We have already read about building and compiling models in Chapter 3, Implementing a Deep Learning Model Using Keras. Let's build a simple neural network, and then we will start building the model. In this section, we will add the layers to be used in our deep learning model:

1. We will first import the important libraries from Keras:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

2. Let's design the CNN with the following code. We add our first layer as the convolution layer with a filter of 32, kernal_size set to (3,3), and ReLU as our activation function. Then, we add a convolution layer with a filter value of 64 with ReLU as our activation function. Then, we added a maxpooling layer. Finally, we drop out and flatten a dense layer with a filter size of 128 and our ReLU activation function. Finally, we add one more dropout layer:

model = tf.keras.Sequential()

model.add(tf.keras.layers.Conv2D(10, kernel_size...