TensorFlow provides easy access to many common public datasets. In this lab, you will learn to load the MNIST database of handwritten digits, a common entry-level machine learning dataset, from TensorFlow Datasets. Using this data, you will build a simple model that will learn to predict numbers found in the images.
Learning Objectives
Successfully complete this lab by achieving the following learning objectives:
- Load the MNIST Dataset
Using TensorFlow Datasets, load the MNIST training and testing data into your program. Additionally, load the dataset information provided by TensorFlow.
- Explore the MNIST Dataset
- Display the dataset information provided by TensorFlow.
- Display the class label names.
- Display some example images.
- Wrangle the MNIST Dataset
- Normalize image pixel data to values between 0 and 1.
- Since the dataset is small, load all of it into memory for better performance.
- Shuffle the training data to help the model generalize, but don’t shuffle the test data.
- Batch the data to make training faster.
- Teach a Model to Predict Handwritten Digits
- Create a basic Keras Sequential deep neural network to predict the number in each image.
- Compile the model with an appropriate optimizer and loss function.
- Train the model for a few epochs using the training data.
- Evaluate the model using the test data.
- Save the model for later use.
