deep-learning

Moving averaging of Loss during Training in Keras

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I am using Keras with TensorFlow to implement a deep neural network. When I plot the loss and number of iterations, there is a significant jump in loss after each epoch. In reality, the loss of each mini-batch should vary from each other, but Keras calculates the moving average of the loss over the mini-batches, that’s why we obtain a smooth curve instead of an arbitrary one. The array of the moving average is reset after each epoch because of which we can observe a jump in the loss.

Best way to save a trained model in PyTorch?

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I was looking for alternative ways to save a trained model in PyTorch. So far, I have found two alternatives. torch.save() to save a model and torch.load() to load a model. model.state_dict() to save a trained model and model.load_state_dict() to load the saved model. I have come across to this discussion where approach 2 is … Read more