf/OqSH/zqkh/87DkJoJIYQQQoyJzEwIIYQQYkwkmBBCCCHEmEgw4RCllEcpdbtSap9SqkcpdVwpdY9SqsjpsWUSpdRCpdS/KqVeUUqdUkq1K6W2KaW+KP/WyaOUKlRK1SiltFLq+06PJ9MopSYrpf5LKXXQ+v1xSin1rFLqYqfHlimUUsVKqTuVUjut3xtNSqm/KaVuUErFbNw0nkkHTOd8G7gV+A1wD7DEOl6jlLpSax1wcnAZ5KPAJ4EngIeAfuBy4GvA+5RS52mtux0cX6b6V2CK04PIREqpOcBzQDHwP8B+YAKwEqh0bmSZQynlAf4IXAD8FLgXKASuB36M+X39eccG6EJSgOkApdQyYCfwG631tbbztwDfA/5Ba/2wU+PLJEqpdcABrXVb1PmvAV8EbtFayyfnBFJKnQNsBD6HCZTv01p/ytlRZQ6l1IvAXGCD1rrB4eFkJKXU+cDfgO9orW+3nc8F9gGTtdYTHRqeK0mawxnXAwr4TtT5B4Eu4IOpHlCm0lpvjg4kLI9YX5encjyZTimVhfk5fgp43OHhZByl1CXARcB/aq0blFI5SqlCp8eVgUqtr/X2k1rrPqAJ6Ez5iFxOgglnrAcCmE9vIVrrHmCb9bhIrpnW10ZHR5F5bgcWAzITkRxXW1+PKaWeBLqBTqXUfqWUfAhJnI1AK/A5pdR7lVKzlVKLlFJ3A2uBu5wcnBtJMOGMGUCT1ro3xmN1wBRrOk0kgfXp+SvAACDppARRSs0D/h/wr1rrIw4PJ1Mtsr4+CEwGPgL8I9AH/FwpdaNTA8skWusW4B1AM/AocBST3vgkcK3W+kEHh+dKUoDpjEIgViAB0GO7pi81wxl3vgOcB9yptX7d4bFkkh8ANcC3nB5IBiuxvrYDl1vT7iilfgMcBr6ulPqpFHAnRAewC1O8/TdM8PZJ4GGl1DVa62ecHJzbyMyEM7qAvEEey7ddIxJMKfVvmCn4B7TWdzs9nkxhTbFfBXxca93v9HgyWHDl0S+DgQSEPkk/AUwnPHshRkkptQITQDyjtb5Da/0brfX/YOpVTgAPWjOcwiLBhDPqMamMWAFFJSYFIrMSCaaUugv4EmZp18edHU3msH6OvwX8ATihlJqvlJoPzLEumWCdm+jUGDNIrfX1RIzHgis7JqVoLJnsdswHu1/bT2qtu4DfY36256Z+WO4lwYQzNmH+7TfYTyql8oHVwGYHxpTRlFJfBb4K/Ay4Scua6EQqAKYCbwUO2G7PWY9/0Dq+yYnBZZhg0fbMGI8Fz51M0VgyWbBfR6zZh+yorwIJJpzyCKCB26LOfwxTK/FQqgeUyZRSX8FUX/8cuFHyyQnXCbw3xs1rPf6UdfyEI6PLLP+HqZf4oFKqOHhSKVUBvBPTU+WgM0PLKHusrzfYT1qza9cALcCh1A7J3aRplUOUUvdicve/wUwPBztgvgy8Qf7gJYZS6pPA94FjwJcxS3LtGqWQKjmUUnMxBZnStCqBlFI3A/cDu4EfAbnAJ4AK4G1a66cdHF5GsLqMbsWkjB7C/F6ejPnANxf4pNba59gAXUimaZxzG3AEuBkzPdyEadn6FQkkEirYs2M2pi1utOcBCSZE2tBaP6CUasJ0GP03TID8d+ADWuuXHR1chtBaH1VKbcAsIb8CuA5T/LoN+GettTRkiyIzE0IIIYQYE6mZEEIIIcSYSDAhhBBCiDGRYEIIIYQQYyLBhBBCCCHGRIIJIYQQQoyJBBNCCCGEGBMJJoQQQggxJtK0Sggxbnm93rswe7Zc7vP5nnN2NEKkLwkmhBCj5vV64+l6J3+ohchwEkwIIRLh/w3x2JFUDUII4QwJJoQQY+bz+e5yegxCCOdIMCGESBl7jQIwB7Ph3WLMttq/A+70+XwnYjxvAWbX1yuAqZiN8f4M/JvP5zsQ4/oszA6PHwKWY3bWrAOeA74xyHPeg9k8aznQAzwN/LPP56sbw7csxLggqzmEEE64HfghsB34DvA6cCPwN6/XO9V+odfrXQ9sBj4IbAL+C3gF+Adgs9frXRd1fS7wFPADYBbwMPA9YAvwLuDCGOPxAr/ApGTuA3YB7wf+7PV688b6zQqR6WRmQggxZtaMQyw9Pp/vP2Kcfwtwrs/ne832Gt/GzFT8B/CP1jkF/AwoBT7o8/kesl3/fuBXwC+8Xu9Sn88XsB66C7gSeBJ4r8/n67U9J896rWhvBtb7fL6dtmsfBq4HrgEeHex7F0LIzIQQIjG+OsjtXwa5/uf2QMJyF9AGfMA2G3ABJg3yd3sgAeDz+R4BXgIWARdBKL3hBbqBj9sDCes5vT6f71SM8XzPHkhYHrS+bhjkexBCWGRmQggxZj6fT43wKc/HeI02r9e7DbgUWAJsA86xHv7rIK/zV0wgsQZ4ARN4TABe9fl89SMYz+YY545bXyeN4HWEGJdkZkII4YTGQc4Hiy8nRH1tGOT64PmJUV9HWjTZGuPcgPU1a4SvJcS4I8GEEMIJ0wY5P9362hb1dXqMawEqoq5rtb5WjnpkQogRk2BCCOGES6NPeL3eCcBqzLLMvdbpYF3FZYO8TvD8VuvrPkxAsdLr9c4Y+zCFEPGQYEII4YQPeb3eNVHn7sKkNX5pK5x8GbNs9CKrD0SIdXwJsB9TiInP5/MDPqAA+GH0sk6v15sbvfRUCDF2UoAphBizIZaGAvyfz+fbFnXuj8DLXq/3UUzdw0XW7Qi2FSA+n097vd6PAM8Aj3i93t9iZh8WAe/ENLv6sG1ZKJjW3ucCbwf2e73e31nXzQKuAu4AfjKKb1MIMQgJJoQQifDVIR47glmZYfdt4DeYvhLvBzowf+Dv9Pl8J+0X+ny+V63GVV/C9I94O6YD5i8xHTBfj7q+z+v1vhn4OPBh4COAAuqt93xppN+cEGJoSut4Nv0TQoixky2/hchMUjMhhBBCiDGRYEIIIYQQYyLBhBBCCCHGRGomhBBCCDEmMjMhhBBCiDGRYEIIIYQQYyLBhBBCCCHGRIIJIYQQQoyJBBNCCCGEGBMJJoQQQggxJv8fCMZlDp2UmNgAAAAASUVORK5CYII=\n",
- "text/plain": [
- "<Figure size 576x432 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "pwk.plot_history(history, save_as='02-history')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 6.2 - Reload and evaluate best model"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:53:38.628480Z",
- "iopub.status.busy": "2021-02-10T09:53:38.628001Z",
- "iopub.status.idle": "2021-02-10T09:55:16.648324Z",
- "shell.execute_reply": "2021-02-10T09:55:16.648855Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "WARNING:tensorflow:Layer lstm will not use cuDNN kernel since it doesn't meet the cuDNN kernel criteria. It will use generic GPU kernel as fallback when running on GPU\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "x_test / loss : 0.4160\n",
- "x_test / accuracy : 0.8304\n"
- ]
- },
- {
- "data": {
- "text/markdown": [
- "#### Accuracy donut is :"
- ],
- "text/plain": [
- "<IPython.core.display.Markdown object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- "<div class=\"comment\">Saved: ./run/figs/IMDB3-03-donut</div>"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
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\n",
- "text/plain": [
- "<Figure size 576x576 with 2 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "model = keras.models.load_model('./run/models/best_model.h5')\n",
- "\n",
- "# ---- Evaluate\n",
- "score = model.evaluate(x_test, y_test, verbose=0)\n",
- "\n",
- "print('x_test / loss : {:5.4f}'.format(score[0]))\n",
- "print('x_test / accuracy : {:5.4f}'.format(score[1]))\n",
- "\n",
- "values=[score[1], 1-score[1]]\n",
- "pwk.plot_donut(values,[\"Accuracy\",\"Errors\"], title=\"#### Accuracy donut is :\", save_as='03-donut')\n",
- "\n",
- "# ---- Confusion matrix\n",
- "\n",
- "y_sigmoid = model.predict(x_test)\n",
- "\n",
- "y_pred = y_sigmoid.copy()\n",
- "y_pred[ y_sigmoid< 0.5 ] = 0\n",
- "y_pred[ y_sigmoid>=0.5 ] = 1 \n",
- "\n",
- "pwk.display_confusion_matrix(y_test,y_pred,labels=range(2))\n",
- "pwk.plot_confusion_matrix(y_test,y_pred,range(2), figsize=(8, 8),normalize=False, save_as='04-confusion-matrix')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2021-02-10T09:55:16.652814Z",
- "iopub.status.busy": "2021-02-10T09:55:16.652229Z",
- "iopub.status.idle": "2021-02-10T09:55:16.654874Z",
- "shell.execute_reply": "2021-02-10T09:55:16.655370Z"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "End time is : Wednesday 10 February 2021, 10:55:16\n",
- "Duration is : 00:07:34 702ms\n",
- "This notebook ends here\n"
- ]
- }
- ],
- "source": [
- "pwk.end()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---\n",
- "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.7.9"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/IMDB/04-Show-vectors.ipynb b/IMDB/04-Show-vectors.ipynb
new file mode 100644
index 0000000..e3fc97c
--- /dev/null
+++ b/IMDB/04-Show-vectors.ipynb
@@ -0,0 +1,252 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
+ "\n",
+ "# <!-- TITLE --> [IMDB4] - Reload embedded vectors\n",
+ "<!-- DESC --> Retrieving embedded vectors from our trained model\n",
+ "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
+ "\n",
+ "## Objectives :\n",
+ " - The objective is to retrieve and visualize our embedded vectors\n",
+ " - For this, we will use our **previously saved model**.\n",
+ "\n",
+ "## What we're going to do :\n",
+ "\n",
+ " - Retrieve our saved model\n",
+ " - Extract our vectors\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 1 - Init python stuff"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "import tensorflow as tf\n",
+ "import tensorflow.keras as keras\n",
+ "import tensorflow.keras.datasets.imdb as imdb\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib\n",
+ "import pandas as pd\n",
+ "\n",
+ "import os,sys,h5py,json,re\n",
+ "\n",
+ "from importlib import reload\n",
+ "\n",
+ "sys.path.append('..')\n",
+ "import fidle.pwk as pwk\n",
+ "\n",
+ "run_dir = './run/IMDB2'\n",
+ "datasets_dir = pwk.init('IMDB4')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1.2 - Parameters\n",
+ "The words in the vocabulary are classified from the most frequent to the rarest. \n",
+ "`vocab_size` is the number of words we will remember in our vocabulary (the other words will be considered as unknown). \n",
+ "`review_len` is the review length \n",
+ "`dictionaries_dir` is where we will go to save our dictionaries. (./data is a good choice)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vocab_size = 10000\n",
+ "review_len = 256\n",
+ "\n",
+ "dictionaries_dir = './data'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Override parameters (batch mode) - Just forget this cell"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pwk.override('vocab_size', 'review_len', 'dictionaries_dir')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 2 - Get the embedding vectors !"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.1 - Load model and dictionaries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = keras.models.load_model(f'{run_dir}/models/best_model.h5')\n",
+ "print('Model loaded.')\n",
+ "\n",
+ "with open(f'{dictionaries_dir}/index_word.json', 'r') as fp:\n",
+ " index_word = json.load(fp)\n",
+ " index_word = { int(i):w for i,w in index_word.items() }\n",
+ " word_index = { w:int(i) for i,w in index_word.items() }\n",
+ " print('Dictionary loaded.')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.2 - Retrieve embeddings"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "embeddings = model.layers[0].get_weights()[0]\n",
+ "print('Shape of embeddings : ',embeddings.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2.3 - Build a nice dictionary"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "word_embedding = { index_word[i]:embeddings[i] for i in range(vocab_size) }"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Step 3 - Have a look !"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "word_embedding['nice']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def l2w(w1,w2):\n",
+ " v1=word_embedding[w1]\n",
+ " v2=word_embedding[w2]\n",
+ " return np.linalg.norm(v2-v1)\n",
+ "\n",
+ "def show_l2(w1,w2):\n",
+ " print(f'\\nL2 between [{w1}] and [{w2}] : ',l2w(w1,w2))\n",
+ " \n",
+ "def neighbors(w1):\n",
+ " v1=word_embedding[w1]\n",
+ " dd={}\n",
+ " for i in range(4, 1000):\n",
+ " w2=index_word[i]\n",
+ " dd[w2]=l2w(w1,w2)\n",
+ " dd= {k: v for k, v in sorted(dd.items(), key=lambda item: item[1])}\n",
+ " print(f'\\nNeighbors of [{w1}] : ', list(dd.keys())[1:15])\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "show_l2('nice', 'pleasant')\n",
+ "show_l2('nice', 'horrible')\n",
+ "\n",
+ "neighbors('horrible')\n",
+ "neighbors('great')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pwk.end()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/IMDB/04-LSTM-Keras.ipynb b/IMDB/05-LSTM-Keras.ipynb
similarity index 100%
rename from IMDB/04-LSTM-Keras.ipynb
rename to IMDB/05-LSTM-Keras.ipynb
--
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