# Face Recognition Using TensorFlow And Keras From Scratch

#### TensorFlow and Keras have emerged as powerful tools for building and training deep learning models. Whether you're a beginner or an experienced practitioner, understanding the fundamentals of these frameworks is crucial for developing robust and efficient neural networks. In this guide, we will explore the process of using TensorFlow and Keras from scratch, covering essential concepts, code snippets, and practical tips.

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### Introduction to TensorFlow and Keras

TensorFlow Overview

TensorFlow is an open-source machine learning framework developed by the Google Brain team. It provides a comprehensive platform for building and deploying machine learning models, with a focus on deep learning. TensorFlow supports both neural network research and production-oriented tasks.

### TensorFlow's key features include:

Graph Computation: TensorFlow models are represented as computation graphs, where nodes in the graph represent mathematical operations, and edges represent the flow of data between operations.

Automatic Differentiation: TensorFlow allows for automatic computation of gradients, crucial for training neural networks using gradient-based optimization algorithms.

TensorBoard: A visualization tool integrated with TensorFlow for monitoring and debugging models.

### Keras Overview

Keras is an open-source high-level neural networks API written in Python. Originally developed as an independent library, Keras has become an integral part of TensorFlow since version 2.0. Keras provides a user-friendly interface for building, training, and deploying deep learning models.

### Key features of Keras include:

User-Friendly API: Keras offers a simple and intuitive interface, making it accessible to both beginners and experienced developers.

Modularity: Neural networks in Keras are constructed as a series of modular building blocks, facilitating easy model design and experimentation.

Compatibility: Keras is designed to work seamlessly with TensorFlow, enabling users to leverage the strengths of both frameworks.

### Setting Up the Environment

Before diving into the code, ensure that you have TensorFlow and Keras installed. You can install them using the following commands:

bash

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pip install tensorflow

With TensorFlow installed, you automatically get access to the integrated Keras library.

### Building a Simple Neural Network

Let's start by creating a basic neural network using TensorFlow and Keras. We'll build a model to classify handwritten digits from the famous MNIST dataset.

python

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import tensorflow as tf

from tensorflow.keras import layers, models

# Load and preprocess the MNIST dataset

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

x_train, x_test = x_train / 255.0, x_test / 255.0 # Normalize pixel values to between 0 and 1

# Build the neural network model

model = models.Sequential([

layers.Flatten(input_shape=(28, 28)), # Flatten 28x28 input images to a 1D array

layers.Dense(128, activation='relu'), # Fully connected layer with 128 units and ReLU activation

layers.Dropout(0.2), # Dropout layer to prevent overfitting

layers.Dense(10, activation='softmax') # Output layer with 10 units for 10 classes and softmax activation

])

# Compile the model

model.compile(optimizer='adam',

loss='sparse_categorical_crossentropy',

metrics=['accuracy'])

# Train the model

model.fit(x_train, y_train, epochs=5, validation_data=(x_test, y_test))

# Evaluate the model on the test set

test_loss, test_acc = model.evaluate(x_test, y_test)

print(f'Test accuracy: {test_acc}')

This code does the following:

Data Loading: Loads the MNIST dataset and normalizes pixel values to the range [0, 1].

Model Definition: Defines a sequential model using Keras with a flattening layer, a dense hidden layer with ReLU activation, a dropout layer for regularization, and an output layer with softmax activation.

Model Compilation: Compiles the model by specifying the optimizer, loss function, and evaluation metric.