Ex-6: Trainable Layers in Neural Networks

Overview

In this exercise, you will learn to create and modify neural network layers.

Objectives

1. Define dense models with multiple hidden layers.
2. Set specific layers as trainable or non-trainable.

The Assignment

define_dense_model_with_hidden_layers

Modify your solution from the previous exercise for define_dense_model_with_hidden_layers. This function should accept an array of hidden layer sizes and an array of the activation functions. Keep the following points in mind when coding:

• The first layer is different because it should accept an input_layer parameter.
• The output layer should use the output_function and output_length parameters.

set_layers_to_trainable

The function set_layers_to_trainable should take a model and an array of layer numbers. It should set the layers in the array to trainable and the other layers to non-trainable.

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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!