Based on the above code, please calculate the metric commonly used for classification problems and compare it with XGBoost.

To calculate a commonly used metric for classification problems, such as accuracy or F1 score, we need to compare the predicted labels with the true labels. In the given code, the predicted labels are stored in `y_pred`, and the true labels are stored in `y_train` (assuming `y_train` represents the training labels).

Here's an example of how to calculate accuracy and F1 score using the predicted and true labels:

import numpy as np
from sklearn.metrics import accuracy_score, f1_score

# Convert predicted probabilities to binary labels
y_pred_binary = np.round(y_pred.squeeze().detach().numpy())

# Convert true labels to numpy array
y_train_numpy = y_train.numpy()

# Calculate accuracy
accuracy = accuracy_score(y_train_numpy, y_pred_binary)

# Calculate F1 score
f1 = f1_score(y_train_numpy, y_pred_binary)

print(f"Accuracy: {accuracy}")
print(f"F1 Score: {f1}")

For comparison with XGBoost, you can train an XGBoost classifier using similar data and calculate the same metrics. Here's an example of how to use XGBoost for binary classification:

import xgboost as xgb

# Convert PyTorch tensors to numpy arrays
X_train_numpy = X_train.numpy()
y_train_numpy = y_train.numpy()

# Create DMatrix for XGBoost training
dtrain = xgb.DMatrix(X_train_numpy, label=y_train_numpy)

# Set XGBoost parameters
params = {
    'objective': 'binary:logistic',
    'eval_metric': 'error'
}

# Train the XGBoost classifier
num_rounds = 100
model_xgb = xgb.train(params, dtrain, num_rounds)

# Predict labels using XGBoost model
y_pred_xgb = model_xgb.predict(dtrain)

# Convert predicted probabilities to binary labels
y_pred_xgb_binary = np.round(y_pred_xgb)

# Calculate accuracy and F1 score
accuracy_xgb = accuracy_score(y_train_numpy, y_pred_xgb_binary)
f1_xgb = f1_score(y_train_numpy, y_pred_xgb_binary)

print(f"XGBoost Accuracy: {accuracy_xgb}")
print(f"XGBoost F1 Score: {f1_xgb}")

By comparing the accuracy and F1 score obtained from the neural network model with those obtained from the XGBoost classifier, you can assess the performance of both models on the classification task.