blocks.py 文件源码

def mlp_ptscorer(inputs, Ddim, N, l2reg, pfx='out', Dinit='glorot_uniform', sum_mode='sum', extra_inp=[]):
    """ Element-wise features from the pair fed to an MLP. """
    linear = Activation('linear')
    if sum_mode == 'absdiff':
        absdiff = Lambda(function=lambda x: K.abs(x[0] - x[1]),
                         output_shape=lambda shape: shape[0])
        # model.add_node(name=pfx+'sum', layer=absdiff_merge(model, inputs))
        mlp_inputs = absdiff(inputs)
    elif sum_mode == 'sum':
        outsum = linear(add(inputs))
        outmul = linear(multiply(inputs))
        mlp_inputs = [outsum, outmul] + extra_inp

    def mlp_args(mlp_inputs):
        """ return model.add_node() args that are good for mlp_inputs list
        of both length 1 and more than 1. """
        if isinstance(mlp_inputs, list):
            mlp_inputs = concatenate(mlp_inputs)
        return mlp_inputs

    # Ddim may be either 0 (no hidden layer), scalar (single hidden layer) or
    # list (multiple hidden layers)
    if Ddim == 0:
        mlp_inputs = mlp_args(mlp_inputs)
        Ddim = []
    elif not isinstance(Ddim, list):
        Ddim = [Ddim]
    if Ddim:
        for i, D in enumerate(Ddim):
            mlp_inputs = Dense(int(N*D), activation='tanh', kernel_initializer=Dinit, kernel_regularizer=l2(l2reg))(mlp_args(mlp_inputs))
            # model.add_node(name=pfx+'hdn[%d]'%(i,),
            #                layer=Dense(output_dim=int(N*D), W_regularizer=l2(l2reg), activation='tanh', init=Dinit),
            #                **mlp_args(mlp_inputs))
            # mlp_inputs = [pfx+'hdn[%d]'%(i,)]
    outmlp = Dense(1, kernel_regularizer=l2(l2reg))(mlp_inputs)
    return outmlp