def train_batch(self, batch) -> BatchResult:
X, y = batch
# TODO: Train the Block model on one batch of data.
# - Forward pass
# - Backward pass
# - Optimize params
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
N = X.shape[0]
x = X.reshape(N, -1)
if self.device:
x = x.to(self.device)
y = y.to(self.device)
class_scores = self.model(x)
loss = self.loss_fn(class_scores, y).numpy()
self.optimizer.zero_grad()
dout = self.loss_fn.backward()
self.model.backward(dout)
self.optimizer.step()
y_pred = torch.argmax(class_scores, dim=1)
num_correct = torch.sum(y == y_pred).numpy()
# ========================
return BatchResult(loss, num_correct)
def test_batch(self, batch) -> BatchResult:
x, y = batch
x = x.to(self.device, dtype=torch.float) # (B,S,V)
y = y.to(self.device, dtype=torch.long) # (B,S)
seq_len = y.shape[1]
with torch.no_grad():
# TODO: Evaluate the RNN model on one batch of data.
# - Forward pass
# - Loss calculation
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
y_scores_list, self.hidden_state = self.model(
x, hidden_state=self.hidden_state)
# Make it work for CrossEntropy.
y_scores_list = torch.transpose(y_scores_list, 1, 2)
loss = self.loss_fn(y_scores_list, y)
y_predictions = torch.argmax(y_scores_list, dim=1)
num_correct = torch.sum(y == y_predictions)
# ========================
return BatchResult(loss.item(), num_correct.item() / seq_len)
def train_batch(self, batch) -> BatchResult:
X, y = batch
if self.device:
X = X.to(self.device)
y = y.to(self.device)
# TODO: Train the PyTorch model on one batch of data.
# - Forward pass
# - Backward pass
# - Optimize params
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
self.optimizer.zero_grad()
y_pred = self.model(X)
loss = self.loss_fn(y_pred, y)
loss.backward()
self.optimizer.step()
indices = torch.argmax(y_pred, dim=1)
num_correct = torch.eq(indices, y).view(-1).sum().item()
loss = loss.data.tolist()
# ========================
return BatchResult(loss, num_correct)
def train_batch(self, batch) -> BatchResult:
x, y = batch
x = x.to(self.device, dtype=torch.float) # (B,S,V)
y = y.to(self.device, dtype=torch.long) # (B,S)
seq_len = y.shape[1]
# TODO: Train the RNN model on one batch of data.
# - Forward pass
# - Calculate total loss over sequence
# - Backward pass (BPTT)
# - Update params
# - Calculate number of correct char predictions
# ====== YOUR CODE: ======
self.optimizer.zero_grad()
pred, hidden_state = self.model(x, self.hidden_state)
self.hidden_state = hidden_state.detach()
pred = pred.view((-1, x.shape[-1]))
y = y.view((-1))
loss = self.loss_fn(pred, y)
loss.backward()
self.optimizer.step()
preds = torch.argmax(pred, 1)
num_correct = torch.sum((y == preds))
# raise NotImplementedError()
# ========================
# Note: scaling num_correct by seq_len because each sample has seq_len
# different predictions.
return BatchResult(loss.item(), num_correct.item() / seq_len)
def train_batch(self, batch) -> BatchResult:
X, y = batch
if self.device:
X = X.to(self.device)
y = y.to(self.device)
# TODO: Train the PyTorch model on one batch of data.
# - Forward pass
# - Backward pass
# - Optimize params
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
self.model.zero_grad()
res = self.model.forward(X) # run fw pass
loss = self.loss_fn(res, y) # calc loss (also to prep grad)
loss.backward() # calc grad through network
loss = loss.item()
self.optimizer.step() # optimize
#Done training, now report progress
_, pred = res.max(
dim=1) # when specifying dim, gives a tuple with index
num_correct = torch.eq(y, pred).sum().item()
# ========================
return BatchResult(loss, num_correct)
def train_batch(self, batch) -> BatchResult:
x, y = batch
x = x.to(self.device, dtype=torch.float) # (B,S,V)
y = y.to(self.device, dtype=torch.long) # (B,S)
seq_len = y.shape[1]
# TODO: Train the RNN model on one batch of data.
# - Forward pass
# - Calculate total loss over sequence
# - Backward pass (BPTT)
# - Update params
# - Calculate number of correct char predictions
# ====== YOUR CODE: ======
self.h.detach_()
self.optimizer.zero_grad()
y_pred, temp_h = self.model(x, self.h)
y_pred = y_pred.transpose(1, 2)
loss = self.loss_fn(y_pred, y)
loss.backward()
self.optimizer.step()
y_max = y_pred.argmax(1)
num_correct = torch.sum((y_max == y)).float()
self.h = temp_h
# ========================
# Note: scaling num_correct by seq_len because each sample has seq_len
# different predictions.
return BatchResult(loss.item(), num_correct.item() / seq_len)
def train_batch(self, batch) -> BatchResult:
x, y = batch
x = x.to(self.device, dtype=torch.float) # (B,S,V)
y = y.to(self.device, dtype=torch.long) # (B,S)
seq_len = y.shape[1]
# TODO: Train the RNN model on one batch of data.
# - Forward pass
# - Calculate total loss over sequence
# - Backward pass (BPTT)
# - Update params
# - Calculate number of correct char predictions
# ====== YOUR CODE: ======
y_scores, self.hidden_state = self.model(
x, hidden_state=self.hidden_state)
# need to transpose to match the shape the loss function
# (CrossEntropy in the notebook) expects
y_scores = torch.transpose(y_scores, 1, 2)
loss = self.loss_fn(y_scores, y)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.hidden_state = self.hidden_state.detach()
self.hidden_state.requires_grad = False
y_pred = torch.argmax(y_scores, dim=1)
num_correct = torch.sum(y == y_pred)
# ========================
# Note: scaling num_correct by seq_len because each sample has seq_len
# different predictions.
return BatchResult(loss.item(), num_correct.item() / seq_len)
def train_batch(self, batch) -> BatchResult:
x, _ = batch
x = x.to(self.device) # Image batch (N,C,H,W)
# TODO: Train a VAE on one batch.
# ====== YOUR CODE: ======
raise NotImplementedError()
# ========================
return BatchResult(loss.item(), 1 / data_loss.item())
def test_batch(self, batch) -> BatchResult:
X, y = batch
# TODO: Evaluate the Block model on one batch of data.
# - Forward pass
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
raise NotImplementedError()
# ========================
return BatchResult(loss, num_correct)
def test_batch(self, batch) -> BatchResult:
x, _ = batch
x = x.to(self.device) # Image batch (N,C,H,W)
with torch.no_grad():
# TODO: Evaluate a VAE on one batch.
# ====== YOUR CODE: ======
raise NotImplementedError()
# ========================
return BatchResult(loss.item(), 1 / data_loss.item())
def train_batch(self, batch) -> BatchResult:
X, y = batch
# TODO: Train the Block model on one batch of data.
# - Forward pass
# - Backward pass
# - Optimize params
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
raise NotImplementedError()
# ========================
return BatchResult(loss, num_correct)
def test_batch(self, batch) -> BatchResult:
X, y = batch
# TODO: Evaluate the Block model on one batch of data.
# - Forward pass
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
class_scores = self.model(X)
loss = self.loss_fn(class_scores, y).numpy()
y_pred = torch.argmax(class_scores, dim=1)
num_correct = torch.sum(y == y_pred).numpy()
# ========================
return BatchResult(loss, num_correct)
def test_batch(self, batch) -> BatchResult:
X, y = batch
# TODO: Evaluate the Block model on one batch of data.
# - Forward pass
# - Calculate number of correct predictions
# self.model.train(False)
logits = self.model(X)
loss = self.loss_fn(logits, y)
pred = logits.argmax(dim=1)
num_correct = (pred == y).sum().item()
# ========================
return BatchResult(loss, num_correct)
def test_batch(self, batch) -> BatchResult:
X, y = batch
# TODO: Evaluate the Block model on one batch of data.
# - Forward pass
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
res = self.model(X)
loss = self.loss_fn(res)
_, pred = res.max(
dim=1) # when specifying dim, gives a tuple with index
num_correct = torch.eq(y, pred).sum()
# ========================
return BatchResult(loss, num_correct)
def test_batch(self, batch) -> BatchResult:
X, y = batch
if self.device:
X = X.to(self.device)
y = y.to(self.device)
with torch.no_grad():
# TODO: Evaluate the PyTorch model on one batch of data.
# - Forward pass
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
raise NotImplementedError()
# ========================
return BatchResult(loss, num_correct)
def test_batch(self, batch) -> BatchResult:
x, y = batch
x = x.to(self.device, dtype=torch.float) # (B,S,V)
y = y.to(self.device, dtype=torch.long) # (B,S)
seq_len = y.shape[1]
with torch.no_grad():
# TODO: Evaluate the RNN model on one batch of data.
# - Forward pass
# - Loss calculation
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
raise NotImplementedError()
# ========================
return BatchResult(loss.item(), num_correct.item() / seq_len)
def test_batch(self, batch) -> BatchResult:
X, y = batch
if self.device:
X = X.to(self.device)
y = y.to(self.device)
with torch.no_grad():
# TODO: Evaluate the PyTorch model on one batch of data.
# - Forward pass
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
class_scores = self.model(X)
loss = self.loss_fn(class_scores, y).numpy()
y_pred = torch.argmax(class_scores, dim=1)
num_correct = torch.sum(y == y_pred).numpy()
# ========================
return BatchResult(loss, num_correct)
def test_batch(self, batch) -> BatchResult:
X, y = batch
if self.device:
X = X.to(self.device)
y = y.to(self.device)
with torch.no_grad():
# TODO: Evaluate the PyTorch model on one batch of data.
# - Forward pass
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
x_scores = self.model(X)
loss = self.loss_fn(x_scores, y)
_, y_predicted = torch.max(x_scores, 1)
num_correct = (y_predicted == y).sum().item()
# ========================
return BatchResult(loss, num_correct)
def test_batch(self, batch) -> BatchResult:
X, y = batch
if self.device:
X = X.to(self.device)
y = y.to(self.device)
with torch.no_grad():
# TODO: Evaluate the PyTorch model on one batch of data.
# - Forward pass
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
res = self.model.forward(X)
loss = self.loss_fn(res, y).item()
_, pred = res.max(
dim=1) # when specifying dim, gives a tuple with index
num_correct = torch.eq(y, pred).sum().item()
# ========================
return BatchResult(loss, num_correct)
def train_batch(self, batch) -> BatchResult:
x, y = batch
x = x.to(self.device, dtype=torch.float) # (B,S,V)
y = y.to(self.device, dtype=torch.long) # (B,S)
seq_len = y.shape[1]
# TODO: Train the RNN model on one batch of data.
# - Forward pass
# - Calculate total loss over sequence
# - Backward pass (BPTT)
# - Update params
# - Calculate number of correct char predictions
# ====== YOUR CODE: ======
raise NotImplementedError()
# ========================
# Note: scaling num_correct by seq_len because each sample has seq_len
# different predictions.
return BatchResult(loss.item(), num_correct.item() / seq_len)
def test_batch(self, batch) -> BatchResult:
X, y = batch
if self.device:
X = X.to(self.device)
y = y.to(self.device)
with torch.no_grad():
# TODO: Evaluate the PyTorch model on one batch of data.
# - Forward pass
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
y_pred = self.model(X)
loss = self.loss_fn(y_pred, y).data.tolist()
indices = torch.argmax(y_pred, dim=1)
num_correct = torch.eq(indices, y).view(-1).sum().item()
# ========================
return BatchResult(loss, num_correct)
def test_batch(self, batch) -> BatchResult:
x, y = batch
x = x.to(self.device, dtype=torch.float) # (B,S,V)
y = y.to(self.device, dtype=torch.long) # (B,S)
seq_len = y.shape[1]
with torch.no_grad():
# TODO: Evaluate the RNN model on one batch of data.
# - Forward pass
# - Loss calculation
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
y_pred, self.h = self.model(x, self.h)
y_pred = y_pred.transpose(1, 2)
loss = self.loss_fn(y_pred, y)
y_max = y_pred.argmax(1)
num_correct = torch.sum((y_max == y)).float()
# ========================
return BatchResult(loss.item(), num_correct.item() / seq_len)
def train_batch(self, batch) -> BatchResult:
X, y = batch
# TODO: Train the Block model on one batch of data.
# - Forward pass
# - Backward pass
# - Optimize params
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
res = self.model(X) #run fw pass
loss = self.loss_fn(res, y) #calc loss (also to prep grad)
grad = self.loss_fn.backward() # calc grad through network
self.model.backward(grad)
self.optimizer.step() #optimize
_, pred = res.max(
dim=1) # when specifying dim, gives a tuple with index
num_correct = torch.eq(y, pred).sum()
# ========================
return BatchResult(loss, num_correct)
def train_batch(self, batch) -> BatchResult:
X, y = batch
if self.device:
X = X.to(self.device)
y = y.to(self.device)
# TODO: Train the PyTorch model on one batch of data.
# - Forward pass
# - Backward pass
# - Optimize params
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
self.optimizer.zero_grad()
x_scores = self.model.forward(X)
loss = self.loss_fn(x_scores, y)
loss.backward()
self.optimizer.step()
_, y_predicted = torch.max(x_scores, dim=1)
num_correct = (y_predicted == y).sum().item()
# ========================
return BatchResult(loss, num_correct)
def train_batch(self, batch) -> BatchResult:
X, y = batch
if self.device:
X = X.to(self.device)
y = y.to(self.device)
# TODO: Train the PyTorch model on one batch of data.
# - Forward pass
# - Backward pass
# - Optimize params
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
class_scores = self.model(X)
self.optimizer.zero_grad()
loss = self.loss_fn(class_scores, y)
loss.backward()
self.optimizer.step()
y_pred = torch.argmax(class_scores, dim=1)
num_correct = torch.sum(y == y_pred).to('cpu').detach().numpy()
loss = loss.to('cpu').detach().numpy().tolist()
# ========================
return BatchResult(loss, num_correct)
def test_batch(self, batch) -> BatchResult:
x, y = batch
x = x.to(self.device, dtype=torch.float) # (B,S,V)
y = y.to(self.device, dtype=torch.long) # (B,S)
seq_len = y.shape[1]
with torch.no_grad():
# TODO: Evaluate the RNN model on one batch of data.
# - Forward pass
# - Loss calculation
# - Calculate number of correct predictions
# ====== YOUR CODE: ======
pred, self.hidden_state = self.model(x, self.hidden_state)
pred = logits.view((-1, x.shape[-1]))
y = y.view((-1))
loss = self.loss_fn(logits, y)
num_correct = torch.sum(torch.argmax(logits, dim=1) == y)
# raise NotImplementedError()
# ========================
return BatchResult(loss.item(), num_correct.item() / seq_len)
def train_batch(self, batch) -> BatchResult:
X, y = batch
# TODO: Train the Block model on one batch of data.
# - Forward pass
# - Backward pass
# - Optimize params
# - Calculate number of correct predictions
# self.model.train(True)
logits = self.model(X)
self.optimizer.zero_grad()
loss = self.loss_fn(logits, y)
# todo check if should be loss.backward
back_loss = self.loss_fn.backward()
self.model.backward(back_loss)
# loss.backward()
self.optimizer.step()
pred = logits.argmax(dim=1)
num_correct = (pred == y).sum().item()
# ========================
return BatchResult(loss, num_correct)
