Branch-AI/one_exercise_per_file/week03/day02/ex02/readme.md

Exercise 2 Dense

The goal of this exercise is to learn to create layers of neurons. Keras proposes options to create custom layers. The neural networks build in these exercises do not require custom layers. Dense layers do the job. A dense layer is simply a layer where each unit or neuron is connected to each neuron in the next layer. As seen yesterday, there are three main types of layers: input, hidden and output. The input layer that specifies the number of inputs (features) is not represented as a layer in Keras. However, Dense has a parameter input_dim that gives the number of inputs in the previous layer. The output layer as any hidden layer can be created using Dense, the only difference is that the output layer contains one single neuron.

1. Create a Dense layer with these parameters and return the output of get_config:

   • First hidden layer connected to 5 input variables.
   • 8 neurons
   • sigmoid as activation function

2. Create a Dense layer with these parameters and return the output of get_config:

   • Hidden layer (not the first one)
   • 4 neurons
   • sigmoid as activation function

3. Create a Dense layer with these parameters and return the output of get_config:

   • Output layer
   • 1 neuron
   • sigmoid as activation function