INNER CODE UNIT · Python

c10

aiff22/DPED · models.py:46

        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


def adversarial(image_):

    with tf.compat.v1.variable_scope("discriminator"):

        conv1 = _conv_layer(image_, 48, 11, 4, batch_nn = False)

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