Function activation_module

tests/utils/custom_layers/basic_layers.py:15–61

(layer, activation_fn, leaky_relu_alpha=0.2, name=None)

def activation_module(layer, activation_fn, leaky_relu_alpha=0.2, name=None):

    act_name = name + "/activation" if name is not None else "activation"

    if activation_fn not in ["ReLU", "ReLU6", "Leaky_ReLU", "PReLU", "PReLU6", "PTReLU6", "CReLU", "ELU", "SELU",
                             "tanh", "sigmoid", "softmax", None]:
        raise Exception("Unknown 'activation_fn': %s" % activation_fn)

    elif activation_fn == "ReLU":
        layer = tl.layers.LambdaLayer(prev_layer=layer, fn=tf.nn.relu, name=act_name)

    elif activation_fn == "ReLU6":
        layer = tl.layers.LambdaLayer(prev_layer=layer, fn=tf.nn.relu6, name=act_name)

    elif activation_fn == "Leaky_ReLU":
        layer = tl.layers.LambdaLayer(
            prev_layer=layer, fn=tf.nn.leaky_relu, fn_args={'alpha': leaky_relu_alpha}, name=act_name
        )

    elif activation_fn == "PReLU":
        layer = tl.layers.PReluLayer(prev_layer=layer, channel_shared=False, name=act_name)

    elif activation_fn == "PReLU6":
        layer = tl.layers.PRelu6Layer(prev_layer=layer, channel_shared=False, name=act_name)

    elif activation_fn == "PTReLU6":
        layer = tl.layers.PTRelu6Layer(prev_layer=layer, channel_shared=False, name=act_name)

    elif activation_fn == "CReLU":
        layer = tl.layers.LambdaLayer(prev_layer=layer, fn=tf.nn.crelu, name=act_name)

    elif activation_fn == "ELU":
        layer = tl.layers.LambdaLayer(prev_layer=layer, fn=tf.nn.elu, name=act_name)

    elif activation_fn == "SELU":
        layer = tl.layers.LambdaLayer(prev_layer=layer, fn=tf.nn.selu, name=act_name)

    elif activation_fn == "tanh":
        layer = tl.layers.LambdaLayer(prev_layer=layer, fn=tf.nn.tanh, name=act_name)

    elif activation_fn == "sigmoid":
        layer = tl.layers.LambdaLayer(prev_layer=layer, fn=tf.nn.sigmoid, name=act_name)

    elif activation_fn == "softmax":
        layer = tl.layers.LambdaLayer(prev_layer=layer, fn=tf.nn.softmax, name=act_name)

    return layer


def conv_module(