recurrent.py 文件源码

def __init__(self, num_units, input_size=None, activation=tf.tanh,
                 inner_activation=tf.sigmoid, bias=True, weights_init=None,
                 trainable=True, restore=True, reuse=False):
        if input_size is not None:
            logging.warn("%s: The input_size parameter is deprecated." % self)
        self._num_units = num_units
        if isinstance(activation, str):
            self._activation = activations.get(activation)
        elif hasattr(activation, '__call__'):
            self._activation = activation
        else:
            raise ValueError("Invalid Activation.")
        if isinstance(inner_activation, str):
            self._inner_activation = activations.get(inner_activation)
        elif hasattr(inner_activation, '__call__'):
            self._inner_activation = inner_activation
        else:
            raise ValueError("Invalid Activation.")
        self.bias = bias
        self.weights_init = weights_init
        if isinstance(weights_init, str):
            self.weights_init = initializations.get(weights_init)()
        self.trainable = trainable
        self.restore = restore
        self.reuse = reuse