From 80f47375d8b213fc8bc304220d064c7269363514 Mon Sep 17 00:00:00 2001
From: "Jean-Luc.Parouty@grenoble-inp.fr" <paroutyj@f-dahu.u-ga.fr>
Date: Wed, 4 Mar 2020 18:42:29 +0100
Subject: [PATCH] Update VAE
Former-commit-id: c5df69ff7d9ba1fa60a1019cfe29bc43c361aaee
---
VAE/01-VAE-with-MNIST.ipynb | 341 +++++++++++++++++-
VAE/02-VAE-with-MNIST-post.ipynb | 4 +-
VAE/03-About-CelebA.ipynb | 6 +-
VAE/04-Prepare-CelebA-batch.ipynb | 4 +-
VAE/05-Check-CelebA.ipynb | 2 +-
VAE/06-VAE-with-CelebA-s.ipynb | 6 +-
VAE/07-VAE-with-CelebA-m.ipynb | 6 +-
...b => 07-VAE-with-CelebA-m.nbconvert.ipynb} | 0
...ost.ipynb => 08-VAE-withCelebA-post.ipynb} | 6 +-
9 files changed, 342 insertions(+), 33 deletions(-)
rename VAE/{08-VAE-with-CelebA-m.nbconvert.ipynb => 07-VAE-with-CelebA-m.nbconvert.ipynb} (100%)
rename VAE/{12-VAE-withCelebA-post.ipynb => 08-VAE-withCelebA-post.ipynb} (99%)
diff --git a/VAE/01-VAE-with-MNIST.ipynb b/VAE/01-VAE-with-MNIST.ipynb
index 576132a..5ab2841 100644
--- a/VAE/01-VAE-with-MNIST.ipynb
+++ b/VAE/01-VAE-with-MNIST.ipynb
@@ -7,13 +7,13 @@
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [VAE1] - Variational AutoEncoder (VAE) with MNIST\n",
- "<!-- DESC --> First generative network experience with the MNIST dataset\n",
+ "<!-- DESC --> Episode 1 : Model construction and Training\n",
"\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
- " - Understanding and implementing a variational autoencoder neurals network (VAE)\n",
- " - Understanding a more advanced programming model\n",
+ " - Understanding and implementing a **variational autoencoder** neurals network (VAE)\n",
+ " - Understanding a more **advanced programming model**\n",
"\n",
"The calculation needs being important, it is preferable to use a very simple dataset such as MNIST to start with.\n",
"\n",
@@ -34,9 +34,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "FIDLE 2020 - Variational AutoEncoder (VAE)\n",
+ "TensorFlow version : 2.0.0\n",
+ "VAE version : 1.28\n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"import sys, importlib\n",
@@ -59,9 +70,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset loaded.\n",
+ "Normalized.\n",
+ "Reshaped to (60000, 28, 28, 1)\n"
+ ]
+ }
+ ],
"source": [
"(x_train, y_train), (x_test, y_test) = Loader_MNIST.load()"
]
@@ -70,20 +91,89 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Step 3 - Get VAE model"
+ "## Step 3 - Get VAE model\n",
+ "Nous allons instancier notre modèle VAE. \n",
+ "Ce dernier est défini avec une classe python pour alléger notre code. \n",
+ "La description de nos deux réseaux est donnée en paramètre. \n",
+ "Notre modèle sera sauvegardé dans le dossier : ./run/<tag>"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model initialized.\n",
+ "Outputs will be in : ./run/MNIST.001\n",
+ "\n",
+ " ---------- Encoder -------------------------------------------------- \n",
+ "\n",
+ "Model: \"model_13\"\n",
+ "__________________________________________________________________________________________________\n",
+ "Layer (type) Output Shape Param # Connected to \n",
+ "==================================================================================================\n",
+ "encoder_input (InputLayer) [(None, 28, 28, 1)] 0 \n",
+ "__________________________________________________________________________________________________\n",
+ "conv2d_12 (Conv2D) (None, 28, 28, 32) 320 encoder_input[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "conv2d_13 (Conv2D) (None, 14, 14, 64) 18496 conv2d_12[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "conv2d_14 (Conv2D) (None, 7, 7, 64) 36928 conv2d_13[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "conv2d_15 (Conv2D) (None, 7, 7, 64) 36928 conv2d_14[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "flatten_3 (Flatten) (None, 3136) 0 conv2d_15[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "mu (Dense) (None, 2) 6274 flatten_3[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "log_var (Dense) (None, 2) 6274 flatten_3[0][0] \n",
+ "__________________________________________________________________________________________________\n",
+ "encoder_output (Lambda) (None, 2) 0 mu[0][0] \n",
+ " log_var[0][0] \n",
+ "==================================================================================================\n",
+ "Total params: 105,220\n",
+ "Trainable params: 105,220\n",
+ "Non-trainable params: 0\n",
+ "__________________________________________________________________________________________________\n",
+ "\n",
+ " ---------- Encoder -------------------------------------------------- \n",
+ "\n",
+ "Model: \"model_14\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "decoder_input (InputLayer) [(None, 2)] 0 \n",
+ "_________________________________________________________________\n",
+ "dense_3 (Dense) (None, 3136) 9408 \n",
+ "_________________________________________________________________\n",
+ "reshape_3 (Reshape) (None, 7, 7, 64) 0 \n",
+ "_________________________________________________________________\n",
+ "conv2d_transpose_12 (Conv2DT (None, 7, 7, 64) 36928 \n",
+ "_________________________________________________________________\n",
+ "conv2d_transpose_13 (Conv2DT (None, 14, 14, 64) 36928 \n",
+ "_________________________________________________________________\n",
+ "conv2d_transpose_14 (Conv2DT (None, 28, 28, 32) 18464 \n",
+ "_________________________________________________________________\n",
+ "conv2d_transpose_15 (Conv2DT (None, 28, 28, 1) 289 \n",
+ "=================================================================\n",
+ "Total params: 102,017\n",
+ "Trainable params: 102,017\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n",
+ "Config saved in : ./run/MNIST.001/models/vae_config.json\n"
+ ]
+ }
+ ],
"source": [
"tag = 'MNIST.001'\n",
"\n",
"input_shape = (28,28,1)\n",
"z_dim = 2\n",
- "verbose = 0\n",
+ "verbose = 1\n",
"\n",
"encoder= [ {'type':'Conv2D', 'filters':32, 'kernel_size':(3,3), 'strides':1, 'padding':'same', 'activation':'relu'},\n",
" {'type':'Conv2D', 'filters':64, 'kernel_size':(3,3), 'strides':2, 'padding':'same', 'activation':'relu'},\n",
@@ -115,9 +205,17 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Compiled.\n"
+ ]
+ }
+ ],
"source": [
"r_loss_factor = 1000\n",
"\n",
@@ -133,7 +231,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -145,9 +243,220 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train on 60000 samples, validate on 10000 samples\n",
+ "Epoch 1/100\n",
+ " 100/60000 [..............................] - ETA: 1:16:33 - loss: 231.5715 - vae_r_loss: 231.5706 - vae_kl_loss: 8.8929e-04WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.261125). Check your callbacks.\n",
+ "60000/60000 [==============================] - 16s 259us/sample - loss: 63.3479 - vae_r_loss: 60.5303 - vae_kl_loss: 2.8176 - val_loss: 52.8295 - val_vae_r_loss: 49.3652 - val_vae_kl_loss: 3.4643\n",
+ "Epoch 2/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 50.9248 - vae_r_loss: 46.7790 - vae_kl_loss: 4.1458 - val_loss: 49.8544 - val_vae_r_loss: 45.4392 - val_vae_kl_loss: 4.4152\n",
+ "Epoch 3/100\n",
+ "60000/60000 [==============================] - 8s 127us/sample - loss: 48.9337 - vae_r_loss: 44.4075 - vae_kl_loss: 4.5262 - val_loss: 48.1853 - val_vae_r_loss: 43.6399 - val_vae_kl_loss: 4.5454\n",
+ "Epoch 4/100\n",
+ "60000/60000 [==============================] - 8s 128us/sample - loss: 47.8006 - vae_r_loss: 43.0700 - vae_kl_loss: 4.7306 - val_loss: 47.6048 - val_vae_r_loss: 42.8379 - val_vae_kl_loss: 4.7669\n",
+ "Epoch 5/100\n",
+ "60000/60000 [==============================] - 8s 129us/sample - loss: 47.1728 - vae_r_loss: 42.3272 - vae_kl_loss: 4.8456 - val_loss: 47.1257 - val_vae_r_loss: 42.5182 - val_vae_kl_loss: 4.6075\n",
+ "Epoch 6/100\n",
+ "60000/60000 [==============================] - 8s 128us/sample - loss: 46.6197 - vae_r_loss: 41.6877 - vae_kl_loss: 4.9320 - val_loss: 46.6778 - val_vae_r_loss: 41.8177 - val_vae_kl_loss: 4.8601\n",
+ "Epoch 7/100\n",
+ "60000/60000 [==============================] - 8s 127us/sample - loss: 46.2559 - vae_r_loss: 41.2509 - vae_kl_loss: 5.0050 - val_loss: 46.8471 - val_vae_r_loss: 41.7164 - val_vae_kl_loss: 5.1308\n",
+ "Epoch 8/100\n",
+ "60000/60000 [==============================] - 8s 129us/sample - loss: 45.9705 - vae_r_loss: 40.9047 - vae_kl_loss: 5.0658 - val_loss: 46.1138 - val_vae_r_loss: 40.8994 - val_vae_kl_loss: 5.2144\n",
+ "Epoch 9/100\n",
+ "60000/60000 [==============================] - 8s 128us/sample - loss: 45.7034 - vae_r_loss: 40.5799 - vae_kl_loss: 5.1235 - val_loss: 45.9027 - val_vae_r_loss: 40.6272 - val_vae_kl_loss: 5.2755\n",
+ "Epoch 10/100\n",
+ "60000/60000 [==============================] - 8s 127us/sample - loss: 45.4206 - vae_r_loss: 40.2416 - vae_kl_loss: 5.1790 - val_loss: 45.8569 - val_vae_r_loss: 40.7173 - val_vae_kl_loss: 5.1396\n",
+ "Epoch 11/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 45.2388 - vae_r_loss: 40.0393 - vae_kl_loss: 5.1995 - val_loss: 45.5438 - val_vae_r_loss: 40.2990 - val_vae_kl_loss: 5.2448\n",
+ "Epoch 12/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 45.0701 - vae_r_loss: 39.8384 - vae_kl_loss: 5.2317 - val_loss: 45.1382 - val_vae_r_loss: 39.8545 - val_vae_kl_loss: 5.2838\n",
+ "Epoch 13/100\n",
+ "60000/60000 [==============================] - 6s 102us/sample - loss: 44.9229 - vae_r_loss: 39.6576 - vae_kl_loss: 5.2653 - val_loss: 45.2182 - val_vae_r_loss: 39.7051 - val_vae_kl_loss: 5.5130\n",
+ "Epoch 14/100\n",
+ "60000/60000 [==============================] - 6s 101us/sample - loss: 44.7520 - vae_r_loss: 39.4462 - vae_kl_loss: 5.3058 - val_loss: 44.9645 - val_vae_r_loss: 39.6967 - val_vae_kl_loss: 5.2678\n",
+ "Epoch 15/100\n",
+ "60000/60000 [==============================] - 7s 112us/sample - loss: 44.6182 - vae_r_loss: 39.2917 - vae_kl_loss: 5.3266 - val_loss: 45.1804 - val_vae_r_loss: 39.8132 - val_vae_kl_loss: 5.3673\n",
+ "Epoch 16/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 44.5127 - vae_r_loss: 39.1662 - vae_kl_loss: 5.3465 - val_loss: 44.7445 - val_vae_r_loss: 39.5578 - val_vae_kl_loss: 5.1867\n",
+ "Epoch 17/100\n",
+ "60000/60000 [==============================] - 7s 122us/sample - loss: 44.3639 - vae_r_loss: 38.9776 - vae_kl_loss: 5.3864 - val_loss: 45.0144 - val_vae_r_loss: 39.4877 - val_vae_kl_loss: 5.5267\n",
+ "Epoch 18/100\n",
+ "60000/60000 [==============================] - 8s 131us/sample - loss: 44.2794 - vae_r_loss: 38.8709 - vae_kl_loss: 5.4085 - val_loss: 44.7394 - val_vae_r_loss: 39.3797 - val_vae_kl_loss: 5.3597\n",
+ "Epoch 19/100\n",
+ "60000/60000 [==============================] - 8s 127us/sample - loss: 44.1593 - vae_r_loss: 38.7339 - vae_kl_loss: 5.4254 - val_loss: 44.8999 - val_vae_r_loss: 39.5979 - val_vae_kl_loss: 5.3020\n",
+ "Epoch 20/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 44.0882 - vae_r_loss: 38.6534 - vae_kl_loss: 5.4348 - val_loss: 44.5456 - val_vae_r_loss: 39.1507 - val_vae_kl_loss: 5.3949\n",
+ "Epoch 21/100\n",
+ "60000/60000 [==============================] - 8s 125us/sample - loss: 43.9450 - vae_r_loss: 38.4855 - vae_kl_loss: 5.4596 - val_loss: 44.6002 - val_vae_r_loss: 39.1242 - val_vae_kl_loss: 5.4760\n",
+ "Epoch 22/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 43.9054 - vae_r_loss: 38.4365 - vae_kl_loss: 5.4689 - val_loss: 44.7089 - val_vae_r_loss: 39.3456 - val_vae_kl_loss: 5.3633\n",
+ "Epoch 23/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 43.8545 - vae_r_loss: 38.3684 - vae_kl_loss: 5.4860 - val_loss: 44.4804 - val_vae_r_loss: 39.0637 - val_vae_kl_loss: 5.4167\n",
+ "Epoch 24/100\n",
+ "60000/60000 [==============================] - 8s 129us/sample - loss: 43.7724 - vae_r_loss: 38.2644 - vae_kl_loss: 5.5080 - val_loss: 44.2626 - val_vae_r_loss: 38.7498 - val_vae_kl_loss: 5.5128\n",
+ "Epoch 25/100\n",
+ "60000/60000 [==============================] - 7s 120us/sample - loss: 43.6945 - vae_r_loss: 38.1870 - vae_kl_loss: 5.5075 - val_loss: 44.5109 - val_vae_r_loss: 38.9870 - val_vae_kl_loss: 5.5240\n",
+ "Epoch 26/100\n",
+ "60000/60000 [==============================] - 6s 103us/sample - loss: 43.6235 - vae_r_loss: 38.0840 - vae_kl_loss: 5.5396 - val_loss: 44.4880 - val_vae_r_loss: 38.9770 - val_vae_kl_loss: 5.5110\n",
+ "Epoch 27/100\n",
+ "60000/60000 [==============================] - 6s 104us/sample - loss: 43.5726 - vae_r_loss: 38.0224 - vae_kl_loss: 5.5502 - val_loss: 44.3887 - val_vae_r_loss: 39.0129 - val_vae_kl_loss: 5.3758\n",
+ "Epoch 28/100\n",
+ "60000/60000 [==============================] - 7s 125us/sample - loss: 43.4963 - vae_r_loss: 37.9367 - vae_kl_loss: 5.5596 - val_loss: 44.2672 - val_vae_r_loss: 38.7244 - val_vae_kl_loss: 5.5427\n",
+ "Epoch 29/100\n",
+ "60000/60000 [==============================] - 7s 125us/sample - loss: 43.4534 - vae_r_loss: 37.8870 - vae_kl_loss: 5.5663 - val_loss: 44.2616 - val_vae_r_loss: 38.6397 - val_vae_kl_loss: 5.6219\n",
+ "Epoch 30/100\n",
+ "60000/60000 [==============================] - 8s 125us/sample - loss: 43.4108 - vae_r_loss: 37.8235 - vae_kl_loss: 5.5873 - val_loss: 44.0783 - val_vae_r_loss: 38.4805 - val_vae_kl_loss: 5.5978\n",
+ "Epoch 31/100\n",
+ "60000/60000 [==============================] - 8s 127us/sample - loss: 43.3281 - vae_r_loss: 37.7423 - vae_kl_loss: 5.5858 - val_loss: 44.2450 - val_vae_r_loss: 38.6322 - val_vae_kl_loss: 5.6128\n",
+ "Epoch 32/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 43.3066 - vae_r_loss: 37.6978 - vae_kl_loss: 5.6089 - val_loss: 44.1004 - val_vae_r_loss: 38.3046 - val_vae_kl_loss: 5.7958\n",
+ "Epoch 33/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 43.2654 - vae_r_loss: 37.6541 - vae_kl_loss: 5.6113 - val_loss: 44.0908 - val_vae_r_loss: 38.7236 - val_vae_kl_loss: 5.3672\n",
+ "Epoch 34/100\n",
+ "60000/60000 [==============================] - 8s 128us/sample - loss: 43.2006 - vae_r_loss: 37.5831 - vae_kl_loss: 5.6176 - val_loss: 44.3048 - val_vae_r_loss: 38.5594 - val_vae_kl_loss: 5.7453\n",
+ "Epoch 35/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 43.1657 - vae_r_loss: 37.5287 - vae_kl_loss: 5.6370 - val_loss: 44.3178 - val_vae_r_loss: 38.7578 - val_vae_kl_loss: 5.5600\n",
+ "Epoch 36/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 43.1199 - vae_r_loss: 37.4728 - vae_kl_loss: 5.6471 - val_loss: 43.9947 - val_vae_r_loss: 38.4591 - val_vae_kl_loss: 5.5356\n",
+ "Epoch 37/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 43.0550 - vae_r_loss: 37.4052 - vae_kl_loss: 5.6499 - val_loss: 44.1075 - val_vae_r_loss: 38.4646 - val_vae_kl_loss: 5.6429\n",
+ "Epoch 38/100\n",
+ "60000/60000 [==============================] - 7s 125us/sample - loss: 43.0274 - vae_r_loss: 37.3612 - vae_kl_loss: 5.6662 - val_loss: 44.1100 - val_vae_r_loss: 38.3107 - val_vae_kl_loss: 5.7994\n",
+ "Epoch 39/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 43.0046 - vae_r_loss: 37.3353 - vae_kl_loss: 5.6693 - val_loss: 43.9765 - val_vae_r_loss: 38.1482 - val_vae_kl_loss: 5.8284\n",
+ "Epoch 40/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 42.9831 - vae_r_loss: 37.2976 - vae_kl_loss: 5.6855 - val_loss: 44.1622 - val_vae_r_loss: 38.5135 - val_vae_kl_loss: 5.6488\n",
+ "Epoch 41/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 42.9614 - vae_r_loss: 37.2653 - vae_kl_loss: 5.6961 - val_loss: 43.9546 - val_vae_r_loss: 38.3111 - val_vae_kl_loss: 5.6435\n",
+ "Epoch 42/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 42.8949 - vae_r_loss: 37.1996 - vae_kl_loss: 5.6953 - val_loss: 44.0486 - val_vae_r_loss: 38.4427 - val_vae_kl_loss: 5.6059\n",
+ "Epoch 43/100\n",
+ "60000/60000 [==============================] - 8s 125us/sample - loss: 42.8460 - vae_r_loss: 37.1553 - vae_kl_loss: 5.6907 - val_loss: 43.9027 - val_vae_r_loss: 38.3105 - val_vae_kl_loss: 5.5921\n",
+ "Epoch 44/100\n",
+ "60000/60000 [==============================] - 8s 125us/sample - loss: 42.8550 - vae_r_loss: 37.1409 - vae_kl_loss: 5.7141 - val_loss: 44.0527 - val_vae_r_loss: 38.4803 - val_vae_kl_loss: 5.5724\n",
+ "Epoch 45/100\n",
+ "60000/60000 [==============================] - 8s 125us/sample - loss: 42.7725 - vae_r_loss: 37.0586 - vae_kl_loss: 5.7139 - val_loss: 43.9695 - val_vae_r_loss: 38.1840 - val_vae_kl_loss: 5.7855\n",
+ "Epoch 46/100\n",
+ "60000/60000 [==============================] - 8s 127us/sample - loss: 42.7583 - vae_r_loss: 37.0431 - vae_kl_loss: 5.7152 - val_loss: 43.8917 - val_vae_r_loss: 38.4005 - val_vae_kl_loss: 5.4912\n",
+ "Epoch 47/100\n",
+ "60000/60000 [==============================] - 7s 112us/sample - loss: 42.7553 - vae_r_loss: 37.0322 - vae_kl_loss: 5.7231 - val_loss: 43.8994 - val_vae_r_loss: 38.2113 - val_vae_kl_loss: 5.6880\n",
+ "Epoch 48/100\n",
+ "60000/60000 [==============================] - 8s 125us/sample - loss: 42.7218 - vae_r_loss: 36.9855 - vae_kl_loss: 5.7363 - val_loss: 43.6855 - val_vae_r_loss: 37.9163 - val_vae_kl_loss: 5.7693\n",
+ "Epoch 49/100\n",
+ "60000/60000 [==============================] - 7s 125us/sample - loss: 42.6747 - vae_r_loss: 36.9308 - vae_kl_loss: 5.7439 - val_loss: 43.9899 - val_vae_r_loss: 38.4054 - val_vae_kl_loss: 5.5844\n",
+ "Epoch 50/100\n",
+ "60000/60000 [==============================] - 7s 125us/sample - loss: 42.6405 - vae_r_loss: 36.8940 - vae_kl_loss: 5.7464 - val_loss: 43.9136 - val_vae_r_loss: 38.1742 - val_vae_kl_loss: 5.7394\n",
+ "Epoch 51/100\n",
+ "60000/60000 [==============================] - 7s 119us/sample - loss: 42.6486 - vae_r_loss: 36.8904 - vae_kl_loss: 5.7582 - val_loss: 43.7776 - val_vae_r_loss: 37.8941 - val_vae_kl_loss: 5.8834\n",
+ "Epoch 52/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 42.5716 - vae_r_loss: 36.8282 - vae_kl_loss: 5.7433 - val_loss: 43.7207 - val_vae_r_loss: 37.9595 - val_vae_kl_loss: 5.7611\n",
+ "Epoch 53/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 42.5695 - vae_r_loss: 36.8049 - vae_kl_loss: 5.7646 - val_loss: 43.8533 - val_vae_r_loss: 38.1541 - val_vae_kl_loss: 5.6993\n",
+ "Epoch 54/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 42.5498 - vae_r_loss: 36.7861 - vae_kl_loss: 5.7637 - val_loss: 43.9121 - val_vae_r_loss: 38.2163 - val_vae_kl_loss: 5.6958\n",
+ "Epoch 55/100\n",
+ "60000/60000 [==============================] - 7s 125us/sample - loss: 42.5410 - vae_r_loss: 36.7715 - vae_kl_loss: 5.7695 - val_loss: 43.9402 - val_vae_r_loss: 38.2676 - val_vae_kl_loss: 5.6726\n",
+ "Epoch 56/100\n",
+ "60000/60000 [==============================] - 7s 118us/sample - loss: 42.5186 - vae_r_loss: 36.7312 - vae_kl_loss: 5.7875 - val_loss: 43.8019 - val_vae_r_loss: 38.0754 - val_vae_kl_loss: 5.7266\n",
+ "Epoch 57/100\n",
+ "60000/60000 [==============================] - 7s 120us/sample - loss: 42.4861 - vae_r_loss: 36.6955 - vae_kl_loss: 5.7906 - val_loss: 43.7560 - val_vae_r_loss: 37.9236 - val_vae_kl_loss: 5.8325\n",
+ "Epoch 58/100\n",
+ "60000/60000 [==============================] - 7s 120us/sample - loss: 42.4515 - vae_r_loss: 36.6663 - vae_kl_loss: 5.7851 - val_loss: 43.8697 - val_vae_r_loss: 37.9264 - val_vae_kl_loss: 5.9433\n",
+ "Epoch 59/100\n",
+ "60000/60000 [==============================] - 8s 129us/sample - loss: 42.4103 - vae_r_loss: 36.6236 - vae_kl_loss: 5.7867 - val_loss: 43.8263 - val_vae_r_loss: 37.9798 - val_vae_kl_loss: 5.8465\n",
+ "Epoch 60/100\n",
+ "60000/60000 [==============================] - 8s 125us/sample - loss: 42.3559 - vae_r_loss: 36.5556 - vae_kl_loss: 5.8003 - val_loss: 43.9342 - val_vae_r_loss: 38.3343 - val_vae_kl_loss: 5.5999\n",
+ "Epoch 61/100\n",
+ "60000/60000 [==============================] - 7s 117us/sample - loss: 42.4222 - vae_r_loss: 36.6162 - vae_kl_loss: 5.8060 - val_loss: 43.7412 - val_vae_r_loss: 37.9454 - val_vae_kl_loss: 5.7958\n",
+ "Epoch 62/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 42.3689 - vae_r_loss: 36.5495 - vae_kl_loss: 5.8194 - val_loss: 43.6502 - val_vae_r_loss: 37.7721 - val_vae_kl_loss: 5.8781\n",
+ "Epoch 63/100\n",
+ "60000/60000 [==============================] - 7s 121us/sample - loss: 42.3349 - vae_r_loss: 36.5133 - vae_kl_loss: 5.8216 - val_loss: 43.8532 - val_vae_r_loss: 38.1812 - val_vae_kl_loss: 5.6720\n",
+ "Epoch 64/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 42.3399 - vae_r_loss: 36.5160 - vae_kl_loss: 5.8239 - val_loss: 43.7407 - val_vae_r_loss: 37.7782 - val_vae_kl_loss: 5.9625\n",
+ "Epoch 65/100\n",
+ "60000/60000 [==============================] - 7s 122us/sample - loss: 42.3138 - vae_r_loss: 36.4957 - vae_kl_loss: 5.8181 - val_loss: 43.7347 - val_vae_r_loss: 37.8601 - val_vae_kl_loss: 5.8746\n",
+ "Epoch 66/100\n",
+ "60000/60000 [==============================] - 7s 123us/sample - loss: 42.2707 - vae_r_loss: 36.4429 - vae_kl_loss: 5.8278 - val_loss: 43.6608 - val_vae_r_loss: 37.7890 - val_vae_kl_loss: 5.8719\n",
+ "Epoch 67/100\n",
+ "60000/60000 [==============================] - 8s 125us/sample - loss: 42.2985 - vae_r_loss: 36.4611 - vae_kl_loss: 5.8374 - val_loss: 43.6500 - val_vae_r_loss: 37.8897 - val_vae_kl_loss: 5.7603\n",
+ "Epoch 68/100\n",
+ "60000/60000 [==============================] - 7s 125us/sample - loss: 42.2463 - vae_r_loss: 36.4053 - vae_kl_loss: 5.8411 - val_loss: 43.8904 - val_vae_r_loss: 38.1325 - val_vae_kl_loss: 5.7579\n",
+ "Epoch 69/100\n",
+ "60000/60000 [==============================] - 8s 125us/sample - loss: 42.2397 - vae_r_loss: 36.4007 - vae_kl_loss: 5.8389 - val_loss: 43.7959 - val_vae_r_loss: 38.0308 - val_vae_kl_loss: 5.7651\n",
+ "Epoch 70/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 42.2090 - vae_r_loss: 36.3648 - vae_kl_loss: 5.8442 - val_loss: 43.6900 - val_vae_r_loss: 37.9130 - val_vae_kl_loss: 5.7771\n",
+ "Epoch 71/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 42.1998 - vae_r_loss: 36.3484 - vae_kl_loss: 5.8514 - val_loss: 43.6552 - val_vae_r_loss: 37.8492 - val_vae_kl_loss: 5.8060\n",
+ "Epoch 72/100\n",
+ "60000/60000 [==============================] - 7s 123us/sample - loss: 42.1943 - vae_r_loss: 36.3286 - vae_kl_loss: 5.8657 - val_loss: 43.6515 - val_vae_r_loss: 37.9546 - val_vae_kl_loss: 5.6969\n",
+ "Epoch 73/100\n",
+ "60000/60000 [==============================] - 8s 125us/sample - loss: 42.1745 - vae_r_loss: 36.3193 - vae_kl_loss: 5.8552 - val_loss: 43.8444 - val_vae_r_loss: 38.1314 - val_vae_kl_loss: 5.7130\n",
+ "Epoch 74/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 42.1288 - vae_r_loss: 36.2784 - vae_kl_loss: 5.8504 - val_loss: 43.8137 - val_vae_r_loss: 38.0373 - val_vae_kl_loss: 5.7764\n",
+ "Epoch 75/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 42.1683 - vae_r_loss: 36.3033 - vae_kl_loss: 5.8650 - val_loss: 43.6371 - val_vae_r_loss: 37.9428 - val_vae_kl_loss: 5.6942\n",
+ "Epoch 76/100\n",
+ "60000/60000 [==============================] - 8s 125us/sample - loss: 42.1302 - vae_r_loss: 36.2609 - vae_kl_loss: 5.8692 - val_loss: 43.8022 - val_vae_r_loss: 37.9602 - val_vae_kl_loss: 5.8420\n",
+ "Epoch 77/100\n",
+ "60000/60000 [==============================] - 8s 125us/sample - loss: 42.1186 - vae_r_loss: 36.2503 - vae_kl_loss: 5.8684 - val_loss: 43.6853 - val_vae_r_loss: 37.9111 - val_vae_kl_loss: 5.7742\n",
+ "Epoch 78/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 42.1304 - vae_r_loss: 36.2550 - vae_kl_loss: 5.8754 - val_loss: 43.7015 - val_vae_r_loss: 37.8260 - val_vae_kl_loss: 5.8755\n",
+ "Epoch 79/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 42.0687 - vae_r_loss: 36.1876 - vae_kl_loss: 5.8811 - val_loss: 43.6678 - val_vae_r_loss: 37.7893 - val_vae_kl_loss: 5.8785\n",
+ "Epoch 80/100\n",
+ "60000/60000 [==============================] - 8s 125us/sample - loss: 42.0476 - vae_r_loss: 36.1643 - vae_kl_loss: 5.8833 - val_loss: 43.6656 - val_vae_r_loss: 37.8170 - val_vae_kl_loss: 5.8487\n",
+ "Epoch 81/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 42.0584 - vae_r_loss: 36.1825 - vae_kl_loss: 5.8759 - val_loss: 43.6267 - val_vae_r_loss: 37.8788 - val_vae_kl_loss: 5.7480\n",
+ "Epoch 82/100\n",
+ "60000/60000 [==============================] - 7s 111us/sample - loss: 42.0196 - vae_r_loss: 36.1357 - vae_kl_loss: 5.8840 - val_loss: 43.7281 - val_vae_r_loss: 37.6417 - val_vae_kl_loss: 6.0864\n",
+ "Epoch 83/100\n",
+ "60000/60000 [==============================] - 7s 125us/sample - loss: 42.0253 - vae_r_loss: 36.1311 - vae_kl_loss: 5.8943 - val_loss: 43.6205 - val_vae_r_loss: 37.8310 - val_vae_kl_loss: 5.7895\n",
+ "Epoch 84/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 42.0190 - vae_r_loss: 36.1276 - vae_kl_loss: 5.8914 - val_loss: 43.6444 - val_vae_r_loss: 37.8001 - val_vae_kl_loss: 5.8443\n",
+ "Epoch 85/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 42.0310 - vae_r_loss: 36.1556 - vae_kl_loss: 5.8754 - val_loss: 43.7125 - val_vae_r_loss: 37.8790 - val_vae_kl_loss: 5.8336\n",
+ "Epoch 86/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 42.0147 - vae_r_loss: 36.1276 - vae_kl_loss: 5.8872 - val_loss: 43.7536 - val_vae_r_loss: 37.7771 - val_vae_kl_loss: 5.9764\n",
+ "Epoch 87/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 41.9674 - vae_r_loss: 36.0723 - vae_kl_loss: 5.8951 - val_loss: 43.6899 - val_vae_r_loss: 37.8354 - val_vae_kl_loss: 5.8545\n",
+ "Epoch 88/100\n",
+ "60000/60000 [==============================] - 7s 122us/sample - loss: 41.9717 - vae_r_loss: 36.0720 - vae_kl_loss: 5.8998 - val_loss: 43.6792 - val_vae_r_loss: 37.9402 - val_vae_kl_loss: 5.7390\n",
+ "Epoch 89/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 41.9129 - vae_r_loss: 36.0126 - vae_kl_loss: 5.9003 - val_loss: 43.6925 - val_vae_r_loss: 37.8338 - val_vae_kl_loss: 5.8587\n",
+ "Epoch 90/100\n",
+ "60000/60000 [==============================] - 7s 124us/sample - loss: 41.9510 - vae_r_loss: 36.0328 - vae_kl_loss: 5.9181 - val_loss: 43.7327 - val_vae_r_loss: 37.8878 - val_vae_kl_loss: 5.8448\n",
+ "Epoch 91/100\n",
+ "60000/60000 [==============================] - 7s 125us/sample - loss: 41.9122 - vae_r_loss: 35.9966 - vae_kl_loss: 5.9155 - val_loss: 43.7091 - val_vae_r_loss: 37.8563 - val_vae_kl_loss: 5.8527\n",
+ "Epoch 92/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 41.9161 - vae_r_loss: 35.9930 - vae_kl_loss: 5.9231 - val_loss: 43.7270 - val_vae_r_loss: 37.8876 - val_vae_kl_loss: 5.8393\n",
+ "Epoch 93/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 41.9154 - vae_r_loss: 35.9875 - vae_kl_loss: 5.9278 - val_loss: 43.6541 - val_vae_r_loss: 37.6903 - val_vae_kl_loss: 5.9639\n",
+ "Epoch 94/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 41.8807 - vae_r_loss: 35.9663 - vae_kl_loss: 5.9144 - val_loss: 43.7643 - val_vae_r_loss: 37.8280 - val_vae_kl_loss: 5.9363\n",
+ "Epoch 95/100\n",
+ "60000/60000 [==============================] - 7s 125us/sample - loss: 41.9016 - vae_r_loss: 35.9739 - vae_kl_loss: 5.9277 - val_loss: 43.8913 - val_vae_r_loss: 37.9501 - val_vae_kl_loss: 5.9412\n",
+ "Epoch 96/100\n",
+ "60000/60000 [==============================] - 7s 120us/sample - loss: 41.8545 - vae_r_loss: 35.9314 - vae_kl_loss: 5.9231 - val_loss: 43.7067 - val_vae_r_loss: 37.7875 - val_vae_kl_loss: 5.9192\n",
+ "Epoch 97/100\n",
+ "60000/60000 [==============================] - 7s 118us/sample - loss: 41.8349 - vae_r_loss: 35.9267 - vae_kl_loss: 5.9083 - val_loss: 43.7083 - val_vae_r_loss: 37.7909 - val_vae_kl_loss: 5.9173\n",
+ "Epoch 98/100\n",
+ "60000/60000 [==============================] - 7s 123us/sample - loss: 41.8574 - vae_r_loss: 35.9213 - vae_kl_loss: 5.9361 - val_loss: 43.6804 - val_vae_r_loss: 37.8716 - val_vae_kl_loss: 5.8088\n",
+ "Epoch 99/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 41.8132 - vae_r_loss: 35.8820 - vae_kl_loss: 5.9312 - val_loss: 43.5919 - val_vae_r_loss: 37.7066 - val_vae_kl_loss: 5.8853\n",
+ "Epoch 100/100\n",
+ "60000/60000 [==============================] - 8s 126us/sample - loss: 41.8338 - vae_r_loss: 35.9009 - vae_kl_loss: 5.9329 - val_loss: 43.6792 - val_vae_r_loss: 37.6395 - val_vae_kl_loss: 6.0397\n",
+ "\n",
+ "Train duration : 750.18 sec. - 0:12:30\n"
+ ]
+ }
+ ],
"source": [
"vae.train(x_train,\n",
" x_test,\n",
diff --git a/VAE/02-VAE-with-MNIST-post.ipynb b/VAE/02-VAE-with-MNIST-post.ipynb
index eb3f020..9d8d7f1 100644
--- a/VAE/02-VAE-with-MNIST-post.ipynb
+++ b/VAE/02-VAE-with-MNIST-post.ipynb
@@ -7,11 +7,11 @@
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis\n",
- "<!-- DESC --> Use of the previously trained model, analysis of the results\n",
+ "<!-- DESC --> Episode 2 : Exploring our latent space\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
- " - First data generation from latent space \n",
+ " - First data generation from **latent space** \n",
" - Understanding of underlying principles\n",
" - Model management\n",
"\n",
diff --git a/VAE/03-About-CelebA.ipynb b/VAE/03-About-CelebA.ipynb
index fa2a0b8..809db8e 100644
--- a/VAE/03-About-CelebA.ipynb
+++ b/VAE/03-About-CelebA.ipynb
@@ -7,12 +7,12 @@
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [VAE3] - About the CelebA dataset\n",
- "<!-- DESC --> New VAE experience, but with a larger and more fun dataset\n",
+ "<!-- DESC --> Episode 3Â : About the CelebA dataset, a more fun dataset !\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
- " - Data analysis and preparation\n",
- " - Problems related to the use of more real datasets\n",
+ " - Data **analysis**\n",
+ " - Problems related to the use of **more real datasets**\n",
"\n",
"We'll do the same thing again but with a more interesting dataset: **CelebFaces** \n",
"\"[CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations.\""
diff --git a/VAE/04-Prepare-CelebA-batch.ipynb b/VAE/04-Prepare-CelebA-batch.ipynb
index fc248d1..d0dd572 100644
--- a/VAE/04-Prepare-CelebA-batch.ipynb
+++ b/VAE/04-Prepare-CelebA-batch.ipynb
@@ -7,11 +7,11 @@
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [VAE4] - Preparation of the CelebA dataset\n",
- "<!-- DESC --> Preparation of a clustered dataset, batchable\n",
+ "<!-- DESC --> Episode 4Â : Preparation of a clustered dataset, batchable\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
- " - Formatting our dataset in cluster files, using batch mode\n",
+ " - Formatting our dataset in **cluster files**, using batch mode\n",
" - Adapting a notebook for batch use\n",
"\n",
"\n",
diff --git a/VAE/05-Check-CelebA.ipynb b/VAE/05-Check-CelebA.ipynb
index 4d7b927..83fbf23 100644
--- a/VAE/05-Check-CelebA.ipynb
+++ b/VAE/05-Check-CelebA.ipynb
@@ -7,7 +7,7 @@
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [VAE5] - Checking the clustered CelebA dataset\n",
- "<!-- DESC --> Verification of prepared data from CelebA dataset\n",
+ "<!-- DESC --> Episode 5Â :\tChecking the clustered dataset\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
diff --git a/VAE/06-VAE-with-CelebA-s.ipynb b/VAE/06-VAE-with-CelebA-s.ipynb
index c5141fe..febf7df 100644
--- a/VAE/06-VAE-with-CelebA-s.ipynb
+++ b/VAE/06-VAE-with-CelebA-s.ipynb
@@ -7,12 +7,12 @@
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [VAE6] - Variational AutoEncoder (VAE) with CelebA (small)\n",
- "<!-- DESC --> VAE with a more fun and realistic dataset - small resolution and batchable\n",
+ "<!-- DESC --> Episode 6Â : Variational AutoEncoder (VAE) with CelebA (small res.)\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
- " - Build and train a VAE model with a large dataset in small resolution(>70 GB)\n",
- " - Understanding a more advanced programming model with data generator\n",
+ " - Build and train a VAE model with a large dataset in **small resolution(>70 GB)**\n",
+ " - Understanding a more advanced programming model with **data generator**\n",
"\n",
"The [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) contains about 200,000 images (202599,218,178,3). \n",
"\n",
diff --git a/VAE/07-VAE-with-CelebA-m.ipynb b/VAE/07-VAE-with-CelebA-m.ipynb
index a6bd343..3943556 100644
--- a/VAE/07-VAE-with-CelebA-m.ipynb
+++ b/VAE/07-VAE-with-CelebA-m.ipynb
@@ -7,12 +7,12 @@
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)\n",
- "<!-- DESC --> VAE with a more fun and realistic dataset - medium resolution and batchable\n",
+ "<!-- DESC --> Episode 7Â : Variational AutoEncoder (VAE) with CelebA (medium res.)\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
- " - Build and train a VAE model with a large dataset in small resolution(>140 GB)\n",
- " - Understanding a more advanced programming model with data generator\n",
+ " - Build and train a VAE model with a large dataset in **medium resolution(>140 GB)**\n",
+ " - Understanding a more advanced programming model with **data generator**\n",
"\n",
"The [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) contains about 200,000 images (202599,218,178,3). \n",
"\n",
diff --git a/VAE/08-VAE-with-CelebA-m.nbconvert.ipynb b/VAE/07-VAE-with-CelebA-m.nbconvert.ipynb
similarity index 100%
rename from VAE/08-VAE-with-CelebA-m.nbconvert.ipynb
rename to VAE/07-VAE-with-CelebA-m.nbconvert.ipynb
diff --git a/VAE/12-VAE-withCelebA-post.ipynb b/VAE/08-VAE-withCelebA-post.ipynb
similarity index 99%
rename from VAE/12-VAE-withCelebA-post.ipynb
rename to VAE/08-VAE-withCelebA-post.ipynb
index 8675899..3ed0684 100644
--- a/VAE/12-VAE-withCelebA-post.ipynb
+++ b/VAE/08-VAE-withCelebA-post.ipynb
@@ -7,13 +7,13 @@
"<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
"# <!-- TITLE --> [VAE12] - Variational AutoEncoder (VAE) with CelebA - Analysis\n",
- "<!-- DESC --> Use of the previously trained model with CelebA, analysis of the results\n",
+ "<!-- DESC --> Episode 8Â : Exploring latent space of our trained models\n",
"<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
"\n",
"## Objectives :\n",
- " - New data generation from latent space\n",
+ " - New data generation from **latent space**\n",
" - Understanding of underlying principles\n",
- " - Guided image generation (latent morphing)\n",
+ " - Guided image generation, **latent morphing**\n",
" - Model management\n",
" \n",
"Here again, we don't consume data anymore, but we generate them ! ;-)\n",
--
GitLab