11e9c7adfbf7d50dd9ef4442cf7806cdb2ee2368,samples/core/get_started/custom_estimator.py,,main,#Any#,105

Before Change

        })

    // Train the Model.
    train = (
        make_dataset(train_x, train_y)
        .repeat()
        .shuffle(1000)
        .batch(args.batch_size))
    classifier.train(input_fn=from_dataset(train), steps=args.train_steps)

    // Evaluate the model.
    test = make_dataset(test_x, test_y).batch(args.batch_size)

After Change

    predictions = classifier.predict(
        input_fn=lambda:eval_input_fn(predict_x, batch_size=args.batch_size))

    for pred_dict, expec in zip(predictions, expected):
        template = ("\nPrediction is "{}" ({:.1f}%), expected "{}"")

        class_id = pred_dict["class_ids"][0]

Instances