**Contents**show

Introduction

- Based on the data, draw an expected decision boundary to separate the classes.
- Express the decision boundary as a set of lines. …
- The number of selected lines represents the number of hidden neurons in the first hidden layer.
- To connect the lines created by the previous layer, a new hidden layer is added.

Hidden layer(s) are the secret sauce of your network. They allow you to model complex data thanks to their nodes/neurons. They are “hidden” because the true values of their nodes are unknown in the training dataset. In fact, we only know the input and output. Each neural network has at least one hidden layer.

There is currently no theoretical reason to use neural networks with any more than two hidden layers. In fact, for many practical problems, there is no reason to use any more than one hidden layer.

Singe-layer Perceptron. The simplest type of feedforward neural network is the perceptron, a feedforward neural network with no hidden units.

## What is 3 layer neural network?

The Neural Network is constructed from 3 type of layers: Input layer — initial data for the neural network. Hidden layers — intermediate layer between input and output layer and place where all the computation is done. Output layer — produce the result for given inputs.

Hidden layer(s) are the secret sauce of your network. They allow you to model complex data thanks to their nodes/neurons. They are “hidden” because the true values of their nodes are unknown in the training dataset. In fact, we only know the input and output. Each neural network has at least one hidden layer.

The hidden layer node values are calculated using the total summation of the input node values multiplied by their assigned weights. This process is termed “transformation.” The bias node with a weight of 1.0 is also added to the summation. The use of bias nodes is optional.

An MLP is generally restricted to having a single hidden layer. The hidden layer allows for non-linearity. A node in the hidden layer isn’t too different to an output node: nodes in the previous layers connect to it with their own weights and biases, and an output is computed, generally with an activation function.

The first Dense object is the first hidden layer. The input layer is specified as a parameter to the first Dense object’s constructor.

## How many layers a basic neural network is consist of?

This neural network is formed in three layers, called the input layer, hidden layer, and output layer. Each layer consists of one or more nodes, represented in this diagram by the small circles.

## What is epoch in neural network?

An epoch means training the neural network with all the training data for one cycle. In an epoch, we use all of the data exactly once. A forward pass and a backward pass together are counted as one pass: An epoch is made up of one or more batches, where we use a part of the dataset to train the neural network.

If you have too many hidden units, you may get low training error but still have high generalization error due to overfitting and high variance. (overfitting – A network that is not sufficiently complex can fail to detect fully the signal in a complicated data set, leading to underfitting.