<jupyter_start><jupyter_text><jupyter_code>from __future__ import print_function
import torch 
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np
plt.ion()
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST(root='.', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,),(0.3081))])),
    batch_size = 64, shuffle = True, num_workers=4
)
test_loader = torch.utils.data.DataLoader(
    datasets.MNIST(root='.', train=False, transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,),(0.3081))])),
    batch_size = 64, shuffle = True, num_workers=4
)
class Net(nn.Module):
  def __init__(self):
    super(Net, self).__init__()
    self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
    self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
    self.conv2_drop = nn.Dropout2d()
    self.fc1 = nn.Linear(320, 50)
    self.fc2 = nn.Linear(50, 10)<jupyter_output><empty_output>
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