INNER CODE UNIT · Python

normalized

aiff22/DPED · models.py:130

    normalized = (net-mu)/(sigma_sq + epsilon)**(.5)

    return scale * normalized + shift


def _conv_init_vars(net, out_channels, filter_size, transpose=False):

    _, rows, cols, in_channels = [i.value for i in net.get_shape()]

    if not transpose:
        weights_shape = [filter_size, filter_size, in_channels, out_channels]
    else:
        weights_shape = [filter_size, filter_size, out_channels, in_channels]

    weights_init = tf.Variable(tf.compat.v1.truncated_normal(weights_shape, stddev=0.01, seed=1), dtype=tf.float32)
    return weights_init

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