ImageDataGenerator doesn't like my fashionMNIST dataset. Which input does it need?

Question

I have a set called train_images and train_labels from the tensorflow basic image classification guide:

https://www.tensorflow.org/tutorials/keras/classification

I load the dataset with:

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

the shape of these two list are respectively: (60000, 28, 28) (60000,)

After that I want to use ImageDataGenerator to flip horizontally some images but when I fit the model with my train lists, it returns my an error saying the x shoud be an array of rank 4

I already tried to do

train_images = (np.expand_dims(train_images,0))

so the shape become (1,60000,28,28) (I have to do this to let the model examine a single image) but it doesn't work to fit the model

this is the rest of the code:

aug = ImageDataGenerator(rotation_range=20, horizontal_flip=True)

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28,28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
    ])

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
    )

BS=32
EPOCHS=10
H = model.fit_generator(
    aug.flow(train_images, train_labels, batch_size=BS),
    validation_data=(test_images, test_labels),
    steps_per_epoch=len(train_images) // BS,
    epochs=EPOCHS)

And this is the error generated:

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-65-e49da92bcb89> in <module>()
      5 #train_images.shape
      6 H = model.fit_generator(
----> 7         aug.flow(train_images, train_labels, batch_size=BS),
      8         validation_data=(test_images, test_labels),
      9         steps_per_epoch=len(train_images) // BS,

1 frames
/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/numpy_array_iterator.py in __init__(self, x, y, image_data_generator, batch_size, shuffle, sample_weight, seed, data_format, save_to_dir, save_prefix, save_format, subset, dtype)

    115             raise ValueError('Input data in `NumpyArrayIterator` '
    116                              'should have rank 4. You passed an array '
--> 117                              'with shape', self.x.shape)
    118         channels_axis = 3 if data_format == 'channels_last' else 1
    119         if self.x.shape[channels_axis] not in {1, 3, 4}:

ValueError: ('Input data in `NumpyArrayIterator` should have rank 4. You passed an array with shape', (60000, 28, 28))

actually train_images is (N° of images, width, height) what is the 4th axis it is waiting for? how to perform this?

Answer 1

You should convert your images to 4D tensor. Now you have NHW format (batch dimension, height, width). The error says that you should have NHWC format - batch, height, width, channel. So you need to do

train_images = (np.expand_dims(train_images, axis=3))

This will add a channel dimension (of size 1), the resulting shape will be (60000,28,28,1) and it should fix your problem.