Prepare your input data as a JSON format text file like the following:

{"key": "1", "sepal_length": 5.9, "sepal_width": 3.0, "petal_length": 4.2, "petal_width": 1.5}
{"key": "2", "sepal_length": 6.9, "sepal_width": 3.1, "petal_length": 5.4, "petal_width": 2.1}
{"key": "3", "sepal_length": 5.1, "sepal_width": 3.3, "petal_length": 1.7, "petal_width": 0.5}

• Make each line a JSON object ({...}).
• Separate objects with line breaks.
• Gather input data for one instance into one JSON object.
• JSON objects consist of name and value pairs.
  • In each pair, the name and value are separated by a :.
  • The name is on the left of the : and the value is on the right (name: value).
• Values can be set as the following three types:
  • Numbers: 1, 23.45, and the like (numerical value, month, and day data).
  • Strings: "abc", "xyz", and the like. Strings are enclosed in " (Strings (enumerated) data).
  • Arrays: [1, 2, 3], [4, 5.6, 7.0], and the like. Arrays can contain multiple numerical values enclosed between [ and ] (Numerical value with multiple dimensions and sequence data).

    info_outline This section explains how to set sequence data.

    Sequence data, like the image below for weather data for the past 7 days, has meaning in its ordering. This section explains how to set an array for this example sequence.

    

    To use this sequence data as part of an Model Generator’s training data, you would simple write it out with commas (,) separating each number, as shown below.

    

    To use sequence data in prediction input data, prepare is as an array.

    "weather": [21, 35, 0, 20, 34, 0, . . . , 18, 32, 20]
    

    (This assumes the item name for the sequence data is weather)

• Each JSON object (an instance of input data) should include a pair with the name "key". The value should be a string to identify that instance of input data.

The results of the prediction are output as JSON format text files to a GCS folder. The files are named automatically as shown below (the XXXXX and YYYYY change based on the number of files).

prediction.results-XXXXX-of-YYYYY

• XXXXX: A number starting with 0 that represents the file’s index number (00000, 00001, etc.).
• YYYYY: The total number of files (00001, 00003, etc.).

The following example shows prediction results for the classification model:

{"label_index": 2, "score": [9.230815578575857e-08, 0.007054927293211222, 0.9929450154304504], "key": "2", "label": "Iris-virginica"}

• Each row contains one JSON object ({...}).
• Rows are separated by line breaks.
• Prediction results for each instance of input data are contained within individual JSON objects.

  "label_index"	Shows which element of the "score" array the value of "label" represents. The elements in the array are ordered starting from 0.
  "score"	The level of certainty for predicting each class. In this example, the certainty for class 0 is 0.000009231%, class 1 is 0.705492729%, and class 2 is 99.294501543%.
  "key"	The value for the "key" used in the prediction input data.
  "label"	The predicted class.

The following example shows prediction results for the regression model:

 {"output": 10304.1962890625, "key": "20170103"} 

• Each row contains one JSON object ({...}).
• Rows are separated by line breaks.
• Prediction results for each instance of input data are contained within individual JSON objects.

  "output"	The predicted value.
  "key"	The value for the "key" used in the prediction input data.

There are two ways to make predictions with the image classification or image object detection models.

• Specify image files stored in GCS
• Specify a JSON file

Each method is explained below.

prediction.results-XXXXX-of-YYYYY

{"labels": ["cat", "dog"], "score": [1.0, 2.0886015139609526e-10], "key": "gs://my-bucket/images/sample_01.jpg", "label": "cat"}
{"labels": ["cat", "dog"], "score": [3.7939051367175125e-07, 0.9999996423721313], "key": "gs://my-bucket/images/sample_02.jpg", "label": "dog"}