在依次读取的多个功能文件上培训Keras模型以节省内存

问题描述：

我在尝试读取大量功能文件时遇到了内存问题（请参见下文）。我想我会拆分培训文件并按顺序读取它们。 这样做的最佳方法是什么？在依次读取的多个功能文件上培训Keras模型以节省内存

x_train = np.load(path_features + 'x_train.npy) 
y_train = np.load(path_features + 'y_train.npy) 
x_test = np.load(path_features + 'x_test.npy) 
y_test = np.load(path_features + 'y_test.npy) 

path_models = '../pipelines/' + pipeline + '/models/' 

# global params 
verbose_level = 1 
inp_shape = x_train.shape[1:] 

# models 
if model_type == 'standard_4': 
    print('Starting to train ' + feature_type + '_' + model_type + '.') 
    num_classes = 1 
    dropout_prob = 0.5 
    activation_function = 'relu' 
    loss_function = 'binary_crossentropy' 
    batch_size = 32 
    epoch_count = 100 
    opt = SGD(lr=0.001) 

    model = Sequential() 
    model.add(Conv2D(filters=16, kernel_size=(3, 3), input_shape=inp_shape)) 
    model.add(MaxPooling2D(pool_size=(2, 2))) 
    model.add(Conv2D(filters=32, kernel_size=(3, 3))) 
    model.add(MaxPooling2D(pool_size=(2, 2))) 
    model.add(Flatten()) 
    model.add(Dense(64, activation=activation_function)) 
    model.add(Dropout(rate=dropout_prob)) 
    model.add(Dense(32, activation=activation_function)) 
    model.add(Dense(num_classes, activation='sigmoid')) 
    model.summary() 
    model.compile(loss=loss_function, optimizer=opt, metrics=['accuracy']) 
    hist = model.fit(x_train, y_train, batch_size=batch_size, epochs=epoch_count, 
        verbose=verbose_level, 
        validation_data=(x_test, y_test)) 

    model.save(path_models + category + '_' + feature_type + '_' + model_type + '.h5') 
    print('Finished training ' + model_type + '.') 

    plot_model(hist, path_models, category, feature_type, model_type) 
    print('Saved model charts.')

答

您可以使用一个蟒蛇generator或keras sequence。

发电机应无限期产生的批次：

def myReader(trainOrTest): 
    while True: 
     do something to define path_features 

     x = np.load(path_features + 'x_' + trainOrTest + '.npy') 
     y = np.load(path_features + 'y_' + trainOrTest + '.npy') 

     #if you're loading them already in a shape accepted by your model: 
     yield (x,y)

然后可以使用fit_generator培养和predict_generator预测值：

model.fit_generator(myReader(trainOrTest),steps_per_epoch=howManyFiles,epochs=.......)

在依次读取的多个功能文件上培训Keras模型以节省内存

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