Why CNN in Python is performing much worse than in Matlab?

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I have trained a CNN in Matlab 2019b that does a binary classification. When this CNN was tested in a test dataset it was getting about 95% accuracy. I used the exportONNXNetwork function so that I can implement my CNN in Tensorflow, Keras. This is the code I am using to use the ONNX file in keras: 

import onnx
from onnx_tf.backend import prepare
import numpy as np
from numpy import array
from IPython.display import display
from PIL import Image

onnx_model = onnx.load("model.onnx")
tf_rep = prepare(onnx_model)
img = Image.open("image.jpg").resize((224,224))
img = array(img).reshape(1,3,224,224)
img = img.astype(np.uint8)

classification = tf_rep.run(img)
print(classification)

When this python code was tested on the same test dataset it was classifying almost everything as class 0 with a few cases of class 1. I am not sure why this is happening.

conv-neural-network keras tensorflow matlab python
1 Answers

At a glance, I think you need to permute the image axes rather than reshape:

img = Image.open("image.jpg").resize((224,224))
img = array(img).transpose(2, 0, 1)
img = np.expand_dims(img, 0)

The image you get from PIL is in the channels last format, i.e. a tensor of shape (height, width, channels), in this case (224, 224, 3). Your model expects the input in the channels first format, i.e. a tensor of shape (channels, height, width), in this case (3, 224, 224).

You need to move the last axis to the front. If you use reshape, NumPy will traverse the array in C order (last axis index changing the fastest), meaning your image will end up scrambled. This is easier to understand on an example:

>>> img = np.arange(48).reshape(4, 4, 3)
>>> img[0, 0, :]
array([0, 1, 2])

The RGB values of the (0, 0) pixel are (0, 1, 2). If you use np.transpose(), this is preserved:

>>> img.transpose(2, 0, 1)[:, 0, 0]
array([0, 1, 2])

If you use reshape, your image will get scrambled:

>>> img.reshape(3, 224, 224)[:, 0, 0]
array([0, 16, 32])
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