Building and testing the model with Monte Carlo Dropout Method
Step 1: Training a Model with Monte Carlo Dropout
The model is being trained for one epoch at a time for a total of 50 epochs. After each epoch, Monte Carlo dropout predictions are generated for the test data using the trained model, with 100 Monte Carlo samples used to estimate the average predictions and uncertainty to obtain the final prediction and computes the uncertainty as the variance of the predictions across the samples.
Python
# MONTE CARLO DROPOUTn_samples = 100def monte_carlo_dropout_predict(model, X, n_samples): predictions = [] for _ in range(n_samples): predictions.append(model(X, training=True)) predictions = np.array(predictions) mean_predictions = predictions.mean(axis=0) uncertainty = predictions.var(axis=0) return mean_predictions, uncertaintyX_test=np.array(X_test) # to convert tuple to array format |
Step 2: Evaluate the model
The code calculates the accuracy of the model’s predictions with Monte Carlo dropout by comparing the maximum of the average predictions with the true labels and taking the mean of the resulting boolean array to get the proportion of correct predictions.
Python
accuracy = np.mean(np.argmax(mean_predictions, axis=1) == y_test)print(f"Epoch {_+1}, Accuracy with Monte Carlo Dropout: {accuracy}") |
Output:
Accuracy with Monte Carlo Dropout: 0.8666666666666667
The process is repeated for 50 epochs. An accuracy of 86% with Monte Carlo Dropout suggests that the model is performing well on the given task and has correctly predicted the training samples. Clearly, Monte Carlo Dropout has more accuracy then traditional Dropout.
What is Monte Carlo (MC) dropout?
Monte Carlo Dropout was introduced in a 2016 research paper by Yarin Gal and Zoubin Ghahramani, is a technique that combines two powerful concepts in machine learning: Monte Carlo methods and dropout regularization. This innovation can be thought of as an upgrade to traditional dropout, offering the potential for significantly more accurate predictions. It is done is at time of testing . In this article, we’ll delve into the concepts and workings of Monte Carlo Dropout.
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