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**Fully Connected layers** in a neural networks are those layers where all the inputs from one layer are connected to every **activation unit** of the next layer. In most popular machine learning models, the last few layers are full connected layers which **compiles the data extracted** by previous layers to form the final output. It is the second most time consuming layer second to Convolution Layer.

The diagram below clarifies the statement.

In the above model:

- The first/input layer has
**3 feature units**and there are**4 activation units**in the next hidden layer. - The 1's in each layer are
**bias units**. **a01**,**a02**and**a03**are input values to the neural network.They are basically features of the training example.- The 4 activation units of first hidden layer is connected to all 3 activation units of second hidden layer The weights/parameters connect the two layers.

So the activation units would be like this:

**Theta00**, **theta01** etc. are weights in the above picture. A conventional neural network is made up of only fully connected layers. Whereas in a Convolutional Neural Network, the last or the last few layers are fully connected layers.

#### Why are not all layers Fully connected?

### Examples of working of fully connected layers

Letâ€™s take a simple example of a Neural network made up of fully connected layers.

### Example of AND and OR boolean expression

Before moving on to the main example, let us see two small examples of neural networks computing **AND and OR boolean operation**.

Key points:

- As you can see in the graph of sigmoid function given in the image,
**g(x)**where**x>4.6**is almost equal to 1 and g(x) where x < 4.6 is almost equal to 0. - The x0(= 1) in the input is the bias unit. Every layer has a bias unit.
**h(subscript theta)**is the output value and is equal to**g(-30 + 20x1 +20x2)**in AND operation.- As you can see in the first example, the output will be 1 only if both x1 and x2 are 1.
- In the second example, output is 1 if either of the input is 1.
- The weights have been pre-adjusted accordingly in both the cases.
- In actual scenario, these weights will be â€˜learnedâ€™ by the Neural Network through
**backpropagation**.

### Example of XNOR neural network

Let us now move to the main example. We will predict **x1 XNOR x2**. The prediction should be 1 if both x1 and x2 are 1 or both of them are zero.

See the diagram below:

As you can see in the note given in the image that an XNOR boolean operation is made up of **AND, OR and NOR boolean operation**. The weights have been adjusted for all the three boolean operations.

In the table you can see that the output is 1 only if either both x1 and x2 are 1 or both are 0.

### Why are fully connected layers required?

We can divide the whole neural network (for classification) into two parts:

**Feature extraction**: In the conventional classification algorithms, like SVMs, we used to extract features from the data to make the classification work. The convolutional layers are serving the same purpose of**feature extraction**. CNNs capture better representation of data and hence we donâ€™t need to do feature engineering.**Classification**: After feature extraction we need to**classify the data into various classes**, this can be done using a fully connected (FC) neural network. In place of fully connected layers, we can also use a conventional classifier like**SVM**. But we generally end up adding FC layers to make the model end-to-end trainable. The fully connected layers learn a (possibly non-linear) function between the high-level features given as an output from the convolutional layers.

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