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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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