Created
July 13, 2019 23:47
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Utils for binary classification in a feed forward neural net.
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| #binary cross entropy loss | |
| def cross_entropy_loss(y_pred, train_Y): | |
| num_samples = y_pred.shape[1] | |
| cost = -1 / num_samples * (np.dot(train_Y, np.log(y_pred).T) + np.dot(1 - train_Y, np.log(1 - y_pred).T)) | |
| return np.squeeze(cost) | |
| #convert probabilities to class prediction with threshold 0.5 | |
| def get_class_from_probs(probabilities): | |
| class_ = np.copy(probabilities) | |
| class_[class_ > 0.5] = 1 | |
| class_[class_ <= 0.5] = 0 | |
| return class_ | |
| #accuracy of predictions (0 to 1) | |
| def accuracy_metric(y_pred, train_Y): | |
| y_pred_class = get_class_from_probs(y_pred) | |
| return (y_pred_class == train_Y).all(axis=0).mean() |
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