INNER CODE UNIT · Python
leaky_relu
aiff22/DPED · models.py:101
def leaky_relu(x, alpha = 0.2):
return tf.maximum(alpha * x, x)
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