【python实现卷积神经网络】激活层实现

activation_functions = {
    'relu': ReLU,
    'sigmoid': Sigmoid,
    'selu': SELU,
    'elu': ELU,
    'softmax': Softmax,
    'leaky_relu': LeakyReLU,
    'tanh': TanH,
    'softplus': SoftPlus
}

class Activation(Layer):
    """A layer that applies an activation operation to the input.
    Parameters:
    -----------
    name: string
        The name of the activation function that will be used.
    """

    def __init__(self, name):
        self.activation_name = name
        self.activation_func = activation_functions[name]()
        self.trainable = True

    def layer_name(self):
        return "Activation (%s)" % (self.activation_func.__class__.__name__)

    def forward_pass(self, X, training=True):
        self.layer_input = X
        return self.activation_func(X)

    def backward_pass(self, accum_grad):
        return accum_grad * self.activation_func.gradient(self.layer_input)

    def output_shape(self):
        return self.input_shape