Monte Carlo Dropout
=====================
In this problem, we will implement the Monte Carlo dropout method to estimate the uncertainty of a neural network's predictions. We will use a dropout layer to randomly drop out neurons during training.
**Example:** Suppose we have a simple neural network with one hidden layer and we want to estimate the uncertainty of its predictions. We can use the Monte Carlo dropout method to do this.
**Constraints:** You can assume that the data is normally distributed and that the model is a simple neural network with one hidden layer.
Test Cases
Test Case 1
Input:
[[1, 2], [3, 4], [5, 6]]Expected:
[[3.0, 0.0], [7.0, 0.0], [11.0, 0.0]]Test Case 2
Input:
[[10, 20], [30, 40], [50, 60]]Expected:
[[35.0, 0.0], [75.0, 0.0], [115.0, 0.0]]+ 3 hidden test cases