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fix typo in comment
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@ -494,7 +494,7 @@ def train_classifier_simple(model, train_loader, val_loader, optimizer, device,
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model.train() # Set model to training mode
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for input_batch, target_batch in train_loader:
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optimizer.zero_grad() # Reset loss gradients from previous epoch
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optimizer.zero_grad() # Reset loss gradients from previous batch iteration
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loss = calc_loss_batch(input_batch, target_batch, model, device)
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loss.backward() # Calculate loss gradients
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optimizer.step() # Update model weights using loss gradients
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@ -1230,7 +1230,7 @@
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" model.train() # Set model to training mode\n",
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" \n",
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" for input_batch, target_batch in train_loader:\n",
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" optimizer.zero_grad() # Reset loss gradients from previous epoch\n",
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" optimizer.zero_grad() # Reset loss gradients from previous batch iteration\n",
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" loss = calc_loss_batch(input_batch, target_batch, model, device)\n",
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" loss.backward() # Calculate loss gradients\n",
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" optimizer.step() # Update model weights using loss gradients\n",
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@ -2477,7 +2477,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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"version": "3.10.6"
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}
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},
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"nbformat": 4,
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@ -84,7 +84,7 @@ def train_model_simple(model, train_loader, val_loader, optimizer, device, num_e
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model.train() # Set model to training mode
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for input_batch, target_batch in train_loader:
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optimizer.zero_grad() # Reset loss gradients from previous epoch
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optimizer.zero_grad() # Reset loss gradients from previous batch iteration
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loss = calc_loss_batch(input_batch, target_batch, model, device)
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loss.backward() # Calculate loss gradients
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optimizer.step() # Update model weights using loss gradients
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@ -1871,7 +1871,7 @@
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" model.train() # Set model to training mode\n",
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"\n",
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" for input_batch, target_batch in train_loader:\n",
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" optimizer.zero_grad() # Reset loss gradients from previous epoch\n",
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" optimizer.zero_grad() # Reset loss gradients from previous batch iteration\n",
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" loss = calc_loss_batch(input_batch, target_batch, model, device)\n",
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" loss.backward() # Calculate loss gradients\n",
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" optimizer.step() # Update model weights using loss gradients\n",
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@ -2371,7 +2371,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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"version": "3.10.6"
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}
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},
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"nbformat": 4,
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@ -201,7 +201,7 @@ def train_classifier_simple(model, train_loader, val_loader, optimizer, device,
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model.train() # Set model to training mode
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for input_batch, target_batch in train_loader:
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optimizer.zero_grad() # Reset loss gradients from previous epoch
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optimizer.zero_grad() # Reset loss gradients from previous batch iteration
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loss = calc_loss_batch(input_batch, target_batch, model, device)
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loss.backward() # Calculate loss gradients
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optimizer.step() # Update model weights using loss gradients
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@ -120,7 +120,7 @@ def train_classifier_simple(model, train_loader, val_loader, optimizer, device,
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model.train() # Set model to training mode
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for input_batch, target_batch in train_loader:
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optimizer.zero_grad() # Reset loss gradients from previous epoch
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optimizer.zero_grad() # Reset loss gradients from previous batch iteration
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loss = calc_loss_batch(input_batch, target_batch, model, device)
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loss.backward() # Calculate loss gradients
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optimizer.step() # Update model weights using loss gradients
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@ -149,7 +149,7 @@ def train_classifier_simple(model, train_loader, val_loader, optimizer, device,
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model.train() # Set model to training mode
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for input_batch, target_batch in train_loader:
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optimizer.zero_grad() # Reset loss gradients from previous epoch
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optimizer.zero_grad() # Reset loss gradients from previous batch iteration
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loss = calc_loss_batch(input_batch, target_batch, model, device, trainable_token=trainable_token)
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loss.backward() # Calculate loss gradients
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optimizer.step() # Update model weights using loss gradients
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