python - Keras 中的 ImageDataGenerator 形状问题

Question

datagen = ImageDataGenerator(
            featurewise_center=False,  # set input mean to 0 over the dataset
            samplewise_center=False,  # set each sample mean to 0
            featurewise_std_normalization=False,  # divide inputs by std of the dataset
            samplewise_std_normalization=False,  # divide each input by its std
            zca_whitening=False,  # apply ZCA whitening
            rotation_range=15,  # randomly rotate images in the range (degrees, 0 to 180)
            width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
            height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
            horizontal_flip=True,  # randomly flip images
            vertical_flip=False)  # randomly flip images
        # (std, mean, and principal components if ZCA whitening is applied).
        # datagen.fit(x_train)


        print(x_train.shape)

        def data_generator(generator, x, y1, y2, batch_size):
            genX = generator.flow(x, seed=7, batch_size=batch_size)
            genY1 = generator.flow(y1, seed=7, batch_size=batch_size)
            genY2 = generator.flow(y2, seed=7, batch_size=batch_size)
            while(True):
                Xi = genX.next()
                Yi1 = genY1.next()
                Yi2 = genY2.next()
                yield Xi, [Yi1, Yi2]

这就是我调用 model.fit_generator 的方式

model.fit_generator(data_generator(datagen, x_train, y_train, y_aux_train, params['batch_size']),
                            epochs=params['epochs'], steps_per_epoch=150,
                            validation_data=data_generator(datagen, x_test, y_test, y_aux_test, params['batch_size']), 
                            validation_steps=100, callbacks=[reduce_lr, tensorboard],verbose=2)

这是我得到的错误 -

ValueError: ('Input data in NumpyArrayIterator should have rank 4. You passed an array with shape', (5630, 4))

Answer 1

您的x、y1 和y2 是多少？ ImageDataGenerator 的输入数据必须有 4 个维度(批处理、 channel 、高度、宽度)。您的数据完全不同，这就是您收到错误的原因。

更新:

根据docs :

flow(x, y=None, batch_size=32, shuffle=True, sample_weight=None, seed=None, save_to_dir=None, save_prefix='', save_format='png', subset=None)

x: Input data. Numpy array of rank 4 or a tuple. If tuple, the first element should contain the images and the second element another numpy array or a list of numpy arrays that gets passed to the output without any modifications. Can be used to feed the model miscellaneous data along with the images.

在genY1=generator.flow(y1,seed=7,batch_size=batch_size)中，您将标签(如我所见，形状为(4，))作为特征传递，并且ImageDataGenerator期望它们有 4 个维度。

你不应该这样传递你的标签。尝试这样的事情:

datagen.flow((x_train, [y_train, y_aux_train]), batch_size=params['batch_size'])

或者:

datagen.flow(x=x_train, y=[y_train, y_aux_train], batch_size=params['batch_size'])