model.py 文件源码

def define_sequence_model(self):
        seed = 12345
        np.random.seed(seed)

        # add hidden layers
        for i in range(self.n_layers):
            if i == 0:
                input_size = self.n_in
            else:
                input_size = self.hidden_layer_size[i - 1]

            if self.hidden_layer_type[i]=='rnn':
                self.model.add(SimpleRNN(
                        units=self.hidden_layer_size[i],
                        input_shape=(None, input_size),
                        return_sequences=True))
            elif self.hidden_layer_type[i]=='gru':
                self.model.add(GRU(
                        units=self.hidden_layer_size[i],
                        input_shape=(None, input_size),
                        return_sequences=True))
            elif self.hidden_layer_type[i]=='lstm':
                self.model.add(LSTM(
                        units=self.hidden_layer_size[i],
                        input_shape=(None, input_size),
                        return_sequences=True))
            elif self.hidden_layer_type[i]=='blstm':
                self.model.add(LSTM(
                        units=self.hidden_layer_size[i],
                        input_shape=(None, input_size),
                        return_sequences=True,
                        go_backwards=True))
            else:
                self.model.add(Dense(
                        units=self.hidden_layer_size[i],
                        activation=self.hidden_layer_type[i],
                        kernel_initializer="normal",
                        input_shape=(None, input_size)))

        # add output layer
        self.final_layer = self.model.add(Dense(
            units=self.n_out,
            input_dim=self.hidden_layer_size[-1],
            kernel_initializer='normal',
            activation=self.output_type.lower()))

        # Compile the model
        self.compile_model()