import torch
from matplotlib import pyplot as plt
from torchvision import datasets
from torch.utils.data import DataLoader
from torchvision import transforms
import torch.optim as optim
import numpy as np
batch_size = 64
batch_size_test = 100
data_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
minist_tainloader = datasets.MNIST(root='./', train=True, download=True, transform=data_transform)
minist_testloader = datasets.MNIST(root='./', train=False, download=True, transform=data_transform)
trainloader = DataLoader(minist_tainloader, batch_size=batch_size, shuffle=True)
testloader = DataLoader(minist_testloader, batch_size=batch_size_test, shuffle=False)
class Model(torch.nn.Module):
def __init__(self):
super(Model, self).__init__()
self.linear1 = torch.nn.Linear(784, 512)
self.linear2 = torch.nn.Linear(512, 256)
self.linear3 = torch.nn.Linear(256, 128)
self.linear4 = torch.nn.Linear(128, 64)
self.linear5 = torch.nn.Linear(64, 10)
self.relu = torch.nn.ReLU()
def forward(self, x):
x = x.view(-1, 784)
x = self.relu(self.linear1(x))
x = self.relu(self.linear2(x))
x = self.relu(self.linear3(x))
x = self.relu(self.linear4(x))
return self.linear5(x)
model = Model()
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=1e-2, momentum=0.5)
loss_list = list()
def test_accuracy():
correct = 0
with torch.no_grad():
for data in testloader:
images, labels = data
pred = model(images)
total_num = 0
correct = 0
for i in range(batch_size_test):
labels_np = labels.numpy().tolist()
pred_np = pred.numpy().tolist()
total_num += 1
if labels_np[i] == pred_np[i].index(max(pred_np[i])):
correct += 1
print(f'Accuracy = {correct/total_num}, i = {i}')
if __name__ == '__main__':
for epoch in range(10):
for i, data in enumerate(trainloader, 0):
inputs, label = data
outputs = model(inputs)
optimizer.zero_grad()
loss = criterion(outputs, label)
loss_list.append(loss)
loss.backward()
optimizer.step()
print(f'[{epoch}]: loss = {loss}')
