DCGAN.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import os\n",
"from glob import glob\n",
"import numpy as np\n",
"from matplotlib import pyplot\n",
"from PIL import Image\n",
"import tensorflow as tf\n",
"\n",
"##README : IF output folder already existed in same route, it makes error. change past output folder's name ##"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"class Dataset(object):\n",
" def __init__(self, data_files):\n",
" IMAGE_WIDTH = 25\n",
" IMAGE_HEIGHT = 25\n",
" self.image_mode = 'RGB'\n",
" image_channels = 3\n",
" self.data_files = data_files\n",
" self.shape = len(data_files), IMAGE_WIDTH, IMAGE_HEIGHT, image_channels\n",
" \n",
" def get_image(iself,mage_path, width, height, mode):\n",
" image = Image.open(image_path)\n",
" image = image.resize((width,height))\n",
" return np.array(image)\n",
"\n",
"\n",
" def get_batch(self,image_files, width, height, mode):\n",
" data_batch = np.array(\n",
" [self.get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32)\n",
" \n",
" # Make sure the images are in 4 dimensions\n",
" if len(data_batch.shape) < 4:\n",
" data_batch = data_batch.reshape(data_batch.shape + (1,))\n",
" return data_batch\n",
"\n",
" def get_batches(self, batch_size):\n",
" IMAGE_MAX_VALUE = 255\n",
" current_index = 0\n",
" while current_index + batch_size <= self.shape[0]:\n",
" data_batch = self.get_batch(\n",
" self.data_files[current_index:current_index + batch_size],\n",
" self.shape[1],self.shape[2],\n",
" self.image_mode)\n",
" \n",
" current_index += batch_size\n",
" \n",
" yield data_batch / IMAGE_MAX_VALUE - 0.5\n",
"\n",
"\n",
"def model_inputs(image_width, image_height, image_channels, z_dim):\n",
" real_input_images = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], 'real_input_images')\n",
" input_z = tf.placeholder(tf.float32, [None, z_dim], 'input_z')\n",
" learning_rate = tf.placeholder(tf.float32, [], 'learning_rate')\n",
" return real_input_images, input_z, learning_rate\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def discriminator(images, reuse=False, alpha=0.2, keep_prob=0.5):\n",
" with tf.variable_scope('discriminator', reuse=reuse):\n",
" # Input layer is 25x25xn\n",
" # Convolutional layer, 13x13x64\n",
" conv1 = tf.layers.conv2d(images, 64, 5, 2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())\n",
" lrelu1 = tf.maximum(alpha * conv1, conv1)\n",
" drop1 = tf.layers.dropout(lrelu1, keep_prob)\n",
" \n",
" # Strided convolutional layer, 7x7x128\n",
" conv2 = tf.layers.conv2d(drop1, 128, 5, 2, 'same', use_bias=False)\n",
" bn2 = tf.layers.batch_normalization(conv2)\n",
" lrelu2 = tf.maximum(alpha * bn2, bn2)\n",
" drop2 = tf.layers.dropout(lrelu2, keep_prob)\n",
" \n",
" # Strided convolutional layer, 4x4x256\n",
" conv3 = tf.layers.conv2d(drop2, 256, 5, 2, 'same', use_bias=False)\n",
" bn3 = tf.layers.batch_normalization(conv3)\n",
" lrelu3 = tf.maximum(alpha * bn3, bn3)\n",
" drop3 = tf.layers.dropout(lrelu3, keep_prob)\n",
" \n",
" # fully connected\n",
" flat = tf.reshape(drop3, (-1, 4*4*256))\n",
" logits = tf.layers.dense(flat, 1)\n",
" out = tf.sigmoid(logits)\n",
" \n",
" return out, logits"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def generator(z, out_channel_dim, is_train=True, alpha=0.2, keep_prob=0.5):\n",
" # TODO: Implement Function\n",
" with tf.variable_scope('generator', reuse=(not is_train)):\n",
" # First fully connected layer, 8x4x512\n",
" fc = tf.layers.dense(z, 4*4*1024, use_bias=False)\n",
" fc = tf.reshape(fc, (-1, 4, 4, 1024))\n",
" bn0 = tf.layers.batch_normalization(fc, training=is_train)\n",
" lrelu0 = tf.maximum(alpha * bn0, bn0)\n",
" drop0 = tf.layers.dropout(lrelu0, keep_prob, training=is_train)\n",
" \n",
" # Deconvolution, 16x8x256\n",
" conv1 = tf.layers.conv2d_transpose(drop0, 512,3, 1, 'valid', use_bias=False)\n",
" bn1 = tf.layers.batch_normalization(conv1, training=is_train)\n",
" lrelu1 = tf.maximum(alpha * bn1, bn1)\n",
" drop1 = tf.layers.dropout(lrelu1, keep_prob, training=is_train)\n",
" \n",
" # Deconvolution, 32x 128\n",
" conv2 = tf.layers.conv2d_transpose(drop1, 256, 3, 2, 'same', use_bias=False)\n",
" bn2 = tf.layers.batch_normalization(conv2, training=is_train)\n",
" lrelu2 = tf.maximum(alpha * bn2, bn2)\n",
" drop2 = tf.layers.dropout(lrelu2, keep_prob, training=is_train)\n",
" \n",
" # Output layer, 28x28xn\n",
" logits = tf.layers.conv2d_transpose(drop2, out_channel_dim, 3, 2, 'valid')\n",
" \n",
" out = tf.tanh(logits)\n",
" \n",
" print(fc.shape)\n",
" print(drop1.shape)\n",
" print(drop2.shape)\n",
" print(logits.shape)\n",
" \n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def model_loss(input_real, input_z, out_channel_dim, alpha=0.2, smooth_factor=0.1):\n",
" d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)\n",
" \n",
" d_loss_real = tf.reduce_mean(\n",
" tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,\n",
" labels=tf.ones_like(d_model_real) * (1 - smooth_factor)))\n",
" \n",
" input_fake = generator(input_z, out_channel_dim, alpha=alpha)\n",
" d_model_fake, d_logits_fake = discriminator(input_fake, reuse=True, alpha=alpha)\n",
" \n",
" d_loss_fake = tf.reduce_mean(\n",
" tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))\n",
" \n",
" g_loss = tf.reduce_mean(\n",
" tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))\n",
"\n",
" return d_loss_real + d_loss_fake, g_loss\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def model_opt(d_loss, g_loss, learning_rate, beta1):\n",
" # Get weights and bias to update\n",
" t_vars = tf.trainable_variables()\n",
" d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n",
" g_vars = [var for var in t_vars if var.name.startswith('generator')]\n",
"\n",
" # Optimize\n",
" with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
" d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)\n",
" g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)\n",
"\n",
" return d_train_opt, g_train_opt\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):\n",
" cmap = None if image_mode == 'RGB' else 'gray'\n",
" z_dim = input_z.get_shape().as_list()[-1]\n",
" example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])\n",
"\n",
" samples = sess.run(\n",
" generator(input_z, out_channel_dim, False),\n",
" feed_dict={input_z: example_z})\n",
" \n",
" # pyplot.show()\n",
" return samples"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode,\n",
" print_every=10, show_every=10):\n",
" # TODO: Build Model\n",
" input_real, input_z, _ = model_inputs(data_shape[2], data_shape[1], data_shape[3], z_dim)\n",
" d_loss, g_loss = model_loss(input_real, input_z, data_shape[3], alpha=0.2)\n",
" d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)\n",
" \n",
" saver = tf.train.Saver()\n",
" sample_z = np.random.uniform(-1, 1, size=(72, z_dim))\n",
" \n",
" samples, losses = [], []\n",
" \n",
" steps = 0\n",
" count = 0\n",
" \n",
" with tf.Session() as sess:\n",
" saver = tf.train.Saver()\n",
" sess.run(tf.global_variables_initializer())\n",
" \n",
" # continue training\n",
" save_path = saver.save(sess, \"/tmp/model.ckpt\")\n",
" ckpt = tf.train.latest_checkpoint('./model/')\n",
" saver.restore(sess, save_path)\n",
" coord = tf.train.Coordinator()\n",
" threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n",
"\n",
" os.mkdir('output')\n",
" for epoch_i in range(epoch_count):\n",
" for batch_images in get_batches(batch_size):\n",
" # Train Model\n",
" steps += 1\n",
" batch_images *= 2.0\n",
" \n",
" # Sample random noise for G\n",
" batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))\n",
" \n",
" # Run optimizers\n",
" sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})\n",
" sess.run(g_train_opt, feed_dict={input_z: batch_z})\n",
" \n",
" if steps % print_every == 0:\n",
" os.mkdir('output/'+ str(steps))\n",
" # At the end of each epoch, get the losses and print them out\n",
" train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})\n",
" train_loss_g = g_loss.eval({input_z: batch_z})\n",
" print(\"Epoch {}/{} Step {}...\".format(epoch_i+1, epoch_count, steps),\n",
" \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n",
" \"Generator Loss: {:.4f}\".format(train_loss_g))\n",
" # Save losses for viewing after training\n",
" #losses.append((train_loss_d, train_loss_g))\n",
" \n",
" if steps % show_every == 0:\n",
" count = count +1\n",
" iterr = count*show_every\n",
" # Show example output for the generator # 25 number for 1 time\n",
" images_grid = show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)\n",
" x = 0\n",
" for image_grid in images_grid : \n",
" x = x+1\n",
" dst = os.path.join(\"output\", str(steps),str(iterr)+str(x)+\".png\")\n",
" pyplot.imsave(dst, image_grid)\n",
" \n",
" # saving the model \n",
" if epoch_i % 10 == 0:\n",
" if not os.path.exists('./model/'):\n",
" os.makedirs('./model')\n",
" saver.save(sess, './model/' + str(epoch_i)) "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"140\n",
"(?, 4, 4, 1024)\n",
"(?, 6, 6, 512)\n",
"(?, 12, 12, 256)\n",
"(?, 25, 25, 3)\n",
"INFO:tensorflow:Restoring parameters from /tmp/model.ckpt\n"
]
},
{
"ename": "NameError",
"evalue": "name 'image_path' 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-9-3cf64f8b526a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mceleba_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./motionpatch/*.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_default\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---> 10\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz_dim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mceleba_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mceleba_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mceleba_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage_mode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-8-14a3faf19639>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, print_every, show_every)\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'output'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mepoch_i\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mbatch_images\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mget_batches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\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 30\u001b[0m \u001b[0;31m# Train Model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0msteps\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-2-77ee1ea74a0f>\u001b[0m in \u001b[0;36mget_batches\u001b[0;34m(self, batch_size)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_files\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcurrent_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mcurrent_index\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m self.image_mode)\n\u001b[0m\u001b[1;32m 33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0mcurrent_index\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-2-77ee1ea74a0f>\u001b[0m in \u001b[0;36mget_batch\u001b[0;34m(self, image_files, width, height, mode)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mimage_files\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m data_batch = np.array(\n\u001b[0;32m---> 18\u001b[0;31m [self.get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32)\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;31m# Make sure the images are in 4 dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-2-77ee1ea74a0f>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mimage_files\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m data_batch = np.array(\n\u001b[0;32m---> 18\u001b[0;31m [self.get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32)\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;31m# Make sure the images are in 4 dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-2-77ee1ea74a0f>\u001b[0m in \u001b[0;36mget_image\u001b[0;34m(iself, mage_path, width, height, mode)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmage_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'image_path' is not defined"
]
}
],
"source": [
"batch_size = 50\n",
"z_dim = 100\n",
"learning_rate = 0.00025\n",
"beta1 = 0.45\n",
"\n",
"epochs = 500\n",
"print(len(glob('./motionpatch/*.png')))\n",
"celeba_dataset = Dataset( glob('./motionpatch/*.png'))\n",
"with tf.Graph().as_default():\n",
" train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches, celeba_dataset.shape, celeba_dataset.image_mode)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
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"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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