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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
# import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.autograd import Variable
input_size = 784
hidden_size = 500
num_classes = 10
output_size = num_classes
num_epoches = 5
batch_size = 100
learning_rate = 0.001
train_dataset = torchvision.datasets.MNIST(root = './data', train = True, transform = transforms.ToTensor(), download = True)
test_dataset = torchvision.datasets.MNIST(root = './data', train = False, transform = transforms.ToTensor(), )
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)
class Net(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Net, self).__init__()
self.layer1 = nn.Linear(input_size, hidden_size)
self.layer2 = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = F.relu(self.layer1(x))
x = self.layer2(x)
return x
net = Net(input_size, hidden_size, output_size)
print(net)
net.load_state_dict(torch.load('feedforward_parameters.pkl'))
# criterion = nn.CrossEntropyLoss()
# optimizer = torch.optim.Adam(net.parameters(), lr= learning_rate)
# for epoch in range(num_epoches):
# for i, (images, labels) in enumerate(train_loader):
# images = Variable(images.view(-1, 28*28))
# labels = Variable(labels)
# optimizer.zero_grad()
# outputs = net(images)
# loss = criterion(outputs, labels)
# loss.backward()
# optimizer.step()
# if (i+1)%100 == 0:
# print ('Epoch [%d/%d], Step [%d/%d], Loss: %.4f' % (epoch+1, num_epoches, i+1, len(train_dataset)//batch_size, loss.data[0]))
# # 60000 train_dataset, batchsize = 100, the ith batch in 600 batches
correct = 0.0
total = 0.0
for images, labels in test_loader:
images = Variable(images.view(-1, 28*28))
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum()
print('Accuracy:%.2lf %%' % (100*correct/total))
torch.save(net.state_dict(),'feedforward_parameters.pkl')
net.load_state_dict(torch.load('feedforward_parameters.pkl'))
torch.save(net.state_dict(),'feedforward_parameters.pkl')
