basic_model.py 文件源码

def train(model, X_train, y_train, X_test, y_test):
    sys.stdout.write('Training model\n\n')
    sys.stdout.flush()

    # train each iteration individually to back up current state
    # safety measure against potential crashes
    epoch_count = 0
    while epoch_count < epoch:
        epoch_count += 1
        sys.stdout.write('Epoch count: ' + str(epoch_count) + '\n')
        sys.stdout.flush()
        model.fit(X_train, y_train, batch_size=batch_size,
                  nb_epoch=1, validation_data=(X_test, y_test))
        sys.stdout.write('Epoch {} done, saving model to file\n\n'.format(epoch_count))
        sys.stdout.flush()
        model.save_weights('./models/convnet_weights.h5')

    return model