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  3. Introduction to TensorFlow in Python
  • 1

    Introduction to TensorFlow

    Free

    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.

  • 2

    Linear models

    In this chapter, you will learn how to build, solve, and make predictions with models in TensorFlow 2. You will focus on a simple class of models – the linear regression model – and will try to predict housing prices. By the end of the chapter, you will know how to load and manipulate data, construct loss functions, perform minimization, make predictions, and reduce resource use with batch training.

  • 3

    Neural Networks

    The previous chapters taught you how to build models in TensorFlow 2. In this chapter, you will apply those same tools to build, train, and make predictions with neural networks. You will learn how to define dense layers, apply activation functions, select an optimizer, and apply regularization to reduce overfitting. You will take advantage of TensorFlow's flexibility by using both low-level linear algebra and high-level Keras API operations to define and train models.

  • 4

    High Level APIs

    In the final chapter, you'll use high-level APIs in TensorFlow 2 to train a sign language letter classifier. You will use both the sequential and functional Keras APIs to train, validate, make predictions with, and evaluate models. You will also learn how to use the Estimators API to streamline the model definition and training process, and to avoid errors.

Initializing

Exercise

Defining Estimators

In the previous exercise, you defined a list of feature columns, feature_list, and a data input function, input_fn(). In this exercise, you will build on that work by defining an estimator that makes use of input data.

Instructions 1/2

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  • 1

    Use a deep neural network regressor with 2 nodes in both the first and second hidden layers and 1 training step.

  • 2

    Modify the code to use a LinearRegressor(), remove the hidden_units, and set the number of steps to 2.