To Run TensorFlow in Python
- (Stanford University - Jaclyn Chen)
- Overview
TensorFlow is a popular choice for machine learning (ML) and deep learning (DL) applications because it is easy to use, has a large community of users, and is well-documented. It is also very flexible and can be used to build a wide variety of models.
Here are the prerequisites for learning the basics of using tensorflow:
- Basic Python knowledge
- Basic knowledge on how to use Jupyter Notebooks
- Basic understanding of machine learning
- Have a basic understanding of calculus, probability, statistics, and linear algebra
Here are the steps to Install and Import TensorFlow in Python:
- Step 1: Check Your Python Version. Before installing TensorFlow, you need to ensure that you have the correct version of Python installed on your system.
- Step 2: Install TensorFlow. Using pip.
- Step 3: Verify Your Installation.
- Step 4: Import TensorFlow in Your Code.
To run TensorFlow in Python, you will need to install TensorFlow and Python on your computer. You can install TensorFlow using pip or Anaconda.
Once you have installed TensorFlow, you can import it into your Python code using the following command:
import tensorflow as tf
You can then create a TensorFlow graph using the tf.Graph() function. The graph will represent the computation you want to perform.
Once you have created a graph, you can create a session to run the graph. The session will execute the graph and return the results.
- An Example
Here is an example of a TensorFlow program that creates a graph to add two numbers:
import tensorflow as tf
# Create two constants
a = tf.constant(10)
b = tf.constant(20)
# Add the two constants
c = tf.add(a, b)
# Create a session
sess = tf.Session()
# Run the graph and print the result
print(sess.run(c))
This program will print the number 30 to the console.
