INNER CODE UNIT · Python
c9
aiff22/DPED · models.py:41
c9 = tf.nn.relu(_instance_norm(conv2d(c8, W9) + b9)) + c7
# Convolutional
W10 = weight_variable([3, 3, 64, 64], name="W10"); b10 = bias_variable([64], name="b10");
c10 = tf.nn.relu(conv2d(c9, W10) + b10)
W11 = weight_variable([3, 3, 64, 64], name="W11"); b11 = bias_variable([64], name="b11");
c11 = tf.nn.relu(conv2d(c10, W11) + b11)
# Final
W12 = weight_variable([9, 9, 64, 3], name="W12"); b12 = bias_variable([3], name="b12");
enhanced = tf.nn.tanh(conv2d(c11, W12) + b12) * 0.58 + 0.5
return enhanced