def generator(self, z, reuse=False):
with tf.variable_scope('generator', reuse=reuse):
layer = Stacker(z)
layer.add_layer(linear, 7 * 7 * 128)
layer.reshape([self.batch_size, 7, 7, 128])
layer.upscale_2x_block(256, CONV_FILTER_5522, relu)
layer.conv2d_transpose(self.Xs_shape, CONV_FILTER_5522)
layer.conv2d(self.input_c, CONV_FILTER_3311)
layer.sigmoid()
return layer.last_layer
def discriminator(self, x, reuse=None, name='discriminator'):
with tf.variable_scope(name, reuse=reuse):
layer = Stacker(x)
layer.add_layer(conv2d, 64, CONV_FILTER_5522)
layer.add_layer(bn)
layer.add_layer(lrelu)
layer.add_layer(conv2d, 128, CONV_FILTER_5522)
layer.add_layer(bn)
layer.add_layer(lrelu)
layer.add_layer(conv2d, 256, CONV_FILTER_5522)
layer.add_layer(bn)
layer.add_layer(lrelu)
layer.add_layer(conv2d, 256, CONV_FILTER_5522)
layer.add_layer(bn)
layer.add_layer(lrelu)
layer.add_layer(tf.reshape, [self.batch_size, -1])
out_logit = layer.add_layer(linear, 1)
out = layer.add_layer(tf.sigmoid)
return out, out_logit
def generator(self, z, reuse=False, name='generator'):
with tf.variable_scope(name, reuse=reuse):
layer = Stacker(z)
layer.add_layer(linear, 4 * 4 * 512)
layer.add_layer(tf.reshape, [self.batch_size, 4, 4, 512])
layer.add_layer(conv2d_transpose, [self.batch_size, 8, 8, 256],
CONV_FILTER_7722)
layer.add_layer(bn)
layer.add_layer(relu)
layer.add_layer(conv2d_transpose, [self.batch_size, 16, 16, 128],
CONV_FILTER_7722)
layer.add_layer(bn)
layer.add_layer(relu)
layer.add_layer(conv2d_transpose,
[self.batch_size, 32, 32, self.input_c],
CONV_FILTER_7722)
layer.add_layer(conv2d, self.input_c, CONV_FILTER_5511)
layer.add_layer(tf.sigmoid)
net = layer.last_layer
return net
def inception_layer(input_, channel_size, name='inception_layer'):
with tf.variable_scope(name):
with tf.variable_scope('out1'):
layer = Stacker(input_)
layer.add_layer(avg_pooling, CONV_FILTER_2211)
out1 = layer.last_layer
with tf.variable_scope('out2'):
layer = Stacker(input_)
layer.add_layer(conv_block, channel_size, CONV_FILTER_5511, lrelu)
out2 = layer.last_layer
with tf.variable_scope('out3'):
layer = Stacker(input_)
layer.add_layer(conv_block, channel_size, CONV_FILTER_5511, lrelu)
layer.add_layer(conv_block, channel_size, CONV_FILTER_5511, relu)
out3 = layer.last_layer
with tf.variable_scope('out4'):
layer = Stacker(input_)
layer.add_layer(conv_block, channel_size, CONV_FILTER_5511, lrelu)
layer.add_layer(conv_block, channel_size, CONV_FILTER_5511, lrelu)
layer.add_layer(conv_block, channel_size, CONV_FILTER_5511, lrelu)
out4 = layer.last_layer
out = tf.concat([out1, out2 + out3 + out4], 3)
return out
def CNN(self, input_):
with tf.variable_scope('classifier'):
layer = Stacker(input_, name='seq1')
layer.add_layer(conv_block, 64, CONV_FILTER_5522, lrelu)
size16 = layer.last_layer
layer.add_layer(inception_layer, 32)
layer.add_layer(inception_layer, 64)
layer.add_layer(inception_layer, 128)
layer.add_layer(tf.reshape, [self.batch_size, -1])
layer2 = Stacker(size16, name='seq2')
layer2.add_layer(conv_block, 128, CONV_FILTER_5522, lrelu)
size8 = layer2.last_layer
layer2.add_layer(inception_layer, 64)
layer2.add_layer(inception_layer, 128)
layer2.add_layer(inception_layer, 256)
layer2.add_layer(tf.reshape, [self.batch_size, -1])
layer3 = Stacker(size8, name='seq3')
layer3.add_layer(conv_block, 256, CONV_FILTER_5522, lrelu)
layer3.add_layer(inception_layer, 128)
layer3.add_layer(inception_layer, 256)
layer3.add_layer(inception_layer, 512)
layer3.add_layer(tf.reshape, [self.batch_size, -1])
merge = tf.concat(
[layer.last_layer, layer2.last_layer, layer3.last_layer],
axis=1)
after_merge = Stacker(merge, name='after_merge')
after_merge.add_layer(linear, self.label_size)
logit = after_merge.last_layer
h = softmax(logit)
return logit, h
