diff --git a/VAE/01-VAE with MNIST.ipynb b/VAE/01-VAE with MNIST.ipynb
index 5c7450927e8956a11faf1a3355494fa52a20a01b..8eb9c45527c8cdc76b79e66fb3001635d231813b 100644
--- a/VAE/01-VAE with MNIST.ipynb
+++ b/VAE/01-VAE with MNIST.ipynb
@@ -22,9 +22,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "IDLE 2020 - Practical Work Module\n",
+ " Version : 0.2.5\n",
+ " Run time : Tuesday 4 February 2020, 00:10:15\n",
+ " Matplotlib style : ../fidle/talk.mplstyle\n",
+ " TensorFlow version : 2.0.0\n",
+ " Keras version : 2.2.4-tf\n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"\n",
@@ -57,9 +70,18 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(60000, 28, 28, 1)\n",
+ "(10000, 28, 28, 1)\n"
+ ]
+ }
+ ],
"source": [
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
"\n",
@@ -80,14 +102,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model initialized.\n",
+ "Outputs will be in : ./run/001\n"
+ ]
+ }
+ ],
"source": [
"# reload(modules.vae)\n",
"# reload(modules.callbacks)\n",
"\n",
- "tag = '000'\n",
+ "tag = '001'\n",
"\n",
"input_shape = (28,28,1)\n",
"z_dim = 2\n",
@@ -122,7 +153,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -141,14 +172,14 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"batch_size = 100\n",
"epochs = 200\n",
- "image_periodicity = 1 # in epoch\n",
- "chkpt_periodicity = 2 # in epoch\n",
+ "image_periodicity = 1 # for each epoch\n",
+ "chkpt_periodicity = 2 # for each epoch\n",
"initial_epoch = 0\n",
"dataset_size = 1"
]
@@ -157,7 +188,166 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train on 60000 samples, validate on 10000 samples\n",
+ "Epoch 1/200\n",
+ " 100/60000 [..............................] - ETA: 23:40 - loss: 231.4378 - vae_r_loss: 231.4373 - vae_kl_loss: 5.3801e-04WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.251492). Check your callbacks.\n",
+ "60000/60000 [==============================] - 6s 101us/sample - loss: 67.7431 - vae_r_loss: 65.0691 - vae_kl_loss: 2.6740 - val_loss: 55.6598 - val_vae_r_loss: 52.4039 - val_vae_kl_loss: 3.2560\n",
+ "Epoch 2/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 54.0334 - vae_r_loss: 50.4695 - vae_kl_loss: 3.5639 - val_loss: 52.9105 - val_vae_r_loss: 49.1433 - val_vae_kl_loss: 3.7672\n",
+ "Epoch 3/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 51.8937 - vae_r_loss: 47.9195 - vae_kl_loss: 3.9743 - val_loss: 51.1775 - val_vae_r_loss: 47.0874 - val_vae_kl_loss: 4.0901\n",
+ "Epoch 4/200\n",
+ "60000/60000 [==============================] - 4s 59us/sample - loss: 50.4622 - vae_r_loss: 46.1359 - vae_kl_loss: 4.3264 - val_loss: 49.8507 - val_vae_r_loss: 45.2015 - val_vae_kl_loss: 4.6492\n",
+ "Epoch 5/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 49.3577 - vae_r_loss: 44.8123 - vae_kl_loss: 4.5454 - val_loss: 48.9416 - val_vae_r_loss: 44.3832 - val_vae_kl_loss: 4.5584\n",
+ "Epoch 6/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 48.5603 - vae_r_loss: 43.8800 - vae_kl_loss: 4.6803 - val_loss: 48.1800 - val_vae_r_loss: 43.5046 - val_vae_kl_loss: 4.6754\n",
+ "Epoch 7/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 48.0286 - vae_r_loss: 43.2646 - vae_kl_loss: 4.7640 - val_loss: 47.9362 - val_vae_r_loss: 43.2833 - val_vae_kl_loss: 4.6529\n",
+ "Epoch 8/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 47.6163 - vae_r_loss: 42.7828 - vae_kl_loss: 4.8336 - val_loss: 47.6161 - val_vae_r_loss: 42.7176 - val_vae_kl_loss: 4.8985\n",
+ "Epoch 9/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 47.2654 - vae_r_loss: 42.3804 - vae_kl_loss: 4.8850 - val_loss: 47.1385 - val_vae_r_loss: 42.2280 - val_vae_kl_loss: 4.9105\n",
+ "Epoch 10/200\n",
+ "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.732872). Check your callbacks.\n",
+ " 100/60000 [..............................] - ETA: 7:23 - loss: 47.8688 - vae_r_loss: 43.0966 - vae_kl_loss: 4.7722WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.366450). Check your callbacks.\n",
+ "60000/60000 [==============================] - 4s 70us/sample - loss: 46.9698 - vae_r_loss: 42.0353 - vae_kl_loss: 4.9345 - val_loss: 47.0246 - val_vae_r_loss: 42.1103 - val_vae_kl_loss: 4.9143\n",
+ "Epoch 11/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 46.7538 - vae_r_loss: 41.7733 - vae_kl_loss: 4.9805 - val_loss: 46.9033 - val_vae_r_loss: 41.9019 - val_vae_kl_loss: 5.0014\n",
+ "Epoch 12/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 46.4962 - vae_r_loss: 41.4867 - vae_kl_loss: 5.0095 - val_loss: 46.6990 - val_vae_r_loss: 41.8006 - val_vae_kl_loss: 4.8985\n",
+ "Epoch 13/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 46.3232 - vae_r_loss: 41.2603 - vae_kl_loss: 5.0629 - val_loss: 46.6737 - val_vae_r_loss: 41.4675 - val_vae_kl_loss: 5.2061\n",
+ "Epoch 14/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 46.1505 - vae_r_loss: 41.0678 - vae_kl_loss: 5.0828 - val_loss: 46.3871 - val_vae_r_loss: 41.4687 - val_vae_kl_loss: 4.9184\n",
+ "Epoch 15/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 45.9750 - vae_r_loss: 40.8533 - vae_kl_loss: 5.1217 - val_loss: 46.1730 - val_vae_r_loss: 41.0982 - val_vae_kl_loss: 5.0748\n",
+ "Epoch 16/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 45.8053 - vae_r_loss: 40.6467 - vae_kl_loss: 5.1586 - val_loss: 46.2439 - val_vae_r_loss: 41.1142 - val_vae_kl_loss: 5.1297\n",
+ "Epoch 17/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 45.6415 - vae_r_loss: 40.4657 - vae_kl_loss: 5.1758 - val_loss: 46.0754 - val_vae_r_loss: 41.0632 - val_vae_kl_loss: 5.0122\n",
+ "Epoch 18/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 45.5121 - vae_r_loss: 40.3147 - vae_kl_loss: 5.1974 - val_loss: 45.8663 - val_vae_r_loss: 40.5329 - val_vae_kl_loss: 5.3334\n",
+ "Epoch 19/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 45.3686 - vae_r_loss: 40.1475 - vae_kl_loss: 5.2211 - val_loss: 46.2054 - val_vae_r_loss: 41.1238 - val_vae_kl_loss: 5.0816\n",
+ "Epoch 20/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 45.2161 - vae_r_loss: 39.9703 - vae_kl_loss: 5.2458 - val_loss: 45.7448 - val_vae_r_loss: 40.6166 - val_vae_kl_loss: 5.1283\n",
+ "Epoch 21/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 45.1159 - vae_r_loss: 39.8419 - vae_kl_loss: 5.2740 - val_loss: 45.8612 - val_vae_r_loss: 40.8692 - val_vae_kl_loss: 4.9920\n",
+ "Epoch 22/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 44.9881 - vae_r_loss: 39.7023 - vae_kl_loss: 5.2857 - val_loss: 45.8085 - val_vae_r_loss: 40.2675 - val_vae_kl_loss: 5.5410\n",
+ "Epoch 23/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 44.8471 - vae_r_loss: 39.5384 - vae_kl_loss: 5.3087 - val_loss: 45.4330 - val_vae_r_loss: 40.0743 - val_vae_kl_loss: 5.3587\n",
+ "Epoch 24/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 44.7550 - vae_r_loss: 39.4362 - vae_kl_loss: 5.3188 - val_loss: 45.3320 - val_vae_r_loss: 39.9992 - val_vae_kl_loss: 5.3328\n",
+ "Epoch 25/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 44.6692 - vae_r_loss: 39.3461 - vae_kl_loss: 5.3232 - val_loss: 45.3552 - val_vae_r_loss: 40.0258 - val_vae_kl_loss: 5.3294\n",
+ "Epoch 26/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 44.5891 - vae_r_loss: 39.2333 - vae_kl_loss: 5.3558 - val_loss: 45.2681 - val_vae_r_loss: 39.9015 - val_vae_kl_loss: 5.3666\n",
+ "Epoch 27/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 44.5072 - vae_r_loss: 39.1374 - vae_kl_loss: 5.3698 - val_loss: 45.3209 - val_vae_r_loss: 39.9636 - val_vae_kl_loss: 5.3574\n",
+ "Epoch 28/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 44.4180 - vae_r_loss: 39.0149 - vae_kl_loss: 5.4031 - val_loss: 45.2435 - val_vae_r_loss: 39.7765 - val_vae_kl_loss: 5.4671\n",
+ "Epoch 29/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 44.3102 - vae_r_loss: 38.9046 - vae_kl_loss: 5.4057 - val_loss: 45.2258 - val_vae_r_loss: 39.8441 - val_vae_kl_loss: 5.3817\n",
+ "Epoch 30/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 44.2489 - vae_r_loss: 38.8299 - vae_kl_loss: 5.4190 - val_loss: 45.0044 - val_vae_r_loss: 39.6516 - val_vae_kl_loss: 5.3528\n",
+ "Epoch 31/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 44.1732 - vae_r_loss: 38.7482 - vae_kl_loss: 5.4249 - val_loss: 45.0000 - val_vae_r_loss: 39.5609 - val_vae_kl_loss: 5.4391\n",
+ "Epoch 32/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 44.0894 - vae_r_loss: 38.6580 - vae_kl_loss: 5.4314 - val_loss: 44.9769 - val_vae_r_loss: 39.5384 - val_vae_kl_loss: 5.4385\n",
+ "Epoch 33/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 44.0582 - vae_r_loss: 38.6092 - vae_kl_loss: 5.4490 - val_loss: 44.9346 - val_vae_r_loss: 39.3805 - val_vae_kl_loss: 5.5541\n",
+ "Epoch 34/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 43.9458 - vae_r_loss: 38.4818 - vae_kl_loss: 5.4640 - val_loss: 45.0624 - val_vae_r_loss: 39.5811 - val_vae_kl_loss: 5.4813\n",
+ "Epoch 35/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 43.8850 - vae_r_loss: 38.4031 - vae_kl_loss: 5.4819 - val_loss: 45.0285 - val_vae_r_loss: 39.5350 - val_vae_kl_loss: 5.4935\n",
+ "Epoch 36/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 43.8698 - vae_r_loss: 38.3779 - vae_kl_loss: 5.4918 - val_loss: 44.9170 - val_vae_r_loss: 39.5714 - val_vae_kl_loss: 5.3456\n",
+ "Epoch 37/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 43.7739 - vae_r_loss: 38.2723 - vae_kl_loss: 5.5016 - val_loss: 44.8441 - val_vae_r_loss: 39.3665 - val_vae_kl_loss: 5.4776\n",
+ "Epoch 38/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 43.7084 - vae_r_loss: 38.1933 - vae_kl_loss: 5.5151 - val_loss: 44.9233 - val_vae_r_loss: 39.5526 - val_vae_kl_loss: 5.3706\n",
+ "Epoch 39/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 43.6626 - vae_r_loss: 38.1320 - vae_kl_loss: 5.5306 - val_loss: 44.6793 - val_vae_r_loss: 39.2304 - val_vae_kl_loss: 5.4489\n",
+ "Epoch 40/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 43.5838 - vae_r_loss: 38.0592 - vae_kl_loss: 5.5246 - val_loss: 44.6130 - val_vae_r_loss: 39.0715 - val_vae_kl_loss: 5.5415\n",
+ "Epoch 41/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 43.5194 - vae_r_loss: 37.9840 - vae_kl_loss: 5.5354 - val_loss: 44.8512 - val_vae_r_loss: 39.6158 - val_vae_kl_loss: 5.2354\n",
+ "Epoch 42/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 43.5129 - vae_r_loss: 37.9786 - vae_kl_loss: 5.5343 - val_loss: 44.6991 - val_vae_r_loss: 39.2098 - val_vae_kl_loss: 5.4894\n",
+ "Epoch 43/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 43.4707 - vae_r_loss: 37.9237 - vae_kl_loss: 5.5470 - val_loss: 44.7121 - val_vae_r_loss: 39.2446 - val_vae_kl_loss: 5.4675\n",
+ "Epoch 44/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 43.3832 - vae_r_loss: 37.8227 - vae_kl_loss: 5.5604 - val_loss: 44.9172 - val_vae_r_loss: 39.3446 - val_vae_kl_loss: 5.5726\n",
+ "Epoch 45/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 43.3868 - vae_r_loss: 37.8075 - vae_kl_loss: 5.5793 - val_loss: 44.5718 - val_vae_r_loss: 39.0284 - val_vae_kl_loss: 5.5434\n",
+ "Epoch 46/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 43.2774 - vae_r_loss: 37.6953 - vae_kl_loss: 5.5821 - val_loss: 44.6954 - val_vae_r_loss: 39.1276 - val_vae_kl_loss: 5.5678\n",
+ "Epoch 47/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 43.2765 - vae_r_loss: 37.6813 - vae_kl_loss: 5.5952 - val_loss: 44.6153 - val_vae_r_loss: 38.9606 - val_vae_kl_loss: 5.6547\n",
+ "Epoch 48/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 43.2385 - vae_r_loss: 37.6431 - vae_kl_loss: 5.5954 - val_loss: 44.5508 - val_vae_r_loss: 39.0830 - val_vae_kl_loss: 5.4678\n",
+ "Epoch 49/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 43.1847 - vae_r_loss: 37.5822 - vae_kl_loss: 5.6025 - val_loss: 44.8277 - val_vae_r_loss: 39.1688 - val_vae_kl_loss: 5.6589\n",
+ "Epoch 50/200\n",
+ "60000/60000 [==============================] - 4s 58us/sample - loss: 43.1557 - vae_r_loss: 37.5533 - vae_kl_loss: 5.6024 - val_loss: 44.5082 - val_vae_r_loss: 38.9529 - val_vae_kl_loss: 5.5553\n",
+ "Epoch 51/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 43.0726 - vae_r_loss: 37.4533 - vae_kl_loss: 5.6193 - val_loss: 44.6332 - val_vae_r_loss: 38.9104 - val_vae_kl_loss: 5.7228\n",
+ "Epoch 52/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 43.1003 - vae_r_loss: 37.4708 - vae_kl_loss: 5.6295 - val_loss: 44.5279 - val_vae_r_loss: 39.0846 - val_vae_kl_loss: 5.4433\n",
+ "Epoch 53/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 43.0121 - vae_r_loss: 37.3923 - vae_kl_loss: 5.6198 - val_loss: 44.5675 - val_vae_r_loss: 38.9651 - val_vae_kl_loss: 5.6024\n",
+ "Epoch 54/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 42.9750 - vae_r_loss: 37.3273 - vae_kl_loss: 5.6477 - val_loss: 44.6084 - val_vae_r_loss: 39.0057 - val_vae_kl_loss: 5.6027\n",
+ "Epoch 55/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 42.9669 - vae_r_loss: 37.3124 - vae_kl_loss: 5.6545 - val_loss: 44.4369 - val_vae_r_loss: 38.7499 - val_vae_kl_loss: 5.6870\n",
+ "Epoch 56/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 42.9172 - vae_r_loss: 37.2666 - vae_kl_loss: 5.6506 - val_loss: 44.4817 - val_vae_r_loss: 38.8071 - val_vae_kl_loss: 5.6747\n",
+ "Epoch 57/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 42.8719 - vae_r_loss: 37.2088 - vae_kl_loss: 5.6630 - val_loss: 44.7545 - val_vae_r_loss: 39.1340 - val_vae_kl_loss: 5.6205\n",
+ "Epoch 58/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 42.8724 - vae_r_loss: 37.2070 - vae_kl_loss: 5.6654 - val_loss: 44.4428 - val_vae_r_loss: 38.8374 - val_vae_kl_loss: 5.6054\n",
+ "Epoch 59/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 42.8085 - vae_r_loss: 37.1356 - vae_kl_loss: 5.6729 - val_loss: 44.3657 - val_vae_r_loss: 38.8973 - val_vae_kl_loss: 5.4684\n",
+ "Epoch 60/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 42.7711 - vae_r_loss: 37.1025 - vae_kl_loss: 5.6687 - val_loss: 44.5526 - val_vae_r_loss: 38.7923 - val_vae_kl_loss: 5.7603\n",
+ "Epoch 61/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 42.7549 - vae_r_loss: 37.0712 - vae_kl_loss: 5.6837 - val_loss: 44.6274 - val_vae_r_loss: 39.1211 - val_vae_kl_loss: 5.5063\n",
+ "Epoch 62/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 42.7314 - vae_r_loss: 37.0368 - vae_kl_loss: 5.6946 - val_loss: 44.3828 - val_vae_r_loss: 38.8327 - val_vae_kl_loss: 5.5502\n",
+ "Epoch 63/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 42.6688 - vae_r_loss: 36.9835 - vae_kl_loss: 5.6853 - val_loss: 44.4869 - val_vae_r_loss: 38.8497 - val_vae_kl_loss: 5.6372\n",
+ "Epoch 64/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 42.6714 - vae_r_loss: 36.9633 - vae_kl_loss: 5.7080 - val_loss: 44.4562 - val_vae_r_loss: 38.7178 - val_vae_kl_loss: 5.7384\n",
+ "Epoch 65/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 42.6547 - vae_r_loss: 36.9360 - vae_kl_loss: 5.7187 - val_loss: 44.4947 - val_vae_r_loss: 38.8561 - val_vae_kl_loss: 5.6386\n",
+ "Epoch 66/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 42.5807 - vae_r_loss: 36.8625 - vae_kl_loss: 5.7182 - val_loss: 44.4270 - val_vae_r_loss: 38.7251 - val_vae_kl_loss: 5.7019\n",
+ "Epoch 67/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 42.5664 - vae_r_loss: 36.8466 - vae_kl_loss: 5.7197 - val_loss: 44.5878 - val_vae_r_loss: 38.8787 - val_vae_kl_loss: 5.7091\n",
+ "Epoch 68/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 42.5503 - vae_r_loss: 36.8269 - vae_kl_loss: 5.7235 - val_loss: 44.6236 - val_vae_r_loss: 38.8846 - val_vae_kl_loss: 5.7390\n",
+ "Epoch 69/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 42.5057 - vae_r_loss: 36.7706 - vae_kl_loss: 5.7352 - val_loss: 44.5720 - val_vae_r_loss: 38.9196 - val_vae_kl_loss: 5.6525\n",
+ "Epoch 70/200\n",
+ "60000/60000 [==============================] - 3s 57us/sample - loss: 42.4955 - vae_r_loss: 36.7553 - vae_kl_loss: 5.7402 - val_loss: 44.4059 - val_vae_r_loss: 38.8886 - val_vae_kl_loss: 5.5173\n",
+ "Epoch 71/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 42.4649 - vae_r_loss: 36.7251 - vae_kl_loss: 5.7398 - val_loss: 44.5864 - val_vae_r_loss: 38.8203 - val_vae_kl_loss: 5.7661\n",
+ "Epoch 72/200\n",
+ "60000/60000 [==============================] - 3s 58us/sample - loss: 42.4907 - vae_r_loss: 36.7440 - vae_kl_loss: 5.7467 - val_loss: 44.3493 - val_vae_r_loss: 38.6765 - val_vae_kl_loss: 5.6727\n",
+ "Epoch 73/200\n",
+ "60000/60000 [==============================] - 3s 56us/sample - loss: 42.4224 - vae_r_loss: 36.6558 - vae_kl_loss: 5.7666 - val_loss: 44.5477 - val_vae_r_loss: 38.7588 - val_vae_kl_loss: 5.7889\n",
+ "Epoch 74/200\n",
+ "43100/60000 [====================>.........] - ETA: 0s - loss: 42.3141 - vae_r_loss: 36.5576 - vae_kl_loss: 5.7565"
+ ]
+ }
+ ],
"source": [
"vae.train(x_train,\n",
" x_test,\n",
diff --git a/VAE/modules/callbacks.py b/VAE/modules/callbacks.py
index 63adb23aede91492630a7fd5e4b7dfde817b8304..e7d6e3f88a00b4fe12570663b9fbc6c5f3104465 100644
--- a/VAE/modules/callbacks.py
+++ b/VAE/modules/callbacks.py
@@ -1,4 +1,4 @@
-from tensorflow.keras.callbacks import Callback
+from tensorflow.keras.callbacks import Callback, LearningRateScheduler
import numpy as np
import matplotlib.pyplot as plt
import os
@@ -33,3 +33,14 @@ class ImagesCallback(Callback):
def on_epoch_begin(self, epoch, logs={}):
self.epoch += 1
+
+def step_decay_schedule(initial_lr, decay_factor=0.5, step_size=1):
+ '''
+ Wrapper function to create a LearningRateScheduler with step decay schedule.
+ '''
+ def schedule(epoch):
+ new_lr = initial_lr * (decay_factor ** np.floor(epoch/step_size))
+
+ return new_lr
+
+ return LearningRateScheduler(schedule)
\ No newline at end of file
diff --git a/VAE/modules/vae.py b/VAE/modules/vae.py
index f9efa3b28a5264e49631b3f5b29daef4a7e74df7..e98409b9e1fa27bb01ec3e0d24413bbe87588ca5 100644
--- a/VAE/modules/vae.py
+++ b/VAE/modules/vae.py
@@ -6,7 +6,7 @@ from tensorflow.keras import backend as K
from tensorflow.keras.layers import Input, Conv2D, Flatten, Dense, Conv2DTranspose, Reshape, Lambda
from tensorflow.keras.layers import Activation, BatchNormalization, LeakyReLU, Dropout
from tensorflow.keras.models import Model
-from tensorflow.keras.callbacks import ModelCheckpoint
+from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import plot_model
@@ -161,18 +161,25 @@ class VariationalAutoencoder():
self.n_test = n_test
self.batch_size = batch_size
- # ---- Callbacks
- images_callback = modules.callbacks.ImagesCallback(initial_epoch, image_periodicity, self)
+ # ---- Callback : Images
+ callbacks_images = modules.callbacks.ImagesCallback(initial_epoch, image_periodicity, self)
-# lr_sched = step_decay_schedule(initial_lr=self.learning_rate, decay_factor=lr_decay, step_size=1)
+ # ---- Callback : Learning rate scheduler
+ lr_sched = modules.callbacks.step_decay_schedule(initial_lr=self.learning_rate, decay_factor=lr_decay, step_size=1)
- filename1 = self.run_directory+"/models/model-{epoch:03d}-{loss:.2f}.h5"
- checkpoint1 = ModelCheckpoint(filename1, save_freq=n_train*chkpt_periodicity ,verbose=0)
+ # ---- Callback : Checkpoint
+ filename = self.run_directory+"/models/model-{epoch:03d}-{loss:.2f}.h5"
+ callback_chkpts = ModelCheckpoint(filename, save_freq=n_train*chkpt_periodicity ,verbose=0)
- filename2 = self.run_directory+"/models/best_model.h5"
- checkpoint2 = ModelCheckpoint(filename2, save_best_only=True, mode='min',monitor='val_loss',verbose=0)
+ # ---- Callback : Best model
+ filename = self.run_directory+"/models/best_model.h5"
+ callback_bestmodel = ModelCheckpoint(filename, save_best_only=True, mode='min',monitor='val_loss',verbose=0)
- callbacks_list = [checkpoint1, checkpoint2, images_callback]
+ # ---- Callback tensorboard
+ dirname = self.run_directory+"/logs"
+ callback_tensorboard = TensorBoard(log_dir=dirname, histogram_freq=1)
+
+ callbacks_list = [callbacks_images, callback_chkpts, callback_bestmodel, callback_tensorboard, lr_sched]
self.model.fit(x_train[:n_train], x_train[:n_train],
batch_size = batch_size,
@@ -189,3 +196,5 @@ class VariationalAutoencoder():
plot_model(self.model, to_file=f'{d}/model.png', show_shapes = True, show_layer_names = True, expand_nested=True)
plot_model(self.encoder, to_file=f'{d}/encoder.png', show_shapes = True, show_layer_names = True)
plot_model(self.decoder, to_file=f'{d}/decoder.png', show_shapes = True, show_layer_names = True)
+
+