In your lab 2 code, we used the following transform: The PyTorch transform transforms.ToTensor() automatically scalesĮach pixel intensity to the range. Is to scale each feature so that they are in the range. Of mean 0 and standard deviation 1 is one approach. We would want each of the features to be scaled similarly. Housing prices based on a house's number of bedroom, square footage, etc, However, if we were performing prediction of, say, Where all input features have similar interpretations.Īll features of an image are pixel intensities, all of which are scaled Prevent overfitting, normalizing your data makes the trainingĭata normalization is less of an issues for input data - like images. Although data normalization does not directly Network, so that all features are scaled similarly (similar meansĪnd standard deviations). ``Data normalization'' means to scale the input features of a neural max ( 1, keepdim = True ) # get the index of the max logit correct += pred. DataLoader ( data, batch_size = 64 ): output = model ( imgs ) pred = output. DataLoader ( mnist_val, batch_size = 1000 ) def get_accuracy ( model, data ): correct = 0 total = 0 model. DataLoader ( mnist_train, batch_size = 100 ) val_acc_loader = torch. format ( val_acc )) train_acc_loader = torch. show () print ( "Final Training Accuracy: ". plot ( iters, val_acc, label = "Validation" ) plt. plot ( iters, train_acc, label = "Train" ) plt. plot ( iters, losses, label = "Train" ) plt. append ( get_accuracy ( model, valid )) # compute validation accuracy n += 1 # plotting plt. append ( get_accuracy ( model, train )) # compute training accuracy val_acc. append ( float ( loss ) / batch_size ) # compute *average* loss train_acc. zero_grad () # a clean up step for PyTorch # save the current training information if n % 10 = 9 : iters. step () # make the updates for each parameter optimizer. backward () # backward pass (compute parameter updates) optimizer. train () #*****************************# out = model ( imgs ) # forward pass loss = criterion ( out, labels ) # compute the total loss loss. parameters (), lr = learn_rate, momentum = 0.9, weight_decay = weight_decay ) iters, losses, train_acc, val_acc =, ,, # training n = 0 # the number of iterations while True : if n >= num_iters : break for imgs, labels in iter ( train_loader ): model. DataLoader ( train, batch_size = batch_size, shuffle = True ) # shuffle after every epoch criterion = nn. I will artificially reduce the number of training examples to 200.ĭef train ( model, train, valid, batch_size = 20, num_iters = 1, learn_rate = 0.01, weight_decay = 0 ): train_loader = torch. We will use the MNIST digit recognition problem as a running example. These are only some of the techniques for preventing overfitting. Nevertheless, transfer learning allows us to leverage informationįrom larger data sets with low computational cost. The architectures and weights of AlexNet was trained using a largerĭataset, and was trained to solve a different image classification problem. Pre-trained weights of a different model (e.g. Transfer learning was introduced in lab 3, where we used We chose the iteration/epoch to use based on Instead, we used a model (a set of weights) from a previous Where we did not use the trained weights from the last training iteration To restart training, rather than use what we already know about hyperparameters and appropriate Using a smaller network means that we need Set may be impractical or expensive in practice. For example, collecting a larger training Some of these are more practical than others. Weight-sharing (as in convolutional neural networks).We've actually already discussed several strategies for detecting overfitting: Then use some of the many strategies to prevent overfitting. So, always start with slightly more capacity than you need, To build a high-capacity model and use known techniques to prevent Since computation is (relatively) cheap,Īnd overfitting is much easier to detect, it is more straightforward Practitioners tend to avoid underfitting altogether by opting for more The reason we did not discuss underfitting much is because nowadays, We also briefly discussed idea of underfitting, but not in as much depth. Rather than information that is generalizable to the taskĪt hand. Where a neural network model learns about the quirks of the training data, In the last few weeks we discussed the idea of overfitting,
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