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2 years ago
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor, Lambda, Compose
import matplotlib.pyplot as plt
dir(datasets)
['CIFAR10',
 'CIFAR100',
 'CocoCaptions',
 'CocoDetection',
 'ImageFolder',
 'LSUN',
 'LSUNClass',
 'MNIST',
 '__all__',
 '__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__path__',
 '__spec__',
 'cifar',
 'coco',
 'folder',
 'lsun',
 'mnist']
# Download training data from open datasets.
training_data = datasets.MNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor(),
)

# Download test data from open datasets.
test_data = datasets.MNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor(),
)
Files already downloaded Files already downloaded 
batch_size = 66

# Create data loaders.
train_dataloader = DataLoader(training_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)

for X, y in test_dataloader:
    print("Shape of X [N, C, H, W]: ", X.shape)
    print("Shape of y: ", y.shape, y.dtype)
    break
Shape of X [N, C, H, W]: torch.Size([66, 1, 28, 28]) Shape of y: torch.Size([66]) torch.int64 
# generate circles
from sklearn.datasets import make_circles
X, y = make_circles(n_samples=1000, noise=0.1, random_state=1)