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Created 2 years ago
import numpy as np
import torch
import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import MNIST
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
# Downloading data set in the "data" directory
dataset = MNIST(root='data/', download=True)
0.1%
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST\raw\train-images-idx3-ubyte.gz
31.0%IOPub message rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_msg_rate_limit`.
Current values:
NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
NotebookApp.rate_limit_window=3.0 (secs)
100.0%
Extracting data/MNIST\raw\train-images-idx3-ubyte.gz to data/MNIST\raw
102.8%
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data/MNIST\raw\train-labels-idx1-ubyte.gz
Extracting data/MNIST\raw\train-labels-idx1-ubyte.gz to data/MNIST\raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to data/MNIST\raw\t10k-images-idx3-ubyte.gz
61.2%IOPub message rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_msg_rate_limit`.
Current values:
NotebookApp.iopub_msg_rate_limit=1000.0 (msgs/sec)
NotebookApp.rate_limit_window=3.0 (secs)
#Seeing the number of data in the data set
train_dataset = MNIST(root='data/', train=True)
test_dataset = MNIST(root='data/', train=False)
len(train_dataset),len(test_dataset)
(60000, 10000)
#Visualizing a data
%matplotlib inline
image, label = dataset[1000]
plt.imshow(image, cmap='gray')
print('Label of 1000th data:', label)
Label of 1000th data: 0
# Converting to Tensor
dataset = MNIST(root='data/', train=True, transform=transforms.ToTensor())