nse.py 文件源码

def __init__(self, output_dim, input_length=None, composer_activation='linear',
                 return_mode='last_output', weights=None, **kwargs):
        '''
        Arguments:
        output_dim (int)
        input_length (int)
        composer_activation (str): activation used in the MLP
        return_mode (str): One of last_output, all_outputs, output_and_memory
            This is analogous to the return_sequences flag in Keras' Recurrent.
            last_output returns only the last h_t
            all_outputs returns the whole sequence of h_ts
            output_and_memory returns the last output and the last memory concatenated
                (needed if this layer is followed by a MMA-NSE)
        weights (list): Initial weights
        '''
        self.output_dim = output_dim
        self.input_dim = output_dim  # Equation 2 in the paper makes this assumption.
        self.initial_weights = weights
        self.input_spec = [InputSpec(ndim=3)]
        self.input_length = input_length
        self.composer_activation = composer_activation
        super(NSE, self).__init__(**kwargs)
        self.reader = LSTM(self.output_dim, dropout_W=0.0, dropout_U=0.0, consume_less="gpu",
                           name="{}_reader".format(self.name))
        # TODO: Let the writer use parameter dropout and any consume_less mode.
        # Setting dropout to 0 here to eliminate the need for constants.
        # Setting consume_less to gpu to eliminate need for preprocessing
        self.writer = LSTM(self.output_dim, dropout_W=0.0, dropout_U=0.0, consume_less="gpu",
                           name="{}_writer".format(self.name))
        self.composer = Dense(self.output_dim * 2, activation=self.composer_activation,
                              name="{}_composer".format(self.name))
        if return_mode not in ["last_output", "all_outputs", "output_and_memory"]:
            raise Exception("Unrecognized return mode: %s" % (return_mode))
        self.return_mode = return_mode