Ex-4: Binary Classifier for MNIST

Overview

In this exercise, you will implement a binary classifier to classify a single number from the MNIST dataset. You will be required to complete the functions provided in the Python files.

Objectives

1. Define dense models with single and hidden layers.
2. Train a model to classify a single digit from the MNIST dataset.
3. Evaluate the performance of the trained model.

The Assignment

You are required to complete the following functions in 'layered_model.py'. Your completed functions should pass the tests provided.

1. define_dense_model_single_layer: Define a dense model with a single layer.
2. define_dense_model_with_hidden_layer: Define a dense model with a hidden layer.
3. fit_mnist_model_single_digit: Train the model for a single digit classification.
4. evaluate_mnist_model_single_digit: Evaluate the performance of the trained model.

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Validating and Evaluating Your Results

Online

1. After committing and pushing your code, check the mark on the top line (near the commit ID).
2. If some tests are failing, click on the ❌ to open up a popup, which will show details about the errors.
3. You can click the Details link to see what went wrong. Pay special attention to lines with the words "Failed" or "error".

4. Near the bottom of the Details page, you can see your score. Here are examples of 0/5 and 5/5:

5. When you achieve a perfect score, you will see a green checkmark near the commit ID.

Locally

1. You can test your code locally by installing and running pytest (pip install pytest or conda install pytest).
2. Run the tests using the command pytest in your terminal. This will show the status of each test and any errors that occurred.

Good luck!