Defining variables

Unlike a constant, a variable's value can be modified. This will be useful when we want to train a model by updating its parameters.

Let's try defining and printing a variable. We'll then convert the variable to a numpy array, print again, and check for differences. Note that Variable(), which is used to create a variable tensor, has been imported from tensorflow and is available to use in the exercise.

This exercise is part of the course

Introduction to TensorFlow in Python

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Exercise instructions

Define a variable, A1, as the 1-dimensional tensor: [1, 2, 3, 4].
Apply .numpy() to A1 and assign it to B1.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Define the 1-dimensional variable A1
A1 = ____([1, 2, 3, 4])

# Print the variable A1
print('\n A1: ', A1)

# Convert A1 to a numpy array and assign it to B1
B1 = ____

# Print B1
print('\n B1: ', B1)

Edit and Run Code

This exercise is part of the course

Introduction to TensorFlow in Python

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Before you can build advanced models in TensorFlow 2, you will first need to understand the basics. In this chapter, you’ll learn how to define constants and variables, perform tensor addition and multiplication, and compute derivatives. Knowledge of linear algebra will be helpful, but not necessary.

Exercise 1: Constants and variables Exercise 2: Defining data as constants Exercise 3: Defining variables

Current Exercise

Exercise 4: Basic operations Exercise 5: Performing element-wise multiplication Exercise 6: Making predictions with matrix multiplication Exercise 7: Summing over tensor dimensions Exercise 8: Advanced operations Exercise 9: Reshaping tensors Exercise 10: Optimizing with gradients Exercise 11: Working with image data

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Exercise 1: Input data Exercise 2: Load data using pandas Exercise 3: Setting the data type Exercise 4: Loss functions Exercise 5: Loss functions in TensorFlow Exercise 6: Modifying the loss function Exercise 7: Linear regression Exercise 8: Set up a linear regression Exercise 9: Train a linear model Exercise 10: Multiple linear regression Exercise 11: Batch training Exercise 12: Preparing to batch train Exercise 13: Training a linear model in batches

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Exercise 1: Dense layers Exercise 2: The linear algebra of dense layers Exercise 3: The low-level approach with multiple examples Exercise 4: Using the dense layer operation Exercise 5: Activation functions Exercise 6: Binary classification problems Exercise 7: Multiclass classification problems Exercise 8: Optimizers Exercise 9: The dangers of local minima Exercise 10: Avoiding local minima Exercise 11: Training a network in TensorFlow Exercise 12: Initialization in TensorFlow Exercise 13: Defining the model and loss function Exercise 14: Training neural networks with TensorFlow

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Exercise 1: Defining neural networks with Keras Exercise 2: The sequential model in Keras Exercise 3: Compiling a sequential model Exercise 4: Defining a multiple input model Exercise 5: Training and validation with Keras Exercise 6: Training with Keras Exercise 7: Metrics and validation with Keras Exercise 8: Overfitting detection Exercise 9: Evaluating models Exercise 10: Training models with the Estimators API Exercise 11: Preparing to train with Estimators Exercise 12: Defining Estimators Exercise 13: Congratulations!