diff --git a/VAE/08.1-VAE-with-CelebA.ipynb b/VAE/08.1-VAE-with-CelebA.ipynb
index ca1ca0a6c004b6bf1c574eefd8f9e53db9332de4..dbf0aa00ad34465cc4106dce67a5678c3ee1de26 100644
--- a/VAE/08.1-VAE-with-CelebA.ipynb
+++ b/VAE/08.1-VAE-with-CelebA.ipynb
@@ -37,98 +37,9 @@
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- " margin-right:20px;\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"
- },
- {
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- "output_type": "stream",
- "text": [
- "Version : 0.6.1 DEV\n",
- "Notebook id : VAE8\n",
- "Run time : Monday 4 January 2021, 22:27:20\n",
- "TensorFlow version : 2.4.0\n",
- "Keras version : 2.4.0\n",
- "Datasets dir : /gpfswork/rech/mlh/uja62cb/datasets\n",
- "Run dir : ./run/CelebA-s.001\n",
- "CI running mode : none\n",
- "Update keras cache : False\n",
- "Save figs : True\n",
- "Path figs : ./run/CelebA-s.001/figs\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import numpy as np\n",
"\n",
@@ -174,7 +85,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -198,17 +109,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Train directory is : /gpfswork/rech/mlh/uja62cb/datasets/celeba/enhanced/clusters-128x128\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# ---- Used for continous integration - Just forget this 2 lines\n",
"#\n",
@@ -224,17 +127,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Data generator : 6331 batchs of 32 images, or 202599 images\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"data_gen = DataGenerator(train_dir, 32, k_size=scale)\n",
"\n",
@@ -250,7 +145,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -273,7 +168,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -306,7 +201,7 @@
},
{
"cell_type": "code",
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+ "execution_count": null,
"metadata": {},
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@@ -345,17 +240,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Init VAE, with r_loss_factor=0.3 and image_size=(128, 128)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"vae = VAE(encoder, decoder, image_size=(128,128), r_loss_factor=0.3)\n",
"\n",
@@ -373,7 +260,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -404,74 +291,24 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Epoch 1/10\n",
- " 5/6331 [..............................] - ETA: 6:03 - loss: 3393.9379 - reconstruction_loss: 11310.1393 - kl_loss: 1.2800 WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0183s vs `on_train_batch_end` time: 0.0263s). Check your callbacks.\n",
- "6331/6331 [==============================] - 132s 20ms/step - loss: 2482.7580 - reconstruction_loss: 8267.9253 - kl_loss: 3.4004\n",
- " (saved) Epoch 2/10\n",
- "6331/6331 [==============================] - 126s 20ms/step - loss: 2418.4020 - reconstruction_loss: 8053.0484 - kl_loss: 3.5534\n",
- "Epoch 3/10\n",
- "6331/6331 [==============================] - 126s 20ms/step - loss: 2407.6979 - reconstruction_loss: 8017.4571 - kl_loss: 3.5152\n",
- " (saved) Epoch 4/10\n",
- "6331/6331 [==============================] - 133s 21ms/step - loss: 2402.6755 - reconstruction_loss: 8000.7420 - kl_loss: 3.5040\n",
- " (saved) Epoch 5/10\n",
- "6331/6331 [==============================] - 125s 20ms/step - loss: 2399.7602 - reconstruction_loss: 7991.0481 - kl_loss: 3.4939\n",
- "Epoch 6/10\n",
- "2677/6331 [===========>..................] - ETA: 1:11 - loss: 2397.4920 - reconstruction_loss: 7983.4897 - kl_loss: 3.4928"
- ]
- },
- {
- "ename": "KeyboardInterrupt",
- "evalue": "",
- "output_type": "error",
- "traceback": [
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- "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
- "\u001b[0;32m/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1098\u001b[0m _r=1):\n\u001b[1;32m 1099\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1101\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1102\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 826\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 827\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 829\u001b[0m \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xla\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 830\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 853\u001b[0m \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 854\u001b[0m \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 855\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 856\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 857\u001b[0m \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2941\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m 2942\u001b[0m return graph_function._call_flat(\n\u001b[0;32m-> 2943\u001b[0;31m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m 2944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2945\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1917\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1918\u001b[0m return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1919\u001b[0;31m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m 1920\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m 1921\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;32m/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 558\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 559\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 560\u001b[0;31m ctx=ctx)\n\u001b[0m\u001b[1;32m 561\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 562\u001b[0m outputs = execute.execute_with_cancellation(\n",
- "\u001b[0;32m/gpfslocalsup/pub/anaconda-py3/2020.02/envs/tensorflow-gpu-2.4.0/lib/python3.7/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 59\u001b[0m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m 61\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
- ]
- }
- ],
+ "outputs": [],
"source": [
"%%time\n",
"\n",
- "batch_size = 128\n",
+ "batch_size = 64\n",
"epochs = 10\n",
"\n",
- "history = vae.fit(data_gen, epochs=epochs, batch_size=batch_size, callbacks=callbacks_list,\n",
- " initial_epoch = 0)\n"
+ "history = vae.fit(data_gen, epochs=epochs, batch_size=batch_size, callbacks=callbacks_list)\n",
+ "# initial_epoch = 0)\n"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'history' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-13-ecf7f511b1ed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpwk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_history\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"Loss\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'reconstruction_loss'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'kl_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_as\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'history'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m: name 'history' is not defined"
- ]
- }
- ],
+ "outputs": [],
"source": [
"pwk.plot_history(history, plot={\"Loss\":['loss','reconstruction_loss', 'kl_loss']}, save_as='history')"
]
diff --git a/VAE/modules/VAE.py b/VAE/modules/VAE.py
index 8d7b96c09098ecda3ec1998b2674cc564c130035..325903666f5941a1dc377ffd8cb5d8f45fcf39fd 100644
--- a/VAE/modules/VAE.py
+++ b/VAE/modules/VAE.py
@@ -43,7 +43,7 @@ class VAE(keras.Model):
def call(self, inputs):
- z = self.encoder(inputs)
+ z_mean, z_log_var, z = self.encoder(inputs)
y_pred = self.decoder(z)
return y_pred
diff --git a/VAE/modules/callbacks.py b/VAE/modules/callbacks.py
index 4701da4514c3cec47c985dcf69c617b9e28b471a..73b9407163a8205b002a705c23c07d3e1ea0bca1 100644
--- a/VAE/modules/callbacks.py
+++ b/VAE/modules/callbacks.py
@@ -31,6 +31,8 @@ class ImagesCallback(Callback):
def on_epoch_end(self, epoch, logs={}):
+ if epoch<1 : return
+
# ---- Get latent points
#
if self.x is None:
@@ -72,6 +74,7 @@ class BestModelCallback(Callback):
self.loss = np.Inf
def on_epoch_end(self, epoch, logs=None):
+ if epoch<1 : return
current = logs.get("loss")
if current<self.loss:
self.loss = current
diff --git a/fidle/log/finished.json b/fidle/log/finished.json
index 5270d12c169f48e7c8957cddf51d4def920f9427..00fc304077da664e3e0bf3e79fa44a909c2bd1cd 100644
--- a/fidle/log/finished.json
+++ b/fidle/log/finished.json
@@ -78,8 +78,8 @@
"duration": "00:00:03 329ms"
},
"VAE1": {
- "path": "/home/pjluc/dev/fidle/VAE",
- "start": "Monday 4 January 2021, 17:59:08",
+ "path": "/gpfsdswork/projects/rech/mlh/uja62cb/fidle/VAE",
+ "start": "Tuesday 5 January 2021, 00:07:12",
"end": "",
"duration": "Unfinished..."
},
@@ -150,8 +150,8 @@
"duration": "00:00:08 736ms"
},
"VAE8": {
- "path": "/home/pjluc/dev/fidle/VAE",
- "start": "Monday 4 January 2021, 23:53:16",
+ "path": "/gpfsdswork/projects/rech/mlh/uja62cb/fidle/VAE",
+ "start": "Tuesday 5 January 2021, 00:23:42",
"end": "",
"duration": "Unfinished..."
}