bigrumodel.py 文件源码

def BiGRU(X_train, y_train, X_test, y_test, gru_units, dense_units, input_shape, \
           batch_size, epochs, drop_out, patience):

    model = Sequential()

    reg = L1L2(l1=0.2, l2=0.2)

    model.add(Bidirectional(GRU(units = gru_units, dropout= drop_out, activation='relu', recurrent_regularizer = reg,
                                return_sequences = True),
                                input_shape = input_shape,
                                merge_mode="concat"))

    model.add(BatchNormalization())

    model.add(TimeDistributed(Dense(dense_units, activation='relu')))
    model.add(BatchNormalization())

    model.add(Bidirectional(GRU(units = gru_units, dropout= drop_out, activation='relu', recurrent_regularizer=reg,
                                    return_sequences = True),
                             merge_mode="concat"))

    model.add(BatchNormalization())

    model.add(Dense(units=1))

    model.add(GlobalAveragePooling1D())

    print(model.summary())

    early_stopping = EarlyStopping(monitor="val_loss", patience = patience)

    model.compile(loss='mse', optimizer= 'adam')

    history_callback = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs,\
              verbose=2, callbacks=[early_stopping], validation_data=[X_test, y_test], shuffle = True)

    return model, history_callback