Simple Pytorch script using MNIST to confirm installation is working
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# pytorch_mnist.py | |
# Simple MNIST training script on Pytorch to confirm installation is working correctly | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torch.optim as optim | |
import torchvision | |
n_epochs = 3 | |
batch_size_train = 64 | |
batch_size_test = 1000 | |
learning_rate = 0.01 | |
momentum = 0.5 | |
log_interval = 10 | |
random_seed = 1 | |
torch.backends.cudnn.enabled = False | |
torch.manual_seed(random_seed) | |
train_loader = torch.utils.data.DataLoader( | |
torchvision.datasets.MNIST('files/', train=True, download=True, | |
transform=torchvision.transforms.Compose([ | |
torchvision.transforms.ToTensor(), | |
torchvision.transforms.Normalize( | |
(0.1307,), (0.3081,)) | |
])), | |
batch_size=batch_size_train, shuffle=True) | |
test_loader = torch.utils.data.DataLoader( | |
torchvision.datasets.MNIST('files/', train=False, download=True, | |
transform=torchvision.transforms.Compose([ | |
torchvision.transforms.ToTensor(), | |
torchvision.transforms.Normalize( | |
(0.1307,), (0.3081,)) | |
])), | |
batch_size=batch_size_test, shuffle=True) | |
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) | |
def forward(self, x): | |
x = F.relu(F.max_pool2d(self.conv1(x), 2)) | |
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) | |
x = x.view(-1, 320) | |
x = F.relu(self.fc1(x)) | |
x = F.dropout(x, training=self.training) | |
x = self.fc2(x) | |
return F.log_softmax(x) | |
network = Net() | |
optimizer = optim.SGD(network.parameters(), lr=learning_rate, | |
momentum=momentum) | |
train_losses = [] | |
train_counter = [] | |
test_losses = [] | |
test_counter = [i*len(train_loader.dataset) for i in range(n_epochs + 1)] | |
def train(epoch): | |
network.train() | |
for batch_idx, (data, target) in enumerate(train_loader): | |
optimizer.zero_grad() | |
output = network(data) | |
loss = F.nll_loss(output, target) | |
loss.backward() | |
optimizer.step() | |
if batch_idx % log_interval == 0: | |
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( | |
epoch, batch_idx * len(data), len(train_loader.dataset), | |
100. * batch_idx / len(train_loader), loss.item())) | |
train_losses.append(loss.item()) | |
train_counter.append( | |
(batch_idx*64) + ((epoch-1)*len(train_loader.dataset))) | |
def test(): | |
network.eval() | |
test_loss = 0 | |
correct = 0 | |
with torch.no_grad(): | |
for data, target in test_loader: | |
output = network(data) | |
test_loss += F.nll_loss(output, target, size_average=False).item() | |
pred = output.data.max(1, keepdim=True)[1] | |
correct += pred.eq(target.data.view_as(pred)).sum() | |
test_loss /= len(test_loader.dataset) | |
test_losses.append(test_loss) | |
print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( | |
test_loss, correct, len(test_loader.dataset), | |
100. * correct / len(test_loader.dataset))) | |
for epoch in range(1, 6): | |
train(epoch) | |
test() |
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