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Created 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)