Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

How does this change our code so far? Let’s check it out!
First, we need to add both Dataset and DataLoader elements into our data
preparation part of the code. Also, notice that we do not send our tensors to the
device just yet (just like we did in Notebook Cell 2.1). It should look like this:
Define - Data Preparation V1
1 %%writefile data_preparation/v1.py
2
3 # Our data was in Numpy arrays, but we need to transform them
4 # into PyTorch's Tensors
5 x_train_tensor = torch.as_tensor(x_train).float()
6 y_train_tensor = torch.as_tensor(y_train).float()
7
8 # Builds Dataset
9 train_data = TensorDataset(x_train_tensor, y_train_tensor) 1
10
11 # Builds DataLoader
12 train_loader = DataLoader( 2
13 dataset=train_data,
14 batch_size=16,
15 shuffle=True,
16 )
1 Building a dataset of tensors
2 Building a data loader that yields mini-batches of size 16
Run - Data Preparation V1
%run -i data_preparation/v1.py
Next, we need to incorporate the mini-batch gradient descent logic into our model
training part of the code. But we need to run the model configuration first.
Run - Model Configuration V1
%run -i model_configuration/v1.py
DataLoader | 139