Keras input shape ordering is (width, height, ch)?

Question

I use Keras2 with TensorFlow as back-end and tried feed horizontal rectangle image (width:150 x height:100 x ch:3) into network.

I use cv2 for pre-processing images and cv2 & TensorFlow treats the shape of images as [height, width, ch] ordering (in my case, it's [100, 150, 3] This format is opposite of (width:150 x height:100 x ch:3), but it's not mistake.)

So I defined Keras model API input as follow code, but it occurred an error.

img = cv2.imread('input/train/{}.jpg'.format(id))
img = cv2.resize(img, (100, 150))

inputs = Input(shape=(100, 150, 3))
x = Conv2D(8, (3, 3), padding='same', kernel_initializer='he_normal')(inputs)
~~~

error message is below

ValueError: Error when checking input: expected input_4 to have shape
(None, 100, 150, 3) but got array with shape (4, 150, 100, 3)

By the way input = Input((150, 100, 3)) can be run.

I feel weird with discrepancy between Keras & TensorFlow, so I'm suspicious that it just don't occurred error, it does not worked properly.

Anybody can explain that? I couldn't locate the input shape ordering in Keras Document.

Answer 1

You can change the dimension ordering as you prefer. You can print and change the dimension ordering like this:

from keras import backend as K
print(K.image_data_format()) # print current format
K.set_image_data_format('channels_last') # set format

If you want to permanently change the dimension ordering, you should edit it in the keras.json file, usually located at ~/.keras/keras.json:

"image_data_format": "channels_last"

Answer 2

My problem occurred from order of width&height at argument of cv2.resize(). cv2.resize() takes the argument like cv2.resize(img, (width, height)), whereas numpy treats image array order of (height, width).