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main.py
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main.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.optim as optim
from PIL import Image
import torchvision
import torchvision.transforms as transforms
from dataset import mnistDataSet
from net import network
classes = ["T-shirt/top","Trouser","Pullover","Dress","Coat","Sandal","Shirt","Sneaker","Bag","Ankle boot"]
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5),(0.5))]
)
batchsize = 50
trainset = mnistDataSet('fashionmnist\\data\\fashion', "train")
testset = mnistDataSet('fashionmnist\\data\\fashion', "t10k")
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batchsize, shuffle=True, num_workers=2)
testloader = torch.utils.data.DataLoader(testset, batch_size=batchsize, shuffle=False, num_workers=2)
if __name__ == "__main__":
net = network()
epochs = 10
loss = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
for e in range(epochs):
net.train()
running_loss = 0.0
for i, d in enumerate(trainloader, 0):
inputs, labels = d
inputs, labels = Variable(inputs), Variable(labels)
optimizer.zero_grad()
outputs = net(inputs)
l = loss(outputs, labels.long())
l.backward()
optimizer.step()
n = batchsize*(i+1)
if n % 10000 == 0:
print("TRAINING: Epoch {}, Batch {}/60000, Loss {}".format(e,n,l.item()))
net.eval()
test_loss = 0
correct = 0
total = 0
for i, d in enumerate(testloader, 0):
inputs, labels = d
inputs, labels = Variable(inputs), Variable(labels)
labels = labels.long()
outputs = net(inputs)
l = loss(outputs, labels)
pred = torch.argmax(outputs.data, 1)
total += batchsize
tru = (pred == labels).sum()
correct += tru.item()
test_loss += l.item()
n = batchsize*(i+1)
print("TESTING: Epoch {}, Batch {}/10000, Loss {:.4f}, ACC {:.8f}".format(e,n,l.item(), tru.item()/batchsize))
test_loss /= len(testloader.dataset)
print("TEST RESULT ON EPOCH {}, ACCURACY {:.8f}, AVG. LOSS {:.4f}".format(e,correct/(len(testloader.dataset)),test_loss))
print("Finished Training")