def code_classifier_forward(config, incoming=None, image=None,
scope="code_classifier", name=None, reuse=False):
with tf.variable_scope(scope, name, reuse=reuse):
code_output = leaky_relu(fully_connected(incoming, 512))
output = leaky_relu(fully_connected(tf.reshape(image, [config.batch_size, 28 * 28]), 512))
prod = tf.matmul(code_output[:, :, None], output[:, None, :])
prob = tf.nn.softmax(prod)
prob2 = tf.nn.softmax(tf.transpose(prod, perm=[0, 2, 1]))
output = tf.concat([code_output,
tf.matmul(prob, output[:, :, None])[:, :, 0],
tf.matmul(prob2, code_output[:, :, None])[:, :, 0]], axis=-1)
output = relu(fully_connected(output, 1024))
output = dropout(output, 0.6)
output = relu(fully_connected(output, 512))
output = dropout(output, 0.6)
output = relu(fully_connected(output, 256))
output = dropout(output, 0.8)
output = fully_connected(output, 10)
return output
def discriminator_forward(config,
incoming,
scope="discriminator",
name=None,
reuse=False):
with tf.variable_scope(scope, name, reuse=reuse):
output = conv_2d(incoming, config.gen_dim, 5, strides=2, name="conv1")
output = leaky_relu(output, 0.2)
output = conv_2d(output,
2 * config.gen_dim,
5,
strides=2,
name="conv2")
output = leaky_relu(output, 0.2)
output = conv_2d(output,
4 * config.gen_dim,
5,
strides=2,
name="conv3")
output = leaky_relu(output, 0.2)
output = conv_2d(output,
8 * config.gen_dim,
5,
strides=2,
name="conv4")
output = leaky_relu(output, 0.2)
output = tf.reshape(output, [-1, 4 * 4 * 8 * config.gen_dim])
output = fully_connected(output, 1, bias=False)
return output
def discriminator_forward(config,
incoming,
labels,
scope="discriminator",
name=None,
reuse=False):
with tf.variable_scope(scope, name, reuse=reuse):
output = leaky_relu(
batch_normalization(
conv_2d(incoming, config.dim, 5, 2, name="conv1")), 0.2)
output = leaky_relu(
batch_normalization(
conv_2d(output, 2 * config.dim, 5, 2, name="conv2")), 0.2)
output_shared = conv_2d(output,
2 * config.dim,
5,
2,
name="conv3_shared")
output_cs = [
conv_2d(output, 2 * config.dim, 5, 2, name="conv3_cs")
for _ in xrange(5)
]
output = tf.concat([output_cs, output_shared])
output = tf.reshape(output, [-1, 4 * 4 * 4 * config.dim])
output = tf.reshape(fully_connected(output, 1, bias=False), [-10])
def discriminator_forward(config,
labels,
incoming,
scope="discriminator",
name=None,
reuse=False):
with tf.variable_scope(scope, name, reuse=reuse):
output = leaky_relu(
batch_normalization(
conv_2d(incoming, config.dim, 5, 2, name="conv1"), 0.2))
output = leaky_relu(
batch_normalization(
conv_2d(output, 2 * config.dim, 5, 2, name="conv2"), 0.2))
output = leaky_relu(
batch_normalization(
conv_2d(output, 4 * config.dim, 5, 2, name="Conv3"), 0.2))
output = tf.reshape(output, [-1, 4 * 4 * 4 * config.dim])
output = fully_connected(output, 56 * config.dim, name="fc1_1")
embed = fully_connected(labels, 8 * config.dim, name="fc1_2")
output = leaky_relu(
batch_normalization(tf.concat([output, embed], axis=-1)), 0.2)
output = fully_connected(output, 8 * config.dim, name="fc2")
output = batch_normalization(output)
output = leaky_relu(output, 0.2)
output = tf.reshape(fully_connected(output, 1, bias=False, name="fc3"),
[-1])
return output
def discriminator_forward(config,
incoming,
scope="discriminator",
name=None,
reuse=False):
with tf.variable_scope(scope, name, reuse=reuse):
output = leaky_relu(conv_2d(incoming, config.dim, 5, 2), 0.2)
output = leaky_relu(conv_2d(output, 2 * config.dim, 5, 2), 0.2)
output = leaky_relu(conv_2d(output, 4 * config.dim, 5, 2), 0.2)
output = tf.reshape(output, [-1, 4 * 4 * 4 * config.dim])
output = tf.reshape(fully_connected(output, 1, bias=False), [-1])
return output
def code_classifier_forward(config,
incoming=None,
image=None,
scope="code_classifier",
name=None,
reuse=False):
with tf.variable_scope(scope, name, reuse=reuse):
code_output = leaky_relu(fully_connected(incoming, 512))
output = conv_2d(image, 32, 5, 2, name="conv1")
output = residual_block(output,
2,
32,
downsample=True,
batch_norm=True,
name="rb1")
output = residual_block(output,
1,
64,
downsample=True,
batch_norm=True,
name="rb2")
output = leaky_relu(
fully_connected(
tf.reshape(output, [config.batch_size, 4 * 4 * 64]), 1024))
prod = tf.matmul(code_output[:, :, None], output[:, None, :])
prob = tf.nn.softmax(prod)
prob2 = tf.nn.softmax(tf.transpose(prod, perm=[0, 2, 1]))
output = tf.concat([
code_output,
tf.matmul(prob, output[:, :, None])[:, :, 0],
tf.matmul(prob2, code_output[:, :, None])[:, :, 0]
],
axis=-1)
output = relu(fully_connected(output, 1024))
output = dropout(output, 0.6)
output = relu(fully_connected(output, 512))
output = dropout(output, 0.6)
output = relu(fully_connected(output, 256))
output = dropout(output, 0.8)
output = fully_connected(output, 5)
return output