def discriminator(input_img):
# input_img = Input(batch_shape=(None, 3, 32, 32), dtype=im_dtype)
with tf.variable_scope('Net_Dis') as scope:
xx = layers.fully_connected(input_img, num_outputs=1000, activation_fn=None)
xx = layers.batch_norm(xx)
xx = tf.nn.relu(xx)
xx = tf.nn.dropout(xx, 0.5)
xx = layers.fully_connected(xx, num_outputs=500, activation_fn=None)
xx = layers.batch_norm(xx)
xx = tf.nn.relu(xx)
xx = tf.nn.dropout(xx, 0.5)
xx = layers.fully_connected(xx, num_outputs=250, activation_fn=None)
xx = layers.batch_norm(xx)
xx = tf.nn.relu(xx)
xx = tf.nn.dropout(xx, 0.5)
xx = layers.fully_connected(xx, num_outputs=250, activation_fn=None)
xx = layers.batch_norm(xx)
xx = tf.nn.relu(xx)
xx = tf.nn.dropout(xx, 0.5)
xx0 = layers.fully_connected(xx, num_outputs=250, activation_fn=None)
xx = layers.batch_norm(xx0)
xx = tf.nn.relu(xx)
logits = layers.fully_connected(xx, label_size, activation_fn=None)
return logits, xx0
# pdb.set_trace()
train_mnist_feature_matching_tf.py 文件源码
python
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