Cheat Sheet
(under construction)
Pytorch Cheat Sheet
See also : numpy cheat sheet
Numpy <-> Torch Tensor
Tensors are similar to NumPy’s ndarrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.
Converting a Torch Tensor to a NumPy Array
a = torch.ones(5)
print(a)
b = a.numpy()
print(b)
Converting NumPy Array to Torch Tensor
See how changing the np array changed the Torch Tensor automatically
import numpy as np
a = np.ones(5)
b = torch.from_numpy(a)
np.add(a, 1, out=a)
print(a)
print(b)
Datasets
There are several options for dataset
load data into a numpy array, then convert this array into a
torch.*Tensor
For images, packages such as Pillow, OpenCV are useful
(Recommend) For vision, we have created a package called
torchvision
data loaders for common datasets such as Imagenet, CIFAR10, MNIST, etc.
Using GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net.to(device)
for epoch in range(epochs):
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
# ...
# evaluation
with torch.no_grad():
for inputs, labels in test_loader:
inputs, labels = inputs.to(device), labels.to(device)
# ...
Visualizing Loss Curve
Evaluation
Model function
make it a function
Train function
Test function
Plot output image function
Last updated
Was this helpful?