Скачать или смотреть How to Use tf.contrib.predictor for Batch Predictions with TensorFlow 1.13

Learn how to effectively predict on batches using `tf.contrib.predictor` and `tf.estimator.export_savedmodel` in TensorFlow 1.13.
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How to Use tf.contrib.predictor for Batch Predictions with TensorFlow 1.13

When working with TensorFlow, particularly in version 1.13, you may encounter a scenario where you need to make predictions using a saved estimator on batches of examples. In this guide, we’ll explore how to leverage the tf.contrib.predictor functionality for batch predictions after exporting your model using tf.estimator.export_savedmodel.

The Problem

You likely started with your model exported successfully:

[[See Video to Reveal this Text or Code Snippet]]

Then, using the following code snippet, you created a predictor:

[[See Video to Reveal this Text or Code Snippet]]

This process works perfectly for single inputs, defined as tf.train.Example when you have only one item. However, when you attempt to pass multiple inputs per feature, the operation fails, leading you to seek a solution for batch predictions.

The Solution

The solution to predicting on batches involves setting up your input data correctly and ensuring that the data types match what the predictor expects. Here’s a detailed step-by-step approach:

Step 1: Import Necessary Libraries

First, you need to import TensorFlow and any other libraries that will help us manage data, such as pandas and numpy:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Set Your Model Directory and Load Data

Define the directory where your saved model is located and load your input data from a CSV file:

[[See Video to Reveal this Text or Code Snippet]]

Step 3: Create the Predictor

Use the from_saved_model method to create a predictor instance:

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Prepare Input Data for Batch Predictions

To effectively prepare input data for batch predictions, you will need to gather the feature data in a format that the predictor can understand. This is quite essential since the feed_tensors attribute tells you the expected data formats. Here's how to do it:

[[See Video to Reveal this Text or Code Snippet]]

Step 5: Make Predictions

Now, you can call the predictor with the prepared input and retrieve your predictions:

[[See Video to Reveal this Text or Code Snippet]]

In this step, model_input contains all necessary features arranged in a batch-ready format.

Conclusion

By following these steps, you can transition from predicting individual items to handling batches seamlessly using TensorFlow 1.13's tf.contrib.predictor. As with any machine learning application, ensuring your input data aligns with model expectations is crucial for success.

Whether you’re looking to improve batch processing or simply want to understand TensorFlow’s predictive capabilities better, this approach provides a solid foundation to build upon.

Happy coding, and may your predictions always be accurate!