Python - PyTorch - MNIST Home : www.sharetechnote.com
PyTorch - MNIST
Downloading the image
import torch
import torchvision
from torchvision import datasets, transforms
from torch import nn, optim
from time import time
import matplotlib.pyplot as plt
import numpy as np
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
]);
dataset = datasets.MNIST(r'..\input\MNIST', download=True, train=True, transform=transform);
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ..\input\MNIST\MNIST\raw\train-images-idx3-ubyte.gz
0.0%0.1%0.2%0.2%0.3%0.4%0.5%0.6%0.7%0.7%0.8%0.9%1.0%1.1%1.2%1.2%1.3%1.4%1.5%1.6%1.7%1.7%1.8%
1.9%2.0%2.1%2.1%2.2%2.3%2.4%2.5%2.6%2.6%2.7%2.8%2.9%3.0%3.1%3.1%3.2%3.3%3.4%3.5%3.6%3.6%3
.....
Extracting ..\input\MNIST\MNIST\raw\train-images-idx3-ubyte.gz to ..\input\MNIST\MNIST\raw
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ..\input\MNIST\MNIST\raw\train-labels-
idx1-ubyte.gz
0.0%28.4%56.7%85.1%113.5%Extracting ..\input\MNIST\MNIST\raw\train-labels-idx1-ubyte.gz to ..\input\MNIST\
MNIST\raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ..\input\MNIST\MNIST\raw\t10k-
images-idx3-ubyte.gz
93.4%93.9%94.4%94.9%95.4%95.9%96.4%96.9%97.4%97.9%98.4%98.9%99.4%99.9%100.4%Extracting ..\input\
MNIST\MNIST\raw\t10k-images-idx3-ubyte.gz to ..\input\MNIST\MNIST\raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ..\input\MNIST\MNIST\raw\t10k-labels-
idx1-ubyte.gz
0.0%180.4%Extracting ..\input\MNIST\MNIST\raw\t10k-labels-idx1-ubyte.gz to ..\input\MNIST\MNIST\raw
Processing...
Done!
Loading the Data
dataloader = torch.utils.data.DataLoader(dataset, batch_size=10, shuffle=False);
Extracting the Individual Data
dataiter = iter(dataloader)
images, labels = dataiter.next()
print(images.shape)
print(labels.shape)
==> torch.Size([10, 1, 28, 28])
torch.Size([10])
plt.imshow(images[0].numpy().squeeze(), cmap='gray_r');
print(images[0])
tensor([[[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000],
[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000],
.......
[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000],
[-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
-1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000, -1.0000]]])
print(images[0].numpy().squeeze())
[[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
......
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]
[-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. -1. -1.
-1. -1. -1. -1. ]]
Referene :
[1] Building Your First PyTorch Solution