chrom_hmm_cnn.py 文件源码

def train_chrom_labeller(model, train_tuple, valid_tuple, save_weight_hd5):
    checkpointer = ModelCheckpoint(filepath=save_weight_hd5, verbose=1, save_best_only=True)
    earlystopper = EarlyStopping(monitor='val_loss', patience=3, verbose=1) 

    history = model.fit(train_tuple[0], train_tuple[1], 
        batch_size=32, nb_epoch=150, shuffle=False, 
        validation_data=(valid_tuple[0], valid_tuple[1]), 
        callbacks=[checkpointer,earlystopper])

    plot_metric_history(history, weight_path_to_title(save_weight_hd5))

    return model


# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# ~~~~~~~ Training Data ~~~~~~~~~~~
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~