INNER CODE UNIT · Python

c1

aiff22/DPED · models.py:9

        c1 = tf.nn.relu(conv2d(input_image, W1) + b1)

        # residual 1

        W2 = weight_variable([3, 3, 64, 64], name="W2"); b2 = bias_variable([64], name="b2");
        c2 = tf.nn.relu(_instance_norm(conv2d(c1, W2) + b2))

        W3 = weight_variable([3, 3, 64, 64], name="W3"); b3 = bias_variable([64], name="b3");
        c3 = tf.nn.relu(_instance_norm(conv2d(c2, W3) + b3)) + c1

        # residual 2

        W4 = weight_variable([3, 3, 64, 64], name="W4"); b4 = bias_variable([64], name="b4");
        c4 = tf.nn.relu(_instance_norm(conv2d(c3, W4) + b4))

        W5 = weight_variable([3, 3, 64, 64], name="W5"); b5 = bias_variable([64], name="b5");
        c5 = tf.nn.relu(_instance_norm(conv2d(c4, W5) + b5)) + c3

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