Explaining Neural Networks Simply | Step-by-Step Deep Learning Tutorial 4 with Tensorflow2.0, Keras, and Python

Neural networks are a powerful machine learning model that mimics the way the human brain works to solve complex problems. They have become increasingly popular in recent years due to their ability to learn complex patterns from data, leading to breakthroughs in areas such as image recognition, natural language processing, and autonomous driving. In this tutorial, we will dive into the world of neural networks and explore how they can be implemented using Tensorflow 2.0, Keras, and Python.

Before we begin, it is important to have a basic understanding of machine learning and Python programming. If you are new to these topics, I recommend familiarizing yourself with the basics before continuing with this tutorial.

What is a Neural Network?

A neural network is a computational model inspired by the structure and functioning of the human brain. It is composed of interconnected nodes called neurons, each of which performs computations on incoming data. These neurons are organized into layers, with each layer responsible for a specific aspect of the computation.

There are three main types of layers in a neural network:

Input Layer: This is the first layer of the network, where the input data is fed into the network.

Hidden Layers: These are one or more layers between the input and output layers where the actual computations take place. Each neuron in a hidden layer performs a weighted sum of its inputs and applies an activation function to the result.

Output Layer: This is the final layer of the network where the output is generated. The number of neurons in the output layer depends on the type of problem being solved (e.g., binary classification, multi-class classification, regression).

Training a Neural Network

Training a neural network involves adjusting the weights and biases of the neurons in the network to minimize the difference between the predicted output and the actual output. This process is known as backpropagation, which uses the gradient descent algorithm to update the weights and biases in the network.

To train a neural network, we need a dataset that consists of input-output pairs. The input data is fed into the input layer of the network, and the output generated by the network is compared to the actual output. The difference between the predicted output and the actual output is measured using a loss function, such as mean squared error or cross-entropy.

The goal of training is to minimize the loss function by adjusting the weights and biases in the network. This is done by computing the gradients of the loss function with respect to the weights and biases using backpropagation, and then updating the weights and biases using the gradient descent algorithm.

Implementing a Neural Network in Tensorflow 2.0 and Keras

Tensorflow 2.0 is an open-source machine learning library developed by Google that provides a simple and flexible way to build and train machine learning models. Keras is a high-level neural networks API that runs on top of Tensorflow, making it easy to build and train neural networks.

To implement a neural network in Tensorflow 2.0 and Keras, follow these steps:

1. Install Tensorflow 2.0: If you haven’t already, install Tensorflow 2.0 using pip:

pip install tensorflow

1. Import the necessary libraries:

import tensorflow as tf
from tensorflow import keras

1. Load the dataset: For this tutorial, we will use the MNIST dataset, which consists of 28×28 pixel grayscale images of handwritten digits (0-9). Load the dataset using the tf.keras.datasets.mnist.load_data() function:

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

1. Preprocess the data: Normalize the input data by dividing each pixel value by 255.0, and one-hot encode the target labels:

x_train = x_train / 255.0
x_test = x_test / 255.0

y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)

1. Build the neural network model: Create a sequential model using Keras and add layers to it:

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

1. Compile the model: Compile the model by specifying the loss function, optimizer, and metrics to use during training:

model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

1. Train the model: Train the model using the fit() method and specify the number of epochs and batch size:

model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))

1. Evaluate the model: Evaluate the model on the test data using the evaluate() method:

loss, accuracy = model.evaluate(x_test, y_test)
print(f'Test Loss: {loss}, Test Accuracy: {accuracy}')

And that’s it! You have now successfully built and trained a neural network model using Tensorflow 2.0 and Keras. Experiment with different architectures, activation functions, and hyperparameters to improve the performance of your model. Neural networks are a versatile and powerful tool that can be applied to a wide range of problems, so feel free to explore and build upon what you have learned in this tutorial. Happy coding!