- Install pytorch, pytorch is a python-first deep learning framework. Using pytorch can automatically combine factors into strategies. If GPU training is needed, only N cards are supported, so choosing CPU mode here is fine. https://pytorch.org/, torchvision is used for image processing, torchaudio is not needed so it is not installed.
sudo /home/skka3134/folder/bot/bin/python -m pip install torch
2. Set up the dataset, inherit from the Dateset class to form TimeSeriesDataset
from torch.utils.data import Dataset
class TimeSeriesDataset(Dataset):
def __init__(self, X, y):
self.X = X
self.y = y
def __len__(self):
return len(self.X)
def __getitem__(self, i):
return self.X[i], self.y[i]
train_dataset = TimeSeriesDataset(X_train, y_train)
test_dataset = TimeSeriesDataset(X_test, y_test)
- Load the dataset
from torch.utils.data import DataLoader
batch_size = 16 #Read 16 data points per batch, if training with GPU, can increase, maybe to 128?
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) #shuffle=True means shuffle the data
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) #shuffle=False means do not shuffle the data
- Visualization processing
for _, batch in enumerate(train_loader):
x_batch, y_batch = batch[0].to(device), batch[1].to(device)
print(x_batch.shape, y_batch.shape)
break