Evaluating models

Two models have been trained and are available: large_model, which has many parameters; and small_model, which has fewer parameters. Both models have been trained using train_features and train_labels, which are available to you. A separate test set, which consists of test_features and test_labels, is also available.

Your goal is to evaluate relative model performance and also determine whether either model exhibits signs of overfitting. You will do this by evaluating large_model and small_model on both the train and test sets. For each model, you can do this by applying the .evaluate(x, y) method to compute the loss for features x and labels y. You will then compare the four losses generated.

This exercise is part of the course

Introduction to TensorFlow in Python

Exercise instructions

Evaluate the small model using the train data.
Evaluate the small model using the test data.
Evaluate the large model using the train data.
Evaluate the large model using the test data.

Hands-on interactive exercise

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

# Evaluate the small model using the train data
small_train = small_model.evaluate(____, ____)

# Evaluate the small model using the test data
small_test = ____

# Evaluate the large model using the train data
large_train = large_model.evaluate(____, ____)

# Evaluate the large model using the test data
large_test = ____

# Print losses
print('\n Small - Train: {}, Test: {}'.format(small_train, small_test))
print('Large - Train: {}, Test: {}'.format(large_train, large_test))

Edit and Run Code

This exercise is part of the course

Introduction to TensorFlow in Python

IntermediateSkill Level

4.8+

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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 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

Current Exercise

Exercise 10: Training models with the Estimators API Exercise 11: Preparing to train with Estimators Exercise 12: Defining Estimators Exercise 13: Congratulations!