Struct google_ml1_beta1::GoogleCloudMlV1beta1__PredictRequest

pub struct GoogleCloudMlV1beta1__PredictRequest {
    pub http_body: Option<GoogleApi__HttpBody>,
}

Request for predictions to be issued against a trained model.

The body of the request is a single JSON object with a single top-level field:

instances

A JSON array containing values representing the instances to use for prediction.

The structure of each element of the instances list is determined by your model’s input definition. Instances can include named inputs or can contain only unlabeled values.

Not all data includes named inputs. Some instances will be simple JSON values (boolean, number, or string). However, instances are often lists of simple values, or complex nested lists. Here are some examples of request bodies:

CSV data with each row encoded as a string value:

{"instances": ["1.0,true,\\"x\\"", "-2.0,false,\\"y\\""]}

Plain text:

{"instances": ["the quick brown fox", "la bruja le dio"]}

Sentences encoded as lists of words (vectors of strings):

{
  "instances": [
    ["the","quick","brown"],
    ["la","bruja","le"],
    ...
  ]
}

Floating point scalar values:

{"instances": [0.0, 1.1, 2.2]}

Vectors of integers:

{
  "instances": [
    [0, 1, 2],
    [3, 4, 5],
    ...
  ]
}

Tensors (in this case, two-dimensional tensors):

{
  "instances": [
    [
      [0, 1, 2],
      [3, 4, 5]
    ],
    ...
  ]
}

Images can be represented different ways. In this encoding scheme the first two dimensions represent the rows and columns of the image, and the third contains lists (vectors) of the R, G, and B values for each pixel.

{
  "instances": [
    [
      [
        [138, 30, 66],
        [130, 20, 56],
        ...
      ],
      [
        [126, 38, 61],
        [122, 24, 57],
        ...
      ],
      ...
    ],
    ...
  ]
}

JSON strings must be encoded as UTF-8. To send binary data, you must base64-encode the data and mark it as binary. To mark a JSON string as binary, replace it with a JSON object with a single attribute named b64:

{"b64": "..."} 

For example:

Two Serialized tf.Examples (fake data, for illustrative purposes only):

{"instances": [{"b64": "X5ad6u"}, {"b64": "IA9j4nx"}]}

Two JPEG image byte strings (fake data, for illustrative purposes only):

{"instances": [{"b64": "ASa8asdf"}, {"b64": "JLK7ljk3"}]}

If your data includes named references, format each instance as a JSON object with the named references as the keys:

JSON input data to be preprocessed:

{
  "instances": [
    {
      "a": 1.0,
      "b": true,
      "c": "x"
    },
    {
      "a": -2.0,
      "b": false,
      "c": "y"
    }
  ]
}

Some models have an underlying TensorFlow graph that accepts multiple input tensors. In this case, you should use the names of JSON name/value pairs to identify the input tensors, as shown in the following exmaples:

For a graph with input tensor aliases “tag” (string) and “image” (base64-encoded string):

{
  "instances": [
    {
      "tag": "beach",
      "image": {"b64": "ASa8asdf"}
    },
    {
      "tag": "car",
      "image": {"b64": "JLK7ljk3"}
    }
  ]
}

For a graph with input tensor aliases “tag” (string) and “image” (3-dimensional array of 8-bit ints):

{
  "instances": [
    {
      "tag": "beach",
      "image": [
        [
          [138, 30, 66],
          [130, 20, 56],
          ...
        ],
        [
          [126, 38, 61],
          [122, 24, 57],
          ...
        ],
        ...
      ]
    },
    {
      "tag": "car",
      "image": [
        [
          [255, 0, 102],
          [255, 0, 97],
          ...
        ],
        [
          [254, 1, 101],
          [254, 2, 93],
          ...
        ],
        ...
      ]
    },
    ...
  ]
}

If the call is successful, the response body will contain one prediction entry per instance in the request body. If prediction fails for any instance, the response body will contain no predictions and will contian a single error entry instead.

Activities

This type is used in activities, which are methods you may call on this type or where this type is involved in. The list links the activity name, along with information about where it is used (one of request and response).

• predict projects (request)

Fields

http_body: Option<GoogleApi__HttpBody>

Required. The prediction request body.

Trait Implementations

impl Clone for GoogleCloudMlV1beta1__PredictRequest

fn clone(&self) -> GoogleCloudMlV1beta1__PredictRequest

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for GoogleCloudMlV1beta1__PredictRequest

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for GoogleCloudMlV1beta1__PredictRequest

fn default() -> GoogleCloudMlV1beta1__PredictRequest

Returns the “default value” for a type.

impl Deserialize for GoogleCloudMlV1beta1__PredictRequest

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer,

Deserialize this value from the given Serde deserializer.

impl Serialize for GoogleCloudMlV1beta1__PredictRequest

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl RequestValue for GoogleCloudMlV1beta1__PredictRequest