Build a nn for binary classification with early stopping

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Reference no: EM133550606

Problem: We will develop an Artificial Neural Networks using MNIST digit data, where we will train an ANN model and then classify new instances. You can directly download the data using dataset currently contains 10 classes where each of the image sizes is (28 × 28). You should split the data into train and test data, where train data should be used for only training the model and test data should be kept separated and used only for evaluation purpose. You should select last two digits 0 and 4 for developing the binary classification model.

A. Build a NN for binary classification with early stopping criteria based on validation loss. Evaluate your model on the test data. Construct a confusion matrix. Present learning curve and include some examples of your prediction

B. Build three NNs for binary classification using three different weight initializers held other hyperparameters constant.

a. Construct three different confusion matrices

b. Show three different learning curves and explain any differences

c. Show accuracy using bar plots and explain - if there any difference in results for using three different initializers

Reference no: EM133550606

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