PyTorch DataLoader

Good job defining the Dataset class! The WaterDataset you just created is now available for you to use.

The next step in preparing the training data is to set up a DataLoader. A PyTorch DataLoader can be created from a Dataset to load data, split it into batches, and perform transformations on the data if desired. Then, it yields a data sample ready for training.

In this exercise, you will build a DataLoader based on the WaterDataset. The DataLoader class you will need has already been imported for you from torch.utils.data. Let's get to it!

This exercise is part of the course

Intermediate Deep Learning with PyTorch

Exercise instructions

Create an instance of WaterDataset from water_train.csv, assigning it to dataset_train.
Create dataloader_train based on dataset_train, using a batch size of two and shuffling the samples.
Get a batch of features and labels from the DataLoader and print them.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create an instance of the WaterDataset
dataset_train = ____(____)

# Create a DataLoader based on dataset_train
dataloader_train = ____(
    ____,
    batch_size=____,
    shuffle=____,
)

# Get a batch of features and labels
features, labels = ____
print(features, labels)

Edit and Run Code

This exercise is part of the course

Intermediate Deep Learning with PyTorch

IntermediateSkill Level

4.8+

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Learn how to train neural networks in a robust way. In this chapter, you will use object-oriented programming to define PyTorch datasets and models and refresh your knowledge of training and evaluating neural networks. You will also get familiar with different optimizers and, finally, get to grips with various techniques that help mitigate the problems of unstable gradients so ubiquitous in neural nets training.

Exercise 1: PyTorch and object-oriented programming Exercise 2: PyTorch Dataset Exercise 3: PyTorch DataLoader

Current Exercise

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Exercise 1: Handling sequences with PyTorch Exercise 2: Generating sequences Exercise 3: Sequential Dataset Exercise 4: Recurrent Neural Networks Exercise 5: Sequential architectures Exercise 6: Building a forecasting RNN Exercise 7: LSTM and GRU cells Exercise 8: RNN vs. LSTM vs. GRU Exercise 9: LSTM network Exercise 10: GRU network Exercise 11: Training and evaluating RNNs Exercise 12: RNN training loop Exercise 13: Evaluating forecasting models

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Exercise 1: Multi-input models Exercise 2: Two-input dataset Exercise 3: Two-input model Exercise 4: Training two-input model Exercise 5: Multi-output models Exercise 6: Two-output Dataset and DataLoader Exercise 7: Two-output model architecture Exercise 8: Training multi-output models Exercise 9: Evaluation of multi-output models and loss weighting Exercise 10: Multi-output model evaluation Exercise 11: Loss weighting Exercise 12: Wrap-up