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ouput -> output

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rasbt
2024-05-05 12:21:10 -05:00
parent d361cef65f
commit 9457676640
4 changed files with 8 additions and 8 deletions
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4
appendix-E/01_main-chapter-code/previous_chapters.py
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@@ -466,7 +466,7 @@ def calc_accuracy_loader(data_loader, model, device, num_batches=None):
for i, (input_batch, target_batch) in enumerate(data_loader):
if i < num_batches:
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
logits = model(input_batch)[:, -1, :] # Logits of last ouput token
logits = model(input_batch)[:, -1, :] # Logits of last output token
predicted_labels = torch.argmax(logits, dim=-1)
num_examples += predicted_labels.shape[0]
@@ -478,7 +478,7 @@ def calc_accuracy_loader(data_loader, model, device, num_batches=None):
def calc_loss_batch(input_batch, target_batch, model, device):
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
logits = model(input_batch)[:, -1, :] # Logits of last ouput token
logits = model(input_batch)[:, -1, :] # Logits of last output token
loss = torch.nn.functional.cross_entropy(logits, target_batch)
return loss

4
ch06/02_bonus_additional-experiments/additional-experiments.py
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@@ -139,7 +139,7 @@ def instantiate_model(choose_model, load_weights):
def calc_loss_batch(input_batch, target_batch, model, device, trainable_token=-1):
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
logits = model(input_batch)[:, trainable_token, :] # Logits of last ouput token
logits = model(input_batch)[:, trainable_token, :] # Logits of last output token
loss = torch.nn.functional.cross_entropy(logits, target_batch)
return loss
@@ -175,7 +175,7 @@ def calc_accuracy_loader(data_loader, model, device, num_batches=None, trainable
for i, (input_batch, target_batch) in enumerate(data_loader):
if i < num_batches:
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
logits = model(input_batch)[:, trainable_token, :] # Logits of last ouput token
logits = model(input_batch)[:, trainable_token, :] # Logits of last output token
predicted_labels = torch.argmax(logits, dim=-1)
num_examples += predicted_labels.shape[0]

4
ch06/03_bonus_imdb-classification/train-bert-hf.py
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@@ -54,7 +54,7 @@ class IMDBDataset(Dataset):
def calc_loss_batch(input_batch, target_batch, model, device):
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
# logits = model(input_batch)[:, -1, :] # Logits of last ouput token
# logits = model(input_batch)[:, -1, :] # Logits of last output token
logits = model(input_batch).logits
loss = torch.nn.functional.cross_entropy(logits, target_batch)
return loss
@@ -90,7 +90,7 @@ def calc_accuracy_loader(data_loader, model, device, num_batches=None):
for i, (input_batch, target_batch) in enumerate(data_loader):
if i < num_batches:
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
# logits = model(input_batch)[:, -1, :] # Logits of last ouput token
# logits = model(input_batch)[:, -1, :] # Logits of last output token
logits = model(input_batch).logits
predicted_labels = torch.argmax(logits, dim=1)
num_examples += predicted_labels.shape[0]

4
ch06/03_bonus_imdb-classification/train-gpt.py
View File

@@ -83,7 +83,7 @@ def instantiate_model(choose_model, load_weights):
def calc_loss_batch(input_batch, target_batch, model, device, trainable_token=-1):
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
logits = model(input_batch)[:, trainable_token, :] # Logits of last ouput token
logits = model(input_batch)[:, trainable_token, :] # Logits of last output token
loss = torch.nn.functional.cross_entropy(logits, target_batch)
return loss
@@ -119,7 +119,7 @@ def calc_accuracy_loader(data_loader, model, device, num_batches=None, trainable
for i, (input_batch, target_batch) in enumerate(data_loader):
if i < num_batches:
input_batch, target_batch = input_batch.to(device), target_batch.to(device)
logits = model(input_batch)[:, trainable_token, :] # Logits of last ouput token
logits = model(input_batch)[:, trainable_token, :] # Logits of last output token
predicted_labels = torch.argmax(logits, dim=-1)
num_examples += predicted_labels.shape[0]