import torch
import torch.nn as nn
from torchvision import datasets, transforms
from torch.autograd import Variable
MNIST Dataset
train_dataset = datasets.MNIST(root='../data/',
train=True,
transform=transforms.ToTensor(),
download=True)
# change it to False after you have downloaded the data
test_dataset = datasets.MNIST(root='../data/',
train=False,
transform=transforms.ToTensor())
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
Hyper Parameters
batch_size = 100
learning_rate = 0.001
num_epochs = 5
# Data Loader (Input Pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
Show some images.
import numpy as np
import torchvision
# functions to show an image
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
# get some random training images
dataiter = iter(train_loader)
images, labels = dataiter.next()
# show images
imshow(torchvision.utils.make_grid(images[:5]))
# print labels
print(' '.join('%5s' % labels[j] for j in range(5)))
4 2 1 8 9
CNN Model (2 conv layer)
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(1, 16, kernel_size=5, padding=2),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(2))
self.layer2 = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=5, padding=2),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(2))
self.fc = nn.Linear(7*7*32, 10)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
cnn = CNN()
Loss and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)
Train the Model
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = Variable(images)
labels = Variable(labels)
# Forward + Backward + Optimize
optimizer.zero_grad()
outputs = cnn(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if (i+1) % 100 == 0:
print ('Epoch [%d/%d], Iter [%d/%d] Loss: %.4f'
%(epoch+1, num_epochs, i+1, len(train_dataset)//batch_size, loss.data[0]))
Epoch [1/5], Iter [100/600] Loss: 0.1637
Epoch [1/5], Iter [200/600] Loss: 0.0515
Epoch [1/5], Iter [300/600] Loss: 0.0795
Epoch [1/5], Iter [400/600] Loss: 0.1069
Epoch [1/5], Iter [500/600] Loss: 0.0765
Epoch [1/5], Iter [600/600] Loss: 0.0471
Epoch [2/5], Iter [100/600] Loss: 0.1318
Epoch [2/5], Iter [200/600] Loss: 0.0747
Epoch [2/5], Iter [300/600] Loss: 0.0151
Epoch [2/5], Iter [400/600] Loss: 0.0669
Epoch [2/5], Iter [500/600] Loss: 0.1070
Epoch [2/5], Iter [600/600] Loss: 0.0130
Epoch [3/5], Iter [100/600] Loss: 0.0300
Epoch [3/5], Iter [200/600] Loss: 0.0247
Epoch [3/5], Iter [300/600] Loss: 0.0513
Epoch [3/5], Iter [400/600] Loss: 0.0316
Epoch [3/5], Iter [500/600] Loss: 0.0249
Epoch [3/5], Iter [600/600] Loss: 0.0053
Epoch [4/5], Iter [100/600] Loss: 0.0763
Epoch [4/5], Iter [200/600] Loss: 0.0601
Epoch [4/5], Iter [300/600] Loss: 0.0224
Epoch [4/5], Iter [400/600] Loss: 0.0359
Epoch [4/5], Iter [500/600] Loss: 0.0131
Epoch [4/5], Iter [600/600] Loss: 0.0113
Epoch [5/5], Iter [100/600] Loss: 0.0097
Epoch [5/5], Iter [200/600] Loss: 0.0129
Epoch [5/5], Iter [300/600] Loss: 0.0402
Epoch [5/5], Iter [400/600] Loss: 0.0290
Epoch [5/5], Iter [500/600] Loss: 0.0173
Epoch [5/5], Iter [600/600] Loss: 0.0017
Test the Model
cnn.eval() # Change model to 'eval' mode (BN uses moving mean/var).
correct = 0
total = 0
for images, labels in test_loader:
images = Variable(images)
outputs = cnn(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
print('Test Accuracy of the model on the 10000 test images: %d %%' % (100 * correct / total))
Test Accuracy of the model on the 10000 test images: 99 %
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