Requirements:
1. Contain a training function that will be called to train a model with the command
“python classify.py train”.
2. Save the model in a folder named “model” after finishing the training process.
3. Show the testing accuracy in each iteration of the training function. The test
accuracy should be greater than or equal to 40% in the end using the CIFAR-10
dataset.
4. Implement a testing function that accepts the command “python classify.py test
xxx.png” to test your model by loading it from the folder “model” created in the
training step. The function should read “xxx.png” and predict the output. The
output might not match the true image type because this type of classifiers cannot
achieve high accuracy
1. Contain a training function that will be called to train a model with the command
“python classify.py train”.
2. Save the model in a folder named “model” after finishing the training process.
3. Show the testing accuracy in each iteration of the training function. The test
accuracy should be greater than or equal to 40% in the end using the CIFAR-10
dataset.
4. Implement a testing function that accepts the command “python classify.py test
xxx.png” to test your model by loading it from the folder “model” created in the
training step. The function should read “xxx.png” and predict the output. The
output might not match the true image type because this type of classifiers cannot
achieve high accuracy
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