Package: tfestimators 1.9.3.9000

Overview | Dataset Preparation | Estimator Construction | Input Function | Training and Evaluation | Learning More

An Abalone Age Predictor | Setup | Downloading and Loading Abalone CSV Data | Instantiating an Estimator | Constructing the model_fn | Configuring a neural network with feature_column and layers | Defining loss for the model | Defining the training op for the model | The complete abalone model_fn | Running the Abalone Model

Getting Started | Intro to Convolutional Neural Networks | Building the CNN MNIST Classifier | Input Layer | Convolutional Layer #1 | Pooling Layer #1 | Convolutional Layer #2 and Pooling Layer #2 | Dense Layer | Logits Layer | Generate Predictions | Calculate Loss | Configure the Training Op | Add evaluation metrics | Training and Evaluating the CNN MNIST Classifier | Load Training and Test Data | Create the Estimator | Set Up a Logging Hook | Train the Model | Evaluate the Model | Run the Model

Overview | Canned Estimators | Input Functions | Feature Columns | Creating an Estimator | Training and Prediction | Model Persistence | Generic methods

Overview | Pattern Matching

Overview | Data Frame Input | Training vs. Evaluation | Matrix Input | List Input

Overview | Built-in Run Hooks | Custom Run Hooks

Overview | Examples

Overview | Example output of parsing spec | Example usage with a classifier