def discriminator(self, pict, text, reuse=False):
print "discriminator"
with tf.variable_scope("discriminator") as scope:
if reuse: scope.reuse_variables()
if reuse == False: print pict.shape
x0 = my_lib.lrelu(
my_lib.conv2d(pict, [5, 5, 3, self.pict_dim], 'd_0'))
if reuse == False: print x0.shape
x1 = my_lib.lrelu(
my_lib.batch_norm(
my_lib.conv2d(x0, [5, 5, self.pict_dim, self.pict_dim * 2],
'd_1'), 'd_bn1'))
if reuse == False: print x1.shape
x2 = my_lib.lrelu(
my_lib.batch_norm(
my_lib.conv2d(x1,
[5, 5, self.pict_dim * 2, self.pict_dim * 4],
'd_2'), 'd_bn2'))
if reuse == False: print x2.shape
x3 = my_lib.lrelu(
my_lib.batch_norm(
my_lib.conv2d(x2,
[5, 5, self.pict_dim * 4, self.pict_dim * 8],
'd_3'), 'd_bn3'))
if reuse == False: print x3.shape
x3 = tf.reshape(x3, [-1, 8192])
x3 = tf.concat([self.text_encoding, x3], axis=1)
if reuse == False: print x3.shape
x4 = my_lib.matmul(x3, 8192 + 128, 1, 'd_4')
if reuse == False: print x4.shape
return tf.nn.sigmoid(x4), x4
def generator(self, z):
print "generator"
with tf.variable_scope("generator") as scope: # may be useless
self.text_encoding = my_lib.matmul(self.right_text_nn,
self.text_dim, 128, 'g_txt')
x0 = tf.concat([z, self.text_encoding], axis=1)
print x0.shape
x1 = tf.nn.relu(
my_lib.matmul(x0, self.noise_dim + 128,
self.pict_dim * 16 * 4 * 4, 'g_0'))
x1 = tf.reshape(x1, [-1, 4, 4, self.pict_dim * 16])
x1 = tf.nn.relu(my_lib.batch_norm(x1, 'g_bn1'))
print x1.shape
x2 = my_lib.conv2d_transpose(
x1, [5, 5, self.pict_dim * 8, self.pict_dim * 16],
[self.batch_size, 8, 8, self.pict_dim * 8], 'g_1')
x2 = tf.nn.relu(my_lib.batch_norm(x2, 'g_bn2'))
print x2.shape
x3 = my_lib.conv2d_transpose(
x2, [5, 5, self.pict_dim * 4, self.pict_dim * 8],
[self.batch_size, 16, 16, self.pict_dim * 4], 'g_2')
x3 = tf.nn.relu(my_lib.batch_norm(x3, 'g_bn3'))
print x3.shape
x4 = my_lib.conv2d_transpose(
x3, [5, 5, self.pict_dim * 2, self.pict_dim * 4],
[self.batch_size, 32, 32, self.pict_dim * 2], 'g_3')
x4 = tf.nn.relu(my_lib.batch_norm(x4, 'g_bn4'))
print x4.shape
x5 = my_lib.conv2d_transpose(x4, [5, 5, 3, self.pict_dim * 2],
[self.batch_size, 64, 64, 3], 'g_4')
print x5.shape
return tf.nn.tanh(x5)