def train_MNIST_MLP():
epochs = 1
trian_batch_size, val_batch_size = 10, 2000
lr = 0.001
cnt = 0
loss_list = []
val_acc_list = []
train_loader, validation_loader = get_dataloader(trian_batch_size,
val_batch_size)
model = MNIST(p=0.3).to(device)
criterion = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)
for epoch in trange(epochs):
model.train()
loss_epoch = 0
for step, (img, label) in enumerate(train_loader):
img, label = img.view(-1, 28 * 28).to(device), label.to(device)
pred = model(img)
optimizer.zero_grad()
loss = criterion(pred, label)
loss_epoch += loss.item() * pred.shape[0]
loss.backward()
optimizer.step()
loss_epoch /= len(train_loader.dataset)
loss_list.append(loss_epoch)
model.eval()
val_acc = 0
for step, (img, label) in enumerate(validation_loader):
img, label = img.view(-1, 28 * 28).to(device), label.to(device)
pred = model(img)
topv, topi = pred.topk(1, dim=1)
n_correct = (topi.view(-1) == label).type(torch.int)
val_acc += n_correct.sum().item()
val_acc /= len(validation_loader.dataset)
val_acc_list.append(val_acc)
print(epoch, loss_epoch, val_acc)
fig, ax = plt.subplots(2, 1, figsize=(30, 15))
ax[0].plot(loss_list)
ax[1].plot(val_acc_list)
x = self.model(x)
return x
epochs = 1
trian_batch_size, val_batch_size = 10, 2000
lr = 0.001
cnt = 0
loss_list = []
val_acc_list = []
train_loader, validation_loader = get_dataloader(trian_batch_size,
val_batch_size)
model = MNIST(p=0.3).to(device)
criterion = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=lr)
for epoch in trange(epochs):
model.train()
loss_epoch = 0
for step, (img, label) in enumerate(train_loader):
img, label = img.view(-1, 28 * 28).to(device), label.to(device)
print(img.shape)
pred = model(img)
optimizer.zero_grad()
loss = criterion(pred, label)
loss_epoch += loss.item() * pred.shape[0]
loss.backward()
optimizer.step()
loss_epoch /= len(train_loader.dataset)
