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@Laurans /mtg_discriminator.py Secret
Last active May 10, 2018

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def discriminator(images, reuse=False):
filters = [64, 128, 256, 512, 256]
alpha = 0.18
with tf.variable_scope('discriminator', reuse=reuse):
x = tf.layers.conv2d(images, filters[0], 5, strides=2, padding='same', activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
x = tf.maximum(alpha * x, x)
for size in filters[1:]:
x = tf.layers.conv2d(x, size, 5, strides=2, padding='same', activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
bn = tf.layers.batch_normalization(x, training=True)
relu = tf.maximum(alpha * bn, bn)
x = tf.layers.dropout(relu, 0.3)
# Flatten
flat = tf.reshape(x, (-1, np.prod(relu.shape[1:])))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
def generator(z, out_channel_dim, is_train=True):
alpha = 0.1
filters = [1024, 512, 256, 128, 64, 32]
strides = [2, 2, 2, 2, 1]
with tf.variable_scope('generator', reuse=not is_train):
# First fully connected layer
x = tf.layers.dense(z, 7*7*filters[0])
# Reshape it to start the convolutional stack
x = tf.reshape(x, (-1, 7, 7, filters[0]))
bn = tf.layers.batch_normalization(x, training=is_train)
x = tf.maximum(alpha * bn, bn)
for size, stride in zip(filters[1:], strides):
noise = tf.random_normal(shape=tf.shape(x), mean=0.0, stddev=0.2, dtype=tf.float32)
x = x + noise
x = tf.layers.conv2d_transpose(x, size, 5, strides=stride, padding='same', activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
bn = tf.layers.batch_normalization(x, training=is_train)
relu = tf.maximum(alpha * bn, bn)
x = tf.layers.dropout(relu, 0.3)
logits = tf.layers.conv2d_transpose(x, out_channel_dim, 5, strides=2, padding='same', activation=None,
kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
out = tf.tanh(logits)
return out
def model_loss(input_real, input_z, out_channel_dim):
g_model = generator(input_z, out_channel_dim)
_ , d_logits_real = discriminator(input_real)
_ , d_logits_fake = discriminator(g_model, reuse=True)
smooth1 = tf.random_uniform(tf.shape(d_logits_real), minval=0, maxval=0.2)
smooth0 = tf.random_uniform(tf.shape(d_logits_fake), minval=0, maxval=0.2)
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_real, labels=tf.ones_like(d_logits_real) - smooth1))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake) + smooth0))
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
while train_loss_d > 2:
# Train discrimator and get loss in train_loss_d
while train_loss_g > train_loss_d:
# Train generator and get loss in train_loss_g
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