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pytorch_nn.py
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import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.nn import Module, Linear, NLLLoss
from torch.nn.functional import relu, log_softmax
from torch.optim import SGD
from torchvision.datasets import MNIST
from torchvision.transforms import Compose, ToTensor, Normalize
from torch.utils.data import DataLoader
def simple_gradient():
# print the gradient of 2x^2 + 5x
x = Variable(torch.ones(2, 2) * 2, requires_grad=True)
......@@ -19,39 +21,39 @@ def create_nn(batch_size=200, learning_rate=0.01, epochs=10,
log_interval=10):
train_loader = DataLoader(
datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
MNIST('../data', train=True, download=True,
transform=Compose([
ToTensor(),
Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
test_loader = DataLoader(
datasets.MNIST('../data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
MNIST('../data', train=False, transform=Compose([
ToTensor(),
Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True)
class Net(nn.Module):
class Net(Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(28 * 28, 200)
self.fc2 = nn.Linear(200, 200)
self.fc3 = nn.Linear(200, 10)
self.fc1 = Linear(28 * 28, 200)
self.fc2 = Linear(200, 200)
self.fc3 = Linear(200, 10)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = relu(self.fc1(x))
x = relu(self.fc2(x))
x = self.fc3(x)
return F.log_softmax(x)
return log_softmax(x)
net = Net()
print(net)
# create a stochastic gradient descent optimizer
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)
optimizer = SGD(net.parameters(), lr=learning_rate, momentum=0.9)
# create a loss function
criterion = nn.NLLLoss()
criterion = NLLLoss()
# run the main training loop
for epoch in range(epochs):
......@@ -67,7 +69,7 @@ def create_nn(batch_size=200, learning_rate=0.01, epochs=10,
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.data[0]))
100. * batch_idx / len(train_loader), loss.data[0]))
# run a test loop
test_loss = 0
......@@ -81,10 +83,11 @@ def create_nn(batch_size=200, learning_rate=0.01, epochs=10,
pred = net_out.data.max(1)[1] # get the index of the max log-probability
correct += pred.eq(target.data).sum()
test_loss /= len(test_loader.dataset)
len_test_dataset = len(test_loader.dataset)
test_loss /= len_test_dataset
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
test_loss, correct, len_test_dataset,
100. * correct / len_test_dataset))
if __name__ == "__main__":
......
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