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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Text Embedding - IMDB dataset\n",
"=============================\n",
"---\n",
"Introduction au Deep Learning (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
"\n",
"## Text classification using **Text embedding** :\n",
"\n",
"The objective is to guess whether film reviews are **positive or negative** based on the analysis of the text. \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",
"https://www.liip.ch/en/blog/sentiment-detection-with-keras-word-embeddings-and-lstm-deep-learning-networks\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": [
"## 1/ Init python stuff"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"IDLE 2020 - Practical Work Module\n",
" Version : 0.2\n",
" Run time : Friday 24 January 2020, 09:13:23\n",
" Matplotlib style : fidle/talk.mplstyle\n",
" TensorFlow version : 2.0.0\n",
" Keras version : 2.2.4-tf\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"from tensorflow import keras\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"\n",
"import fidle.pwk as ooo\n",
"\n",
"ooo.init()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2/ Retrieve data\n",
"\n",
"**From Keras :**\n",
"This IMDb dataset can bet get directly from [Keras datasets](https://www.tensorflow.org/api_docs/python/tf/keras/datasets) \n",
"\n",
"Due to their nature, textual data can be somewhat complex.\n",
"\n",
"#### Data structure : \n",
"The dataset is composed of 2 parts: **reviews** and **opinions** (positive/negative), with a **dictionary**\n",
"\n",
" - dataset = (reviews, opinions)\n",
" - reviews = \\[ review_0, review_1, ...\\]\n",
" - review_i = [ int1, int2, ...] where int_i is the index of the word in the dictionary.\n",
" - opinions = \\[ int0, int1, ...\\] where int_j == 0 if opinion is negative or 1 if opinion is positive.\n",
" - dictionary = \\[ mot1:int1, mot2:int2, ... ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1/ Get dataset\n",
"For simplicity, we will use a pre-formatted dataset. \n",
"See : https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb/load_data \n",
"\n",
"However, Keras offers some usefull tools for formatting textual data. \n",
"See : https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"imdb = keras.datasets.imdb\n",
"\n",
"# ----- Retrieve x,y\n",
"#\n",
"(x_train, y_train), (x_test, y_test) = imdb.load_data( num_words = 10000, \n",
" skip_top = 0,\n",
" maxlen = None,\n",
" seed = 42,\n",
" start_char = 1,\n",
" oov_char = 2,\n",
" index_from = 3, )"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 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(\" 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": [
"### 2.2/ 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"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"# ---- Retrieve dictionary {word:index}\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": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Dictionary size : 88587\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",
"\n",
"In real words :\n",
"\n",
" <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",
"print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])\n",
"print('\\nIn real words :\\n\\n', dataset2text(x_train[12]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.3/ Have a look for neurons"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,1)\n",
"fig.set_size_inches(12,6)\n",
"ax.hist([len(i) for i in x_train],80 )\n",
"ax.set_title('Distribution of reviews by size')\n",
"ax.set_xlim(left=0,right=1500)\n",
"ax.set_xlabel(\"Review's size\")\n",
"ax.set_ylabel(\"Review's count\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3 - Preprocess the data\n",
"In order to be processed by an NN, all entries must have the same length. \n",
"We chose a maxlength of **max_len** \n",
"We will therefore complete them with a padding (of \\<pad\\>\\) "
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Review example (x_train[12]) :\n",
"\n",
" [ 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",
"\n",
"In real words :\n",
"\n",
" <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": [
"max_len = 256\n",
"\n",
"x_train = keras.preprocessing.sequence.pad_sequences(x_train,\n",
" value = 0,\n",
" padding = 'post',\n",
" maxlen = max_len)\n",
"\n",
"x_test = keras.preprocessing.sequence.pad_sequences(x_valid,\n",
" value = 0 ,\n",
" padding = 'post',\n",
" maxlen = max_len)\n",
"\n",
"print('\\nReview example (x_train[12]) :\\n\\n',x_train[12])\n",
"print('\\nIn real words :\\n\\n', dataset2text(x_train[12]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4 - Building the model\n",
"Few remarks :\n",
"1. GlobalAveragePooling1D : Fait un pooling sur la seconde dimension (batch_size, steps, features) -> (batch_size, features) \n",
"Autrement dit : on moyenne l'ensemble des mots d'une phrase\n",
"2. 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 : https://stats.stackexchange.com/questions/324992/how-the-embedding-layer-is-trained-in-keras-embedding-layer"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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": [
"\n",
"vocab_size = 10000\n",
"n_neurons = 32 # 200 pour voir overfiting\n",
"\n",
"keras.backend.clear_session()\n",
"\n",
"model = keras.Sequential()\n",
"model.add(keras.layers.Embedding(vocab_size, n_neurons, input_length=256))\n",
"model.add(keras.layers.GlobalAveragePooling1D())\n",
"model.add(keras.layers.Dense(n_neurons, activation=tf.nn.relu))\n",
"model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))\n",
"\n",
"model.summary()\n",
"\n",
"model.compile(optimizer='adam',\n",
" loss='binary_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"callbacks=[]\n",
"callbacks.append( keras.callbacks.ModelCheckpoint(ooo.get_check_dir()+'/model{epoch:02d}-{val_acc:.3f}.h5') )\n",
"callbacks.append( keras.callbacks.ModelCheckpoint(ooo.get_model_dir()+'/best_model.h5', save_best_only=True) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Entrainement"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/30\n",
"25000/25000 [==============================] - 1s 60us/sample - loss: 0.6882 - acc: 0.6171 - val_loss: 0.6787 - val_acc: 0.6901\n",
"Epoch 2/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.6512 - acc: 0.7332 - val_loss: 0.6168 - val_acc: 0.7786\n",
"Epoch 3/30\n",
"25000/25000 [==============================] - 1s 51us/sample - loss: 0.5554 - acc: 0.8075 - val_loss: 0.5076 - val_acc: 0.8237\n",
"Epoch 4/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.4378 - acc: 0.8525 - val_loss: 0.4123 - val_acc: 0.8485\n",
"Epoch 5/30\n",
"25000/25000 [==============================] - 1s 49us/sample - loss: 0.3519 - acc: 0.8786 - val_loss: 0.3565 - val_acc: 0.8620\n",
"Epoch 6/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.2993 - acc: 0.8922 - val_loss: 0.3269 - val_acc: 0.8690\n",
"Epoch 7/30\n",
"25000/25000 [==============================] - 1s 49us/sample - loss: 0.2654 - acc: 0.9026 - val_loss: 0.3090 - val_acc: 0.8749\n",
"Epoch 8/30\n",
"25000/25000 [==============================] - 1s 49us/sample - loss: 0.2401 - acc: 0.9123 - val_loss: 0.2997 - val_acc: 0.8769\n",
"Epoch 9/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.2201 - acc: 0.9212 - val_loss: 0.2912 - val_acc: 0.8803\n",
"Epoch 10/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.2027 - acc: 0.9272 - val_loss: 0.2880 - val_acc: 0.8826\n",
"Epoch 11/30\n",
"25000/25000 [==============================] - 1s 49us/sample - loss: 0.1886 - acc: 0.9330 - val_loss: 0.2868 - val_acc: 0.8816\n",
"Epoch 12/30\n",
"25000/25000 [==============================] - 1s 49us/sample - loss: 0.1763 - acc: 0.9377 - val_loss: 0.2878 - val_acc: 0.8825\n",
"Epoch 13/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.1656 - acc: 0.9428 - val_loss: 0.2903 - val_acc: 0.8816\n",
"Epoch 14/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.1560 - acc: 0.9474 - val_loss: 0.2935 - val_acc: 0.8826\n",
"Epoch 15/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.1469 - acc: 0.9507 - val_loss: 0.2976 - val_acc: 0.8808\n",
"Epoch 16/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.1387 - acc: 0.9537 - val_loss: 0.3032 - val_acc: 0.8813\n",
"Epoch 17/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.1316 - acc: 0.9575 - val_loss: 0.3092 - val_acc: 0.8790\n",
"Epoch 18/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.1248 - acc: 0.9595 - val_loss: 0.3190 - val_acc: 0.8749\n",
"Epoch 19/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.1186 - acc: 0.9623 - val_loss: 0.3224 - val_acc: 0.8767\n",
"Epoch 20/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.1120 - acc: 0.9651 - val_loss: 0.3299 - val_acc: 0.8758\n",
"Epoch 21/30\n",
"25000/25000 [==============================] - 1s 49us/sample - loss: 0.1075 - acc: 0.9663 - val_loss: 0.3381 - val_acc: 0.8756\n",
"Epoch 22/30\n",
"25000/25000 [==============================] - 1s 49us/sample - loss: 0.1016 - acc: 0.9690 - val_loss: 0.3473 - val_acc: 0.8744\n",
"Epoch 23/30\n",
"25000/25000 [==============================] - 1s 49us/sample - loss: 0.0966 - acc: 0.9718 - val_loss: 0.3602 - val_acc: 0.8706\n",
"Epoch 24/30\n",
"25000/25000 [==============================] - 1s 49us/sample - loss: 0.0923 - acc: 0.9736 - val_loss: 0.3655 - val_acc: 0.8712\n",
"Epoch 25/30\n",
"25000/25000 [==============================] - 1s 50us/sample - loss: 0.0872 - acc: 0.9756 - val_loss: 0.3773 - val_acc: 0.8683\n",
"Epoch 26/30\n",
"25000/25000 [==============================] - 1s 49us/sample - loss: 0.0836 - acc: 0.9776 - val_loss: 0.3853 - val_acc: 0.8688\n",
"Epoch 27/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.0791 - acc: 0.9797 - val_loss: 0.3969 - val_acc: 0.8666\n",
"Epoch 28/30\n",
"25000/25000 [==============================] - 1s 49us/sample - loss: 0.0758 - acc: 0.9806 - val_loss: 0.4066 - val_acc: 0.8658\n",
"Epoch 29/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.0721 - acc: 0.9814 - val_loss: 0.4177 - val_acc: 0.8642\n",
"Epoch 30/30\n",
"25000/25000 [==============================] - 1s 48us/sample - loss: 0.0685 - acc: 0.9834 - val_loss: 0.4281 - val_acc: 0.8635\n",
"CPU times: user 51.5 s, sys: 2.2 s, total: 53.7 s\n",
"Wall time: 37.1 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_valid, y_valid),\n",
" verbose=1,\n",
" callbacks=callbacks)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evalulation du modèle"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Load best model... Done.\n",
"\n",
"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",
"None\n",
"25000/25000 [==============================] - 1s 20us/sample - loss: 0.2868 - acc: 0.8816\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1728x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"print('Load best model... ', end='')\n",
"model = keras.models.load_model(ooo.get_model_dir()+'/best_model.h5')\n",
"print('Done.\\n')\n",
"\n",
"print(model.summary())\n",
"# ------ Evaluation du résultat ------------------------------------\n",
"\n",
"results = model.evaluate(x_valid, y_valid)\n",
"accuracy = results[1]\n",
"\n",
"fig, axs = plt.subplots()\n",
"fig.set_size_inches(8,8)\n",
"axs.pie([accuracy,1-accuracy], explode=[0,0.1], labels=[\"\",\"Errors\"], \n",
" autopct='%1.1f%%', shadow=False, startangle=70, \n",
" colors=[\"lightsteelblue\",\"coral\"], textprops={'fontsize': 18})\n",
"ooo.save_fig('accuracy')\n",
"plt.show()\n",
"\n",
"\n",
"# ------ Statistiques d'apprentissage -------------------------------\n",
"\n",
"acc = history.history['acc']\n",
"val_acc = history.history['val_acc']\n",
"loss = history.history['loss']\n",
"val_loss = history.history['val_loss']\n",
"x_epochs = range(1, len(acc) + 1)\n",
"\n",
"fig, (ax1,ax2) = plt.subplots(1,2)\n",
"fig.set_size_inches(24,8)\n",
"ax1.plot(x_epochs, loss, 'o', fillstyle='full', markersize=6, color='steelblue', label='Training loss')\n",
"ax1.plot(x_epochs, val_loss, '-', fillstyle='none', markersize=6, color='red', label='Validation loss')\n",
"ax1.axhline(y=results[0], linestyle='--', linewidth=1, color='grey', label='Final test loss({:5.3})'.format(results[0]))\n",
"ax1.set_title('Training and validation loss')\n",
"ax1.set_xlabel('Epochs')\n",
"ax1.set_ylabel('Loss')\n",
"ax1.legend()\n",
"\n",
"ax2.plot(x_epochs, acc, 'o', fillstyle='full', markersize=6, color='steelblue', label='Training accuracy')\n",
"ax2.plot(x_epochs, val_acc, '-', fillstyle='none', markersize=6, color='red', label='Validation accuracy')\n",
"ax2.axhline(y=results[1], linestyle='--', linewidth=1, color='grey', label='Final test accuracy ({:5.3})'.format(results[1]))\n",
"ax2.set_title('Training and validation accuracy')\n",
"ax2.set_xlabel('Epochs')\n",
"ax2.set_ylabel('Accuracy')\n",
"ax2.legend()\n",
"\n",
"ooo.save_fig(\"Evaluations\")\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}