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

Before Change

    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)
    eval_result = classifier.evaluate(input_fn=from_dataset(test))
    print("\nTest set accuracy: {accuracy:0.3f}\n".format(**eval_result))

    // Generate predictions from the model
    predict_input = make_dataset({

After Change

    // Feature columns describe how to use the input.
    my_feature_columns = []
    for key in train_x.keys():
        my_feature_columns.append(tf.feature_column.numeric_column(key=key))

    // Build 2 hidden layer DNN with 10, 10 units respectively.
    classifier = tf.estimator.Estimator(
        model_fn=my_model,
        params={
            "feature_columns": my_feature_columns,

Instances