TensorFlow Basics (TFBS)

Welcome to the TensorFlow Basics tutorial! This guide will help you get started with TensorFlow, an open-source library for machine learning and deep learning.

Overview

TensorFlow is widely used for various applications such as image recognition, natural language processing, and time series analysis. In this tutorial, we will cover the fundamentals of TensorFlow, including:

Installation
Basic Operations
Building and Training Models
Saving and Restoring Models

Installation

Before you start, make sure you have Python installed on your system. You can download it from the official Python website.

Once Python is installed, you can install TensorFlow using pip:

pip install tensorflow

Basic Operations

TensorFlow provides a wide range of operations for performing mathematical computations. Here are some of the basic operations:

Addition: tf.add(x, y)
Subtraction: tf.subtract(x, y)
Multiplication: tf.multiply(x, y)
Division: tf.divide(x, y)

Building and Training Models

TensorFlow allows you to build and train complex models. Here's a simple example of a linear regression model:

import tensorflow as tf

# Define the model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(units=1, input_shape=[1])
])

# Compile the model
model.compile(optimizer='sgd', loss='mean_squared_error')

# Train the model
model.fit([1, 2, 3, 4, 5], [1, 2, 3, 4, 5], epochs=100)

# Make predictions
print(model.predict([6]))

Saving and Restoring Models

After training your model, you can save it for later use:

model.save('/path/to/save/model')

To restore the model, use:

restored_model = tf.keras.models.load_model('/path/to/save/model')