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toymodel.py
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toymodel.py
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from torchvision import datasets, transforms
import torch
from torch.utils.data import DataLoader
import torch.nn as nn
from Networks import models
import numpy as np
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_data = datasets.MNIST("./data/", train=True, download=True, transform=transforms.ToTensor())
test_data = datasets.MNIST("./data/", train=False, transform=transforms.ToTensor())
dataset_size = len(train_data)
train_loader = DataLoader(dataset=train_data, batch_size=64, shuffle=True)
test_loader = DataLoader(dataset=test_data, batch_size=64, shuffle=True)
model = models.get_model('LeNet')
optimizer = torch.optim.SGD(model.parameters(), lr=0.5)
model.to(device)
model.train()
criterion = nn.CrossEntropyLoss()
losses = []
epochs = 10
for epoch in range(epochs):
train_correct = 0
for batch in train_loader:
data, target = batch
data = data.to(device)
target = target.to(device)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
losses.append(loss.item())
_, tar = torch.max(output.data, 1)
train_correct += torch.sum(tar == target.data)
print(f"Train Epoch: {epoch} \t Loss: {np.mean(losses):.6f} \t correct:{100 * train_correct / dataset_size}")