def train_model(no_epochs):
if torch.cuda.is_available():
dev = "cuda:0"
else:
dev = "cpu"
device = torch.device(dev)
print(device)
batch_size = 256
data_loaders = Data_Loaders(batch_size)
model = Action_Conditioned_FF()
model.to(device)
loss_function = nn.BCEWithLogitsLoss()
losses = []
min_loss = model.evaluate(model, data_loaders.test_loader, loss_function)
losses.append(min_loss)
learning_rate = 0.1
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
for epoch_i in range(no_epochs):
model.train()
epoch_loss = 0
epoch_acc = 0
# sample['input'] and sample['label']
for idx, sample in enumerate(data_loaders.train_loader):
inpt = sample['input'].to(device)
labels = sample['label'].to(device)
labels = labels.unsqueeze(1)
optimizer.zero_grad()
outputs = model(inpt)
loss = loss_function(outputs, labels)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
print(f'| Epoch: {epoch_i+1}', end=" | ")
print(f'Loss: {epoch_loss/len(data_loaders.train_loader):.4f} |')
#print(f'Acc: {epoch_acc/len(data_loaders.train_loader):.3f}')
model.eval()
with torch.no_grad():
test_loss = model.evaluate(model, data_loaders.test_loader,
loss_function)
print(f'------- Test Loss: {test_loss:.4f} -------')
losses.append(test_loss)
PATH = f"saved/weights/weights_{test_loss:.3f}.pkl"
torch.save(model.state_dict(),
PATH,
_use_new_zipfile_serialization=False)
def train_model(no_epochs):
batch_size = 16
data_loaders = Data_Loaders(batch_size)
model = Action_Conditioned_FF()
loss_function = torch.nn.BCEWithLogitsLoss()
# loss_function = torch.nn.MSELoss()
losses = []
min_loss = model.evaluate(model, data_loaders.test_loader, loss_function)
losses.append(min_loss)
# print(min_loss)
optimizer = Adam(model.parameters(), lr=0.001)
prev_loss = float('inf')
for epoch_i in tqdm(range(no_epochs)):
model.train()
for idx, sample in enumerate(data_loaders.train_loader
): # sample['input'] and sample['label']
x, y = sample['input'], sample['label']
optimizer.zero_grad()
y_hat = model.forward(x)
# loss = loss_function(y_hat.unsqueeze(dim=0), y.long())
loss = loss_function(y_hat.reshape(1).float(), y.float())
loss.backward()
optimizer.step()
total_loss = model.evaluate(model, data_loaders.test_loader,
loss_function)
if total_loss < prev_loss:
torch.save(model.state_dict(),
"saved/saved_model.pkl",
_use_new_zipfile_serialization=False)
prev_loss = total_loss
print()
print(total_loss)
losses.append(
model.evaluate(model, data_loaders.test_loader, loss_function))
# print('e')
torch.save(model.state_dict(),
"saved/saved_model.pkl",
_use_new_zipfile_serialization=False)
def train_model(no_epochs):
batch_size = 32
data_loaders = dl.Data_Loaders(batch_size)
model = Action_Conditioned_FF()
loss_fn = nn.BCELoss()
optimizer = torch.optim.Adam(model.model.parameters(), lr=0.001)
test_losses = []
train_losses = []
for epoch_i in range(no_epochs):
#print(f"epoch # {epoch_i + 1}")
model.train()
loss_test = model.evaluate(model, data_loaders.test_loader, loss_fn)
print('test')
print(loss_test)
test_losses.append(loss_test)
l=[]
for idx, sample in enumerate(data_loaders.train_loader): # sample['input'] and sample['label']
input, label = sample['input'].float(), sample['label'].float()
#forward step
out = model.forward(input)
loss_train = loss_fn(out, label.view(-1,1))
l.append(loss_train)
#backpropagation
optimizer.zero_grad()
loss_train.backward()
optimizer.step()
print('train')
print(sum(l)/len(l))
train_losses.append(sum(l) / len(l))
torch.save(model.state_dict(), 'saved_model.pkl', _use_new_zipfile_serialization=False)