model.py 文件源码

def build(self):
        dim_data = self.size_of_input_data_dim
        nb_time_step = self.size_of_input_timesteps
        financial_time_series_input = Input(shape=(nb_time_step, dim_data))

        lstm_layer_1 = LSTM(output_dim=nb_hidden_units, dropout_U=dropout, dropout_W=dropout, inner_activation='sigmoid',
                            W_regularizer=l2(l2_norm_alpha), b_regularizer=l2(l2_norm_alpha), activation='tanh',
                            return_sequences=True)
        lstm_layer_2 = LSTM(output_dim=nb_hidden_units, dropout_U=dropout, dropout_W=dropout, inner_activation='sigmoid',
                            W_regularizer=l2(l2_norm_alpha), b_regularizer=l2(l2_norm_alpha), activation='tanh',
                            return_sequences=True)

        h1 = lstm_layer_1(financial_time_series_input)
        h2 = lstm_layer_2(h1)
        time_series_predictions = TimeDistributedDense(1)(h2)
        self.model = Model(financial_time_series_input, time_series_predictions,
                           name="deep rnn for financial time series forecasting")