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63 lines
3.0 KiB
Python
63 lines
3.0 KiB
Python
2 years ago
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import pandas as pd
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import numpy as np
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from IPython.display import display, clear_output
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import torch
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import torch.nn as nn
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def print_results(epoch, logs):
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results = pd.DataFrame(data=np.array([v for k,v in logs.items()]).reshape(2,2),
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columns=['loss', 'accuracy'],
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index=['train','val'])
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results = results.style.set_caption(f"{epoch}")
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clear_output(wait=True)
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display(results)
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def train(X_train, y_train, X_val, y_val, model, epochs=50, lr=1e-2,verbose=False):
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X_train = torch.tensor(X_train, dtype=torch.float32).cuda()
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X_val = torch.tensor(X_val, dtype=torch.float32).cuda()
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history = {'loss': [], 'accuracy': [], 'val_loss': [], 'val_accuracy': []}
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model = torch.compile(model).cuda()
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criterion = nn.BCELoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
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train_dataset = torch.utils.data.TensorDataset(X_train, torch.tensor(y_train))
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train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=4096, shuffle=True)
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val_dataset = torch.utils.data.TensorDataset(X_val, torch.tensor(y_val))
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val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=4096, shuffle=False)
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for i in range(epochs):
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running_loss = 0.0
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running_accuracy = 0.0
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# Train
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model.train()
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for X_batch, y_batch in train_loader:
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optimizer.zero_grad()
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y_pred = model(X_batch.cuda()).cpu()
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# print(y_pred.shape, y_batch.shape)
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loss = criterion(y_pred, y_batch.float())
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loss.backward()
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optimizer.step()
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running_loss += loss.item() * X_batch.size(0)
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running_accuracy += ((y_pred > 0.5) == y_batch).sum().item()
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running_loss /= X_train.shape[0]
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running_accuracy /= X_train.shape[0]
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val_running_loss = 0.0
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val_running_accuracy = 0.0
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# Validate
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model.eval()
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with torch.no_grad():
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for X_batch, y_batch in val_loader:
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y_pred = model(X_batch.cuda()).cpu()
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loss = criterion(y_pred, y_batch.float())
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val_running_loss += loss.item() * X_batch.size(0)
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val_running_accuracy += ((y_pred > 0.5) == y_batch).sum().item()
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val_running_loss /= X_val.shape[0]
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val_running_accuracy /= X_val.shape[0]
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if verbose:
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print_results(i, {'loss': running_loss, 'accuracy': running_accuracy, 'val_loss': val_running_loss, 'val_accuracy': val_running_accuracy})
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history['loss'].append(running_loss)
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history['accuracy'].append(running_accuracy)
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history['val_loss'].append(val_running_loss)
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history['val_accuracy'].append(val_running_accuracy)
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if verbose:
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print_results(i, {'loss': running_loss, 'accuracy': running_accuracy, 'val_loss': val_running_loss, 'val_accuracy': val_running_accuracy})
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return {'loss': running_loss, 'accuracy': running_accuracy, 'val_loss': val_running_loss, 'val_accuracy': val_running_accuracy}, history
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