{
"cells": [
{
"cell_type": "markdown",
"id": "alpha-bahrain",
"metadata": {},
"source": [
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [SCRATCH1] - Scratchbook\n",
"<!-- DESC --> A scratchbook for small examples\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
" - Take a quick look at thousands of little things\n",
"\n",
"## Inside this scratchbook :\n",
"\n",
"[1 - LSTM Keras layer](#1---LSTM-Keras-layer)\n"
]
},
{
"cell_type": "markdown",
"id": "accessory-church",
"metadata": {},
"source": [
"# One init to rule them all"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "floppy-organic",
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras.callbacks import TensorBoard\n",
"from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
"\n",
"import numpy as np\n",
"import math, random"
]
},
{
"cell_type": "markdown",
"id": "danish-rebound",
"metadata": {},
"source": [
"## 1 - LSTM Keras layer"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "opposite-plasma",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Inputs shape is : (32, 20, 8)\n",
"Output shape is : (32, 16)\n"
]
}
],
"source": [
"inputs = tf.random.normal([32, 20, 8])\n",
"\n",
"lstm = tf.keras.layers.LSTM(16)\n",
"output = lstm(inputs)\n",
"\n",
"print('Inputs shape is : ', inputs.shape)\n",
"print('Output shape is : ', output.shape)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "forbidden-murray",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output shape : (32, 20, 18)\n",
"Memory state : (32, 18)\n",
"Carry state : (32, 18)\n"
]
}
],
"source": [
"lstm = tf.keras.layers.LSTM(18, return_sequences=True, return_state=True)\n",
"\n",
"output, memory_state, carry_state = lstm(inputs)\n",
"\n",
"print('Output shape : ', output.shape)\n",
"print('Memory state : ', memory_state.shape)\n",
"print('Carry state : ', carry_state.shape)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "verified-fruit",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(18,), dtype=float32, numpy=\n",
"array([-0.20923303, 0.00193496, 0.05929745, 0.0429938 , -0.02835345,\n",
" 0.14096233, 0.07420755, 0.1777523 , 0.1205566 , -0.03841979,\n",
" -0.02402029, 0.16098973, 0.10468155, -0.06480312, -0.02497844,\n",
" 0.09700071, -0.24351674, 0.04884451], dtype=float32)>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# --- See the last vector of the output\n",
"output[-1,-1]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "homeless-library",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(18,), dtype=float32, numpy=\n",
"array([-0.20923303, 0.00193496, 0.05929745, 0.0429938 , -0.02835345,\n",
" 0.14096233, 0.07420755, 0.1777523 , 0.1205566 , -0.03841979,\n",
" -0.02402029, 0.16098973, 0.10468155, -0.06480312, -0.02497844,\n",
" 0.09700071, -0.24351674, 0.04884451], dtype=float32)>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ---- Memory state is the last output\n",
"memory_state[-1]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "preliminary-psychiatry",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor: shape=(18,), dtype=float32, numpy=\n",
"array([-0.3245376 , 0.00296011, 0.13041827, 0.10711877, -0.05223516,\n",
" 0.4009896 , 0.21599025, 0.4260387 , 0.30799934, -0.0799172 ,\n",
" -0.06359857, 0.29457492, 0.18084048, -0.14462015, -0.04707906,\n",
" 0.15726675, -0.38622206, 0.09004797], dtype=float32)>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"carry_state[-1]"
]
},
{
"cell_type": "markdown",
"id": "41d326b2-376e-49d6-9429-07016d98dc09",
"metadata": {},
"source": [
"## 2 - TimeseriesGenerator"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "42276389-4ea6-42d1-93bc-6650062ef86a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of batch : 10\n",
"\n",
"#0 : [[1 2 3 4 5]] => [6]\n",
"#1 : [[2 3 4 5 6]] => [7]\n",
"#2 : [[3 4 5 6 7]] => [8]\n",
"#3 : [[4 5 6 7 8]] => [9]\n",
"#4 : [[5 6 7 8 9]] => [10]\n",
"#5 : [[ 6 7 8 9 10]] => [11]\n",
"#6 : [[ 7 8 9 10 11]] => [12]\n",
"#7 : [[ 8 9 10 11 12]] => [13]\n",
"#8 : [[ 9 10 11 12 13]] => [14]\n",
"#9 : [[10 11 12 13 14]] => [15]\n"
]
}
],
"source": [
"from keras.preprocessing.sequence import TimeseriesGenerator\n",
"\n",
"# ---- Define a dataset\n",
"\n",
"series = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])\n",
"\n",
"# ---- Generator\n",
"\n",
"generator = TimeseriesGenerator(series, series, length=5, batch_size=1)\n",
"\n",
"# ---- Samples\n",
"\n",
"nb_batch = len(generator)\n",
"\n",
"print(f'Number of batch : {nb_batch}\\n')\n",
"for i in range(nb_batch):\n",
" x, y = generator[i]\n",
" print(f'#{i} : {x} => {y}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4d94892b-d3a5-448d-aa2b-28c3a01a4b72",
"metadata": {},
"outputs": [],
"source": []
}
],
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