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Updated 2 years ago
# Jovian Commit Essentials
# Please retain and execute this cell without modifying the contents for `jovian.commit` to work
# !pip install jovian --upgrade -q
# import jovian
# jovian.set_project('04-feedforward-nn')
# jovian.set_colab_id('1MpYqKQx0ztlUM4-OGJsq-xteATKaZtI-')import torch
import torchvision
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from torchvision.utils import make_grid
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import random_split
%matplotlib inline
# Use a white background for matplotlib figures
matplotlib.rcParams['figure.facecolor'] = '#ffffff'dataset = MNIST(root='data/', download=True, transform=ToTensor())C:\Users\yashr\AppData\Local\Programs\Python\Python39\lib\site-packages\torchvision\datasets\mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ..\torch\csrc\utils\tensor_numpy.cpp:180.)
return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
image , label = dataset[0]
print(image.shape)
print(image.permute(1,2,0).shape)torch.Size([1, 28, 28])
torch.Size([28, 28, 1])
image, label = dataset[0]
print('image.shape:', image.shape)
plt.imshow(image.permute(1, 2, 0), cmap='gray')
print('Label:', label)image.shape: torch.Size([1, 28, 28])
Label: 5
