architectures.py 文件源码

def feed_forward_net(input, output, hidden_layers=[64, 64], activations='relu',
                     dropout_rate=0., l2=0., constrain_norm=False):
    '''
    Helper function for building a Keras feed forward network.

    input:  Keras Input object appropriate for the data. e.g. input=Input(shape=(20,))
    output: Function representing final layer for the network that maps from the last
            hidden layer to output.
            e.g. if output = Dense(10, activation='softmax') if we're doing 10 class
            classification or output = Dense(1, activation='linear') if we're doing
            regression.
    '''
    state = input
    if isinstance(activations, str):
        activations = [activations] * len(hidden_layers)

    for h, a in zip(hidden_layers, activations):
        if l2 > 0.:
            w_reg = keras.regularizers.l2(l2)
        else:
            w_reg = None
        const = maxnorm(2) if constrain_norm else  None
        state = Dense(h, activation=a, kernel_regularizer=w_reg, kernel_constraint=const)(state)
        if dropout_rate > 0.:
            state = Dropout(dropout_rate)(state)
    return output(state)