Pytorch cross validation


  • PyTorch K-Fold Cross-Validation using Dataloader and Sklearn
  • How to use K-fold Cross Validation with PyTorch?
  • Reproducibility in PyTorch with K-Fold Cross Validation
  • Training Neural Networks with Validation using PyTorch
  • Tutorial: Fine tuning BERT for Sentiment Analysis
  • PyTorch K-Fold Cross-Validation using Dataloader and Sklearn

    Difficulty Level : Medium Last Updated : 19 Aug, Neural Networks are a biologically-inspired programming paradigm that deep learning is built around. Python provides various libraries using which you can create and train neural networks over given data. PyTorch is one such library that provides us with various utilities to build and train neural networks easily.

    When it comes to Neural Networks it becomes essential to set optimal architecture and hyper parameters. While training a neural network the training loss always keeps reducing provided the learning rate is optimal. One way to measure this is by introducing a validation set to keep track of the testing accuracy of the neural network.

    We can use pip or conda to install PyTorch:- Attention reader! Get hold of all the important Machine Learning Concepts with the Machine Learning Foundation Course at a student-friendly price and become industry ready.

    In Deep Learning we often train our neural networks in batches of a certain size, DataLoader is a data loading utility in PyTorch that creates an iterable over these batches of the dataset.

    Compose [ transforms. ToTensor ] In the above code, we declared a variable called transform which essentially helps us transform the raw data in the defined format. Here our transform is simply taking the raw data and converting it to a Tensor. A Tensor is a fancy way of saying a n-dimensional matrix. In the end, we did a split the train tensor into 2 tensors of and data points which become our train and valid tensors. Building our Model There are 2 ways we can create neural networks in PyTorch i.

    The format to create a neural network using the class method is as follows:- from torch import nn class model nn. Linear , self. Linear or Linear Layer is used to apply a linear transformation to the incoming data.

    In the forward method we start off by flattening the image and passing it through each layer and applying the activation function for the same. Optimizers take model parameters and learning rate as the input arguments.

    There are various optimizers you can try like Adam, Adagrad, etc. SGD model. So layers like dropout etc. Evaluation Mode: Set by model. To tackle this we can set a max valid loss which can be np.

    How to use K-fold Cross Validation with PyTorch?

    K-fold Cross Validation is a more robust evaluation technique. Using the training batches, you can then train your model, and subsequently evaluate it with the testing batch.

    This allows you to train the model for multiple times with different dataset configurations. Even better, it allows you to be more confident in your model evaluation results. It can be used on the go. Sequential nn. ReLU , nn. Flatten , nn. Linear 50, 20 , nn. Linear 20, 10 def forward self, x : '''Forward pass''' return self. Adam network.

    Saving trained model. Suppose that your goal is to build a classifier that correctly classifies input images — like in the example below. You input an image that represents a handwritten digit, and the output is expected to be 5. There are myriad ways for building such a classifier in terms of the model type that can be chosen. But which is best? You have to evaluate each model in order to find how well it works.

    Model evaluation happens after a machine learning model has been trained. It ensures that the model works with real-world data too by feeding samples from an dataset called the test set, which contains samples that the model has not seen before. By comparing the subsequent predictions with the ground truth labels that are also available for these samples, we can see how well the model performs on this dataset.

    And we can thus also see how well it performs on data from the real world, if we used that during model evaluation. Let's pause for a second! Sign up to MachineCurve's free Machine Learning update today!

    You will learn new things and better understand concepts you already know. We send emails at least every Friday. Email Address By signing up, you consent that any information you receive can include services and special offers by email. However, we have to be cautious when evaluating our model. We cannot simply use the data that we trained the model with, to avoid becoming a student who grades their own homework. Because that is what would happen when you evaluated with your training data: as the model has learned to capture patterns related to that particular dataset, the model might perform poorly if these patterns were spurious and therefore not present within real-world data.

    Especially with high-variance models, this can become a problem. Instead, we evaluate models with that test set, which has been selected and contains samples not present within the training set. But how to construct this test set is another question. There are multiple methods for doing so. We then understand why we might apply K-fold Cross Validation instead. This becomes especially important when you generate samples from a sample i. For example, if the first part of your dataset has pictures of ice cream, while the latter one only represents espressos, trouble is guaranteed when you generate the split as displayed above.

    Random shuffling may help you solve these issues. The arrow of time: if you have a time series dataset, your dataset is likely ordered chronologically. Data redundancy: if some samples appear more than once, a simple hold-out split with random shuffling may introduce redundancy between training and testing datasets. That is, identical samples belong to both datasets. This is problematic too, as data used for training thus leaks into the dataset for testing implicitly.

    We would then have a model that is evaluated much more robustly. And precisely that is what K-fold Cross Validation is all about. As in each split a different part of the training data will be used for validation purposes, you effectively train and evaluate your model multiple times, allowing you to tell whether it works with more confidence than with a simple hold-out split.

    Ensuring that your dependencies are up to date. Stating your model imports. Defining the nn. Module class of your neural network, as well as a weights reset function. Adding the preparatory steps in your runtime code. Loading your dataset. Defining the K-fold Cross Validator, and generating folds. Iterating over each fold, training and evaluating another model instance. Averaging across all folds to get final performance. Make sure to install 3. PyTorch, which is the deep learning library that you are training the models with.

    Scikit-learn, for generating the folds. Obviously, you might also want to run everything inside a Jupyter Notebook. All PyTorch functionality is imported as torch.

    We also have some sub imports: Neural network functionality is imported as nn. The DataLoader that we import from torch. We also import specific functionality related to Computer Vision — using torchvision.

    We also import transforms from Torch Vision, which allows us to convert the data into Tensor format later. Finally, we import KFold from sklearn. Module base class — and thus effectively implements a PyTorch neural network.

    You can see that we use one convolutional layer Conv2d with ReLU activations and some Linear layers responsible for generating the predictions. We store the stack in self. Here, we simply pass the data — available in x — to the layers. During the folds, it will be used to reset the parameters of the model. This way, we ensure that the model is trained with weights that are initialized pseudo randomly, avoiding weight leakage. More specifically, our runtime code covers the following aspects: The preparatory steps, where we perform some no surprise preparation steps for running the model.

    Defining the K-fold Cross Validator to generate the folds. Then, generating the splits that we can actually use for training the model, which we also do — once for every fold. After training for every fold, we evaluate the performance for that fold. Finally, we perform performance evaluation for the model — across the folds. In this part, we do the following things: We set the configuration options. CrossEntropyLoss as our loss function. We define a dictionary that will store the results for every fold.

    We set a fixed random number seed, meaning that all our pseudo random number initializers will be initialized using the same initialization token. To solve this, we simply load both parts, and then concatenate them in a ConcatDataset object. Join hundreds of other learners! You can do so by defining a loop where you iterate over the splits, specifying the fold and the list of identifiers of the training and testing samples for that particular fold.

    These can be used for performing the actual training process. Within the for loop, we first perform a print statement, indicating the current fold. You then perform the training process. A sampler can be used within a DataLoader to use particular samples only; in this case based on identifiers, because the SubsetRandomSampler samples elements randomly from a list, without replacements.

    In other words, you create two subsamplers that adhere to the split as specified within the for loop. You can use any batch size that fits in memory, but a batch size of 10 works well in pretty much all of the cases. After preparing the dataset for this particular fold, you initialize the neural network by initializing the class — using SimpleConvNet.

    Then, when the neural network is initialized, you can initialize the optimizer for this particular training session — in this case, we use Adam, with a 1e-4 learning rate.

    Click the link if you want to understand this process in more detail. First, we save the model — so that it will be usable for generating productions later, should you want to re-use it. We compute accuracy after evaluation, print it on screen, and add it to the results dictionary for that particular fold.

    If they do, you know in which fold, and can take a closer look at the data to see what is happening there. Running the code gives you the following result for 5 folds with one epoch per fold. This ensures that the distribution of the data was relatively equal across splits and that it will likely work on real-world data if it has a relatively similar distribution. Generally, what I would now do often is to retrain the model with the full dataset, without evaluation on a hold-out split or with a really small one — e.

    We have already seen that it generalizes and that it does so across folds. We can now use all the data at hand to boost performance perhaps slightly further. I hope that you have learned something from it.

    Reproducibility in PyTorch with K-Fold Cross Validation

    In [0]: import torch if torch. Then we will use the Naive Bayes model as our classifier. Why Naive Bayse? In Scikit-learn's guide to choose the right estimator, it is also suggested that Naive Bayes should be used for text data. I also tried using SVD to reduce dimensionality; however, it did not yield a better performance.

    Training Neural Networks with Validation using PyTorch

    Therefore, we will want to remove stop words, punctuations and characters that don't contribute much to the sentence's meaning. In [0]: import nltk Uncomment to download "stopwords" nltk. In [0]: from sklearn. Alpha' plt. This value is the baseline performance and will be used to evaluate the performance of our fine-tune BERT model. In [1]:! The level of processing here is much less than in previous approachs because BERT was trained with the entire sentences.

    Can you help? Processed: I'm having issues. This is because 1 the model has a specific, fixed vocabulary and 2 the BERT tokenizer has a particular way of handling out-of-vocabulary words.

    Tensor : Tensor of token ids to be fed to a model. Tensor : Tensor of indices specifying which tokens should be attended to by the model. Token IDs: [,,,,,,,,,,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] Tokenizing data This will help save on memory during training and boost the training speed.

    Tutorial: Fine tuning BERT for Sentiment Analysis

    On each iteration, a new model must be trained Validate on the test set Save the result of the validation Repeat steps 2 — 5 Cpn times To get the final score average the results that you got on step 5 You can perform Leave-p-out CV using sklearn — sklearn. Stratified k-Fold Sometimes we may face a large imbalance of the target value in the dataset.

    For example, in a dataset concerning wristwatch prices, there might be a larger number of wristwatch having a high price. In the case of classification, in cats and dogs dataset there might be a large shift towards the dog class. Stratified k-Fold is a variation of the standard k-Fold CV technique which is designed to be effective in such cases of target imbalance.

    It works as follows. Stratified k-Fold splits the dataset on k folds such that each fold contains approximately the same percentage of samples of each target class as the complete set. In the case of regression, Stratified k-Fold makes sure that the mean target value is approximately equal in all the folds. The algorithm of Stratified k-Fold technique: Pick a number of folds — k Split the dataset into k folds.

    Each fold must contain approximately the same percentage of samples of each target class as the complete set Choose k — 1 folds which will be the training set. On each iteration a new model must be trained Validate on the test set Save the result of the validation Repeat steps 3 — 6 k times. As you may have noticed, the algorithm for Stratified k-Fold technique is similar to the standard k-Folds. Stratified k-Fold also has a built-in method in sklearn — sklearn.

    When choosing between different CV methods, make sure you are using the proper one. For example, you might think that your model performs badly simply because you are using k-Fold CV to validate the model which was trained on the dataset with a class imbalance.

    To avoid that you should always do a proper exploratory data analysis on your data. It is a variation of k-Fold but in the case of Repeated k-Folds k is not the number of folds. It is the number of times we will train the model. The general idea is that on every iteration we will randomly select samples all over the dataset as our test set. The algorithm of Repeated k-Fold technique: Pick k — a number of times the model will be trained Pick a number of samples which will be the test set Split the dataset Train on the training set.

    On each iteration of cross-validation, a new model must be trained Validate on the test set Save the result of the validation Repeat steps k times To get the final score average the results that you got on step 6. Repeated k-Fold has clear advantages over standard k-Fold CV. Secondly, we can even set unique proportions for every iteration. Thirdly, random selection of samples from the dataset makes Repeated k-Fold even more robust to selection bias.

    Still, there are some disadvantages. At the same time, some samples might be selected multiple times. Sklearn will help you to implement Repeated k-Fold CV. Just use sklearn. It guarantees that you will have different folds on each iteration. Imagine that we have a parameter p which usually depends on the base algorithm that we are cross-validating. For example, for Logistic Regression it might be the penalty parameter which is used to specify the norm used in the penalization.

    You now have 4 measurements Repeat steps 9 times. Rotate which training fold is the validation fold. Use that p to evaluate on the test set Repeat 10 times from step 2, using each fold in turn as the test fold Save the mean and standard deviation of the evaluation measure over the 10 test folds The algorithm that performed the best was the one with the best average out-of-sample performance across the 10 test folds This technique is computationally expensive because throughout steps 1 — 10 plenty of models should be trained and evaluated.

    Unfortunately, there is no built-in method in sklearn that would perform Nested k-Fold CV for you. The general idea is that we choose a number k — the length of the training set and validate on every possible split containing k samples in the training set.

    If k is higher than 2, we will have to train our model plenty of times which as we have already figured out is an expensive procedure time and computation-wise. This is why Complete CV is used either in theoretical researches or if there is an effective formula that will help to minimize the calculations. Thus, knowing the benefits and disadvantages of cross-validation techniques is vital. You may find them relevant for your ML task and use them instead of sklearn built-in methods.

    In general, as you may have noticed many CV techniques vpf calculator excel sklearn built-in methods. I strongly recommend using them as these methods will save you plenty of time for more complicated tasks. In deep learning, you would normally tempt to avoid CV because of the cost associated with training k different models.


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