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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Variational AutoEncoder (VAE) with MNIST\n",
"========================================\n",
"---\n",
"Formation Introduction au Deep Learning (FIDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020 \n",
"\n",
"## Episode 2 - Analyse our trained model\n",
" - Defining a VAE model\n",
" - Build the model\n",
" - Train it\n",
" - Follow the learning process with Tensorboard\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1 - Init python stuff"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FIDLE 2020 - Variational AutoEncoder (VAE)\n",
"TensorFlow version : 2.0.0\n"
]
}
],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"import tensorflow.keras.datasets.mnist as mnist\n",
"import sys, importlib\n",
"\n",
"import modules.vae\n",
"importlib.reload(modules.vae)\n",
"\n",
"print('FIDLE 2020 - Variational AutoEncoder (VAE)')\n",
"print('TensorFlow version :',tf.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2 - Get data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset loaded.\n",
"x_train shape : (60000, 28, 28, 1)\n",
"x_test_shape : (10000, 28, 28, 1)\n"
]
}
],
"source": [
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
"\n",
"x_train = x_train.astype('float32') / 255.\n",
"x_train = np.expand_dims(x_train, axis=3)\n",
"x_test = x_test.astype('float32') / 255.\n",
"x_test = np.expand_dims(x_test, axis=3)\n",
"print('Dataset loaded.')\n",
"print(f'x_train shape : {x_train.shape}\\nx_test_shape : {x_test.shape}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3 - Load best model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"vae\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"end_time = time.time()\n",
"dt = end_time-start_time\n",
"dth = str(datetime.timedelta(seconds=dt))\n",
"print(f'\\nTrain duration : {dt:.2f} sec. - {dth:}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4 - Compile it"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learning_rate = 0.0005\n",
"r_loss_factor = 1000\n",
"\n",
"vae.compile(learning_rate, r_loss_factor)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 5 - Train"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"batch_size = 100\n",
"epochs = 200\n",
"image_periodicity = 1 # for each epoch\n",
"chkpt_periodicity = 2 # for each epoch\n",
"initial_epoch = 0\n",
"dataset_size = 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"vae.train(x_train,\n",
" x_test,\n",
" batch_size = batch_size, \n",
" epochs = epochs,\n",
" image_periodicity = image_periodicity,\n",
" chkpt_periodicity = chkpt_periodicity,\n",
" initial_epoch = initial_epoch,\n",
" dataset_size = dataset_size,\n",
" lr_decay = 1\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
}