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def discriminator(x, is_reuse=False, alpha = 0.2):
''' Build the discriminator network.
Arguments
---------
x : Input tensor for the discriminator
n_units: Number of units in hidden layer
reuse : Reuse the variables with tf.variable_scope
alpha : leak parameter for leaky ReLU
Returns
-------
out, logits:
'''
with tf.variable_scope("discriminator", reuse = is_reuse):
# Input layer 128*128*3 --> 64x64x64
# Conv --> BatchNorm --> LeakyReLU
conv1 = tf.layers.conv2d(inputs = x,
filters = 64,
kernel_size = [5,5],
strides = [2,2],
padding = "SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
name='conv1')
batch_norm1 = tf.layers.batch_normalization(conv1,
training = True,
epsilon = 1e-5,
name = 'batch_norm1')
conv1_out = tf.nn.leaky_relu(batch_norm1, alpha=alpha, name="conv1_out")
# 64x64x64--> 32x32x128
# Conv --> BatchNorm --> LeakyReLU
conv2 = tf.layers.conv2d(inputs = conv1_out,
filters = 128,
kernel_size = [5, 5],
strides = [2, 2],
padding = "SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
name='conv2')
batch_norm2 = tf.layers.batch_normalization(conv2,
training = True,
epsilon = 1e-5,
name = 'batch_norm2')
conv2_out = tf.nn.leaky_relu(batch_norm2, alpha=alpha, name="conv2_out")
# 32x32x128 --> 16x16x256
# Conv --> BatchNorm --> LeakyReLU
conv3 = tf.layers.conv2d(inputs = conv2_out,
filters = 256,
kernel_size = [5, 5],
strides = [2, 2],
padding = "SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
name='conv3')
batch_norm3 = tf.layers.batch_normalization(conv3,
training = True,
epsilon = 1e-5,
name = 'batch_norm3')
conv3_out = tf.nn.leaky_relu(batch_norm3, alpha=alpha, name="conv3_out")
# 16x16x256 --> 16x16x512
# Conv --> BatchNorm --> LeakyReLU
conv4 = tf.layers.conv2d(inputs = conv3_out,
filters = 512,
kernel_size = [5, 5],
strides = [1, 1],
padding = "SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
name='conv4')
batch_norm4 = tf.layers.batch_normalization(conv4,
training = True,
epsilon = 1e-5,
name = 'batch_norm4')
conv4_out = tf.nn.leaky_relu(batch_norm4, alpha=alpha, name="conv4_out")
# 16x16x512 --> 8x8x1024
# Conv --> BatchNorm --> LeakyReLU
conv5 = tf.layers.conv2d(inputs = conv4_out,
filters = 1024,
kernel_size = [5, 5],
strides = [2, 2],
padding = "SAME",
kernel_initializer=tf.truncated_normal_initializer(stddev=0.02),
name='conv5')
batch_norm5 = tf.layers.batch_normalization(conv5,
training = True,
epsilon = 1e-5,
name = 'batch_norm5')
conv5_out = tf.nn.leaky_relu(batch_norm5, alpha=alpha, name="conv5_out")
# Flatten it
flatten = tf.reshape(conv5_out, (-1, 8*8*1024))
# Logits
logits = tf.layers.dense(inputs = flatten,
units = 1,
activation = None)
out = tf.sigmoid(logits)
return out, logits
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