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feat: Added minor updates in comments for clarity and proposed some u… #44

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feat: Added minor updates in comments for clarity and proposed some u… #44

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31 changes: 26 additions & 5 deletions notebooks/04_gan/03_cgan/cgan.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -70,12 +70,13 @@
"BATCH_SIZE = 128\n",
"Z_DIM = 32\n",
"LEARNING_RATE = 0.00005\n",
"ADAM_BETA_1 = 0.5\n",
"ADAM_BETA_2 = 0.999\n",
"EPOCHS = 20\n",
"CRITIC_STEPS = 3\n",
"GP_WEIGHT = 10.0\n",
"LOAD_MODEL = False\n",
"# Uncomment below if you want to play with these parameters and see how they are affect learning process\n",
"#ADAM_BETA_1 = 0.5\n",
"#ADAM_BETA_2 = 0.999\n",
"ADAM_BETA_1 = 0.5\n",
"ADAM_BETA_2 = 0.9\n",
"LABEL = \"Blond_Hair\""
Expand Down Expand Up @@ -162,7 +163,8 @@
"outputs": [],
"source": [
"# Show some faces from the training set\n",
"train_sample = sample_batch(train)"
"#train_sample = sample_batch(train)\n",
"#display(train_sample, cmap=None)"
]
},
{
Expand All @@ -172,7 +174,25 @@
"metadata": {},
"outputs": [],
"source": [
"display(train_sample, cmap=None)"
"# Show some faces from the training set\n",
"# The updated version of the image display below, mainly concentrated on 2 things:\n",
"# 1. Make sure that only blond hair celebrties being selected in example\n",
"# 2. Show explicitly filtered labels and image shape for education purposes\n",
"\n",
"for img_batch, label_batch in train_data.take(1):\n",
" # Create filter mask for labels=1 (i.e. Blond_Hair)\n",
" mask=tf.equal(label_batch,1)\n",
"\n",
" # Apply mask for filter labels and images\n",
" filtered_images=tf.boolean_mask(img_batch, mask)\n",
" filtered_labels=tf.boolean_mask(label_batch, mask)\n",
" # Convert to numpy array\n",
" filtered_images_np=filtered_images.numpy()\n",
"\n",
" print('Filtered images shape: ', filtered_images.shape)\n",
" print('filtered labels: ', filtered_labels)\n",
"\n",
"display(filtered_images_np, cmap=None)"
]
},
{
Expand Down Expand Up @@ -435,6 +455,7 @@
"source": [
"# Create a model save checkpoint\n",
"model_checkpoint_callback = callbacks.ModelCheckpoint(\n",
" # NOTE: For tensorflow 11 and upper you may be required to change extension from .ckpt to .weights.h5\n",
" filepath=\"./checkpoint/checkpoint.ckpt\",\n",
" save_weights_only=True,\n",
" save_freq=\"epoch\",\n",
Expand Down Expand Up @@ -492,7 +513,7 @@
"history = cgan.fit(\n",
" train,\n",
" epochs=EPOCHS * 100,\n",
" steps_per_epoch=1,\n",
" steps_per_epoch=1, # NOTE: You probably want to tune up this parameter. Steps per epoch=1 means that is literally take one batch per epoch, this will cause rapid education but very poor results.\n",
" callbacks=[\n",
" model_checkpoint_callback,\n",
" tensorboard_callback,\n",
Expand Down