Introduction to Tensorflow & Keras in Telugu by Vamsi Bhavani

TensorFlow and Keras are widely used deep learning frameworks for building and training neural networks. In this tutorial, we will explain how these two libraries work together and how you can use them to create powerful machine learning models.

1. Introduction to TensorFlow:
   TensorFlow is an open-source library developed by Google for building machine learning and deep learning models. It provides a flexible and scalable framework for training and deploying neural networks on various platforms.

TensorFlow uses a computational graph to represent the neural network model. This graph consists of nodes that represent operations and edges that represent the flow of data between these operations. Each node in the graph represents a mathematical operation, such as matrix multiplication or activation function.

1. Introduction to Keras:
   Keras is a high-level neural network library that runs on top of TensorFlow. It provides a simple and user-friendly interface for building and training deep learning models. Keras allows you to quickly prototype and experiment with different architectures without worrying about low-level details.

Keras provides a set of predefined layers that you can stack together to build a neural network. These layers include dense (fully connected), convolutional, and recurrent layers, among others. You can also define custom layers and advanced loss functions using Keras.

1. Installing TensorFlow and Keras:
   To get started with TensorFlow and Keras, you need to install these libraries on your system. You can install them using pip, which is a package manager for Python. Run the following commands in your terminal to install TensorFlow and Keras:

pip install tensorflow
pip install keras

Once you have installed these libraries, you can import them in your Python scripts using the following code:

import tensorflow as tf
from keras import models
from keras import layers

1. Building a Neural Network with Keras:
   Now, let’s build a simple neural network using Keras. We will create a feedforward neural network with three dense layers. Each layer will have a different number of neurons and will use the ReLU activation function. Here’s how you can define and compile the model in Keras:

model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(784,)))
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

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

In the above code, we create a Sequential model and add three dense layers to it. The input layer has 784 neurons (corresponding to the input shape), the hidden layers have 64 and 32 neurons, and the output layer has 10 neurons with a softmax activation function. We compile the model using the adam optimizer and sparse categorical crossentropy loss function.

1. Training the Neural Network:
   Once you have defined the model, you can train it on a dataset using the fit method in Keras. You need to provide the input data and the corresponding labels to train the model. Here’s how you can train the model on a dataset:

model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_val, y_val))

In the above code, x_train and y_train are the input data and labels for the training set, while x_val and y_val are the input data and labels for the validation set. We train the model for 10 epochs with a batch size of 32 and validate it on the validation set.

1. Evaluating the Model:
   After training the model, you can evaluate its performance on a test dataset using the evaluate method in Keras. This method returns the loss and accuracy of the model on the test set. Here’s how you can evaluate the model:

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

In the above code, x_test and y_test are the input data and labels for the test set. We evaluate the model on the test set and print the loss and accuracy metrics.

1. Conclusion:
   In this tutorial, we have explained how TensorFlow and Keras work together to build and train neural networks. We have covered the basics of building a neural network with Keras, training it on a dataset, and evaluating its performance. TensorFlow and Keras provide a powerful and flexible framework for building cutting-edge machine learning models. I hope this tutorial has helped you understand the basics of these libraries and how to use them effectively in your projects. Thank you for reading!