What is the use of activation functions in keras ?
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# What is the use of activation functions in keras ?

This recipe explains what is the use of activation functions in keras

## Recipe Objective

Activation Functions in Keras

An activation function is a mathematical **gate** in between the input feeding the current neuron and its output going to the next layer. It can be as simple as a step function that turns the neuron output on and off, depending on a rule or threshold. Or it can be a transformation that maps the input signals into output signals that are needed for the neural network to function.

3 Types of Activation Functions 1. Binary Step Function 2. Linear Activation Function 3. Non-Linear Activation Functions

Activation Functions futher divided into sub parts that we are familiar with. 1. Sigmoid / Logistic 2. TanH / Hyperbolic Tangent 3. ReLU (Rectified Linear Unit) 4. Leaky ReLU 5. Parametric ReLU 6. Softmax 7. Swish 8. Softplus

## Step 1- Importing Libraries

``` import tensorflow as tf from tensorflow.keras import layers from tensorflow.keras import activations from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from tensorflow.keras import layers ```

## Step 2- Defining the model.

Defining the model and then define the layers, kernel initializer, and its input nodes shape.

``` #Model model = Sequential() model.add(layers.Dense(64, kernel_initializer='uniform', input_shape=(10,))) ```

## Step 3- Defining Activation function.

We will show you how to use activation functions on some models to works

``` a = tf.constant([-200, -10, 0.0, 10, 200], dtype = tf.float32) b= tf.keras.activations.relu(a).numpy() print(b)```
`[  0.   0.   0.  10. 200.]`
``` a = tf.constant([-200, -10.0, 0.0, 10.0, 200], dtype = tf.float32) b = tf.keras.activations.sigmoid(a).numpy() b```
```array([0.000000e+00, 4.539993e-05, 5.000000e-01, 9.999546e-01,
1.000000e+00], dtype=float32)```
```a = tf.constant([-200, -10.0, 0.0, 10.0, 200], dtype = tf.float32) b = tf.keras.activations.softplus(a) b.numpy() ```
```array([0.0000000e+00, 4.5398901e-05, 6.9314718e-01, 1.0000046e+01,
2.0000000e+02], dtype=float32)```
``` a = tf.constant([-200.0,-10.0, 0.0,10.0,200.0], dtype = tf.float32) b = tf.keras.activations.tanh(a) b.numpy()```
`array([-1., -1.,  0.,  1.,  1.], dtype=float32)`

We passed the same input to all the activation functions to get the different outputs. So, we can easily understand and observe the difference between all the activation functions easily.

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