EES.py 文件源码

def EES_train():
    EES = model_EES16()
    EES.compile(optimizer=adam(lr=0.0003), loss='mse')
    print EES.summary()

    data, label = pd.read_training_data("./train.h5")
    val_data, val_label = pd.read_training_data("./val.h5")

    checkpoint = ModelCheckpoint("EES_check.h5", monitor='val_loss', verbose=1, save_best_only=True,
                                 save_weights_only=False, mode='min')
    callbacks_list = [checkpoint]

    history_callback = EES.fit(data, label, batch_size=64, validation_data=(val_data, val_label),
                               callbacks=callbacks_list, shuffle=True, nb_epoch=200, verbose=1)
    pandas.DataFrame(history_callback.history).to_csv("history.csv")
    EES.save_weights("EES_final.h5")