Oddly getting a dramatic accuracy difference for the same model when it is deployed on MATLAB compared to TensorFlow & Keras?

As the title is self-descriptive, I’m getting a dramatic accuracy difference for the same model when I deploy it on MATLAB compared to TensorFlow & Keras. My model is actually a basic one, which uses transfer technique to fine-tune a pre-trained model, namely, ResNet50, by excluding the top and adding a Dense layer to utilize it for another classification task. I’ve added the sample code that shows the architecture of the model below. The model was trained under the same hyper-parameters on both platforms. The accuracy values I get on TensorFlow and MATLAB are 0.7455, and 0.424, respectively, on the CIFAR-10. What could be the reason behind this great accuracy difference? Could you please help me?

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Model Architecture:

base_model = ResNet50(include_top=False, weights=’imagenet’, input_shape=(75, 75, 3),pooling=’max’, classes=10)

base_model.trainable = False

model = Sequential()

model.add(base_model)

model.add(Dense(10, activation=’softmax’))