def generator(self, inputs, is_training, reuse=False):
with tf.variable_scope('generator', reuse=reuse):
g_hidden1 = fully_connect(inputs, 1024, name='g_hidden1')
g_hidden2 = lrelu(bn(fully_connect(g_hidden1, 7*7*128), is_training))
g_reshape = tf.reshape(g_hidden2, [-1, 7, 7, 128])
g_hidden3 = lrelu(bn(conv2d_t(g_reshape, [None, 14, 14, 64], [4, 4], name='g_hidden3'), is_training))
g_hidden4 = conv2d_t(g_hidden3, [None, 28, 28, 1], [4, 4], name='g_hidden4')
g_logtis = sigmoid(g_hidden4)
return g_hidden4, g_logtis
def discriminator(self, inputs, is_training, reuse=False):
with tf.variable_scope('discriminator', reuse=reuse):
d_hidden1 = conv2d(inputs, 64, [4, 4], strides=[1, 2, 2, 1], name='d_hidden1')
d_hidden2 = lrelu(bn(conv2d(d_hidden1, 128, [4, 4], strides=[1, 2, 2, 1], name='d_hidden2'), is_training))
d_flatten = tf.reshape(d_hidden2, [-1, 7*7*128])
d_hidden3 = lrelu(bn(fully_connect(d_flatten, 1024, name='d_hidden3'), is_training))
d_hidden4 = fully_connect(d_hidden3, 1, name='d_hidden4')
d_logits = sigmoid(d_hidden4)
return d_hidden4, d_logits
def make_model(self, inputs, is_training):
with tf.variable_scope('STAGE_1'):
layer = relu(
bn(
conv2d(inputs,
64, [7, 7],
strides=[1, 2, 2, 1],
name='initial_block'), is_training))
layer = max_pool(layer)
with tf.variable_scope('STAGE_2'):
layer = self.conv_block(layer, [64, 64, 256],
is_training,
'a',
s=1)
layer = self.identity_block(layer, [64, 64, 256], is_training, 'b')
layer = self.identity_block(layer, [64, 64, 256], is_training, 'c')
with tf.variable_scope('STAGE_3'):
layer = self.conv_block(layer, [128, 128, 512], is_training, 'a')
layer = self.identity_block(layer, [128, 128, 512], is_training,
'b')
layer = self.identity_block(layer, [128, 128, 512], is_training,
'c')
with tf.variable_scope('STAGE_4'):
layer = self.conv_block(layer, [256, 256, 1024], is_training, 'a')
layer = self.identity_block(layer, [256, 256, 1024], is_training,
'b')
layer = self.identity_block(layer, [256, 256, 1024], is_training,
'c')
layer = self.identity_block(layer, [256, 256, 1024], is_training,
'd')
layer = self.identity_block(layer, [256, 256, 1024], is_training,
'e')
layer = self.identity_block(layer, [256, 256, 1024], is_training,
'f')
with tf.variable_scope('STAGE_5'):
layer = self.conv_block(layer, [512, 512, 2048], is_training, 'a')
layer = self.identity_block(layer, [512, 512, 2048], is_training,
'b')
layer = self.identity_block(layer, [512, 512, 2048], is_training,
'c')
with tf.variable_scope('FINAL_STAGE'):
layer = avg_pool(layer, [1, 7, 7, 1], [1, 1, 1, 1],
padding='VALID')
_, h, w, d = layer.get_shape().as_list()
layer = tf.reshape(layer, [-1, h * w * d])
layer = fully_connect(layer, self.N_CLASS, 'fc')
return layer
def make_model(self, inputs, keep_prob):
conv1_1 = conv2d(inputs, 64, [3, 3], name='conv1_1')
conv1_2 = conv2d(conv1_1, 64, [3, 3], name='conv1_2')
pool1 = max_pool(conv1_2, name='pool1')
conv2_1 = conv2d(pool1, 128, [3, 3], name='conv2_1')
conv2_2 = conv2d(conv2_1, 128, [3, 3], name='conv2_2')
pool2 = max_pool(conv2_2, name='pool2')
conv3_1 = conv2d(pool2, 256, [3, 3], name='conv3_1')
conv3_2 = conv2d(conv3_1, 256, [3, 3], name='conv3_2')
conv3_3 = conv2d(conv3_2, 256, [3, 3], name='conv3_3')
pool3 = max_pool(conv3_3, name='pool3')
conv4_1 = conv2d(pool3, 512, [3, 3], name='conv4_1')
conv4_2 = conv2d(conv4_1, 512, [3, 3], name='conv4_2')
conv4_3 = conv2d(conv4_2, 512, [3, 3], name='conv4_3')
pool4 = max_pool(conv4_3, name='pool4')
conv5_1 = conv2d(pool4, 512, [3, 3], name='conv5_1')
conv5_2 = conv2d(conv5_1, 512, [3, 3], name='conv5_2')
conv5_3 = conv2d(conv5_2, 512, [3, 3], name='conv5_3')
_, h, w, d = conv5_3.get_shape().as_list()
flatten = tf.reshape(conv5_3, shape=[-1, h * w * d], name='flatten')
fc1 = fully_connect(flatten, 4096, name='fc1')
fc1_dropout = tf.nn.dropout(fc1,
keep_prob=keep_prob,
name='fc1_dropout')
fc2 = fully_connect(fc1_dropout, 4096, name='fc2')
fc2_dropout = tf.nn.dropout(fc2,
keep_prob=keep_prob,
name='fc2_dropout')
logits = fully_connect(fc2_dropout, self.N_CLASS, name='fc3')
return logits
def discriminator_B(self, inputs, is_training, reuse=False):
with tf.variable_scope('discirminator_B', reuse=reuse):
layer1 = lrelu(
bn(
conv2d(inputs,
64, [4, 4],
strides=[1, 2, 2, 1],
initializer='random',
name='d_B_hiddne1'), is_training))
layer2 = lrelu(
bn(
conv2d(layer1,
128, [4, 4],
strides=[1, 2, 2, 1],
initializer='random',
name='d_B_hidden2'), is_training))
flatten = tf.reshape(layer2, [-1, 7 * 7 * 128])
layer3 = lrelu(
bn(fully_connect(flatten, 1024, name='d_B_hidden3'),
is_training))
layer4 = fully_connect(layer3, 1, name='d_B_hidden')
logits = sigmoid(layer4)
return logits, layer4