def Discriminator(input, reuse=False):
with tf.variable_scope("Discriminator") as scope:
if reuse:
scope.reuse_variables()
image = tf.reshape(input, [-1, 28, 28, 1])
conv1 = tlib.Con2D(image, FLAGS.DIM, 5, 2, scope="conv1")
relu1 = tlib.leaky_relu(conv1)
conv2 = tlib.Con2D(relu1, 2 * FLAGS.DIM, 5, 2, scope="conv2")
relu2 = tlib.leaky_relu(conv2)
conv3 = tlib.Con2D(relu2, 4 * FLAGS.DIM, 5, 2, scope="conv3")
relu3 = tlib.leaky_relu(conv3)
out_put = tf.reshape(relu3, [-1, 4 * 4 * 4 * FLAGS.DIM])
fc1_source = tlib.fc(out_put, 1, scope="fc1")
fc2_class = tlib.fc(out_put, FLAGS.n_class, scope="fc2")
fc3_con = tlib.fc(out_put, 2, scope="fc3")
n_class_ = tf.nn.softmax(fc2_class, name="class")
return fc1_source, fc2_class, fc3_con, n_class_
def Discriminator_k(input, reuse=False):
with tf.variable_scope("Discriminator") as scope:
if reuse:
scope.reuse_variables()
#image = tf.transpose(tf.reshape(input,[-1,3,32,32]),perm=[0,2,3,1])
conv1 = tlib.Con2D(input, FLAGS.DIM, 5, 2, scope="conv1")
relu1 = tlib.leaky_relu(conv1)
conv2 = tlib.Con2D(relu1, 2 * FLAGS.DIM, 5, 2, scope="conv2")
relu2 = tlib.leaky_relu(conv2)
conv3 = tlib.Con2D(relu2, 4 * FLAGS.DIM, 5, 2, scope="conv3")
relu3 = tlib.leaky_relu(conv3)
out_put = tf.reshape(relu3, [-1, 4 * 4 * 4 * FLAGS.DIM])
fc1 = tlib.fc(out_put, 1, scope="fc1")
return tf.reshape(fc1, [-1])
def Discriminator(input, reuse=False):
with tf.variable_scope("Discriminator") as scope:
if reuse:
scope.reuse_variables()
image = tf.reshape(input, [-1, 28, 28, 1])
conv1 = tlib.Con2D(image, FLAGS.DIM, 5, 2, scope="conv1")
relu1 = tlib.leaky_relu(conv1)
conv2 = tlib.Con2D(relu1, 2 * FLAGS.DIM, 5, 2, scope="conv2")
relu2 = tlib.leaky_relu(conv2)
conv3 = tlib.Con2D(relu2, 4 * FLAGS.DIM, 5, 2, scope="conv3")
relu3 = tlib.leaky_relu(conv3)
out_put = tf.reshape(relu3, [-1, 4 * 4 * 4 * FLAGS.DIM])
fc1 = tlib.fc(out_put, 1, scope="fc1")
return fc1