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# CS188 Introduction to Artificial Intelligence - Project 5: Machine Learning

USUC BerkeleyCS188Introduction to Artificial IntelligencePythonMachine Learning

# For this question, you will train a network to classify handwritten digits from the MNIST dataset. Each digit is of size 28×28$28×28$ pixels, the values of which are stored in a 784$784$-dimensional vector of floating point numbers. Each output we provide is a 10-dimensional vector which has zeros in all positions, except for a one in the position corresponding to the correct class of the digit. Complete the implementation of the DigitClassificationModel class in models.py. The return value from DigitClassificationModel.run() should be a batch_size×10$\text{batch_size}×10$ node containing scores, where higher scores indicate a higher probability of a digit belonging to a particular class (0-9). You should use nn.SoftmaxLoss as your loss. Do not put a ReLU activation after the last layer of the network. For both this question and Q4, in addition to training data, there is also validation data and a test set. You can use dataset.get_validation_accuracy() to compute validation accuracy for your model, which can be useful when deciding whether to stop training. The test set will be used by the autograder. To receive points for this question, your model should achieve an accuracy of at least 97% on the test set. For reference, our staff implementation consistently achieves an accuracy of 98% on the validation data after training for around 5 epochs. Note that the test grades you on test accuracy, while you only have access to validation accuracy - so if your validation accuracy meets the 97% threshold, you may still fail the test if your test accuracy does not meet the threshold. Therefore, it may help to set a slightly higher stopping threshold on validation accuracy, such as 97.5% or 98%. To test your implementation, run the autograder:  python autograder.py -q q3

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