INNER CODE UNIT · Python

_conv_layer

aiff22/DPED · models.py:105

def _conv_layer(net, num_filters, filter_size, strides, batch_nn=True):
    
    weights_init = _conv_init_vars(net, num_filters, filter_size)
    strides_shape = [1, strides, strides, 1]
    bias = tf.Variable(tf.constant(0.01, shape=[num_filters]))

    net = tf.nn.conv2d(net, weights_init, strides_shape, padding='SAME') + bias   
    net = leaky_relu(net)

    if batch_nn:
        net = _instance_norm(net)

    return net


def _instance_norm(net):

    batch, rows, cols, channels = [i.value for i in net.get_shape()]

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