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75<h1><a href="ml_v1beta1.html">Google Cloud Machine Learning</a> . <a href="ml_v1beta1.projects.html">projects</a></h1>
76<h2>Instance Methods</h2>
77<p class="toc_element">
78 <code><a href="ml_v1beta1.projects.jobs.html">jobs()</a></code>
79</p>
80<p class="firstline">Returns the jobs Resource.</p>
81
82<p class="toc_element">
83 <code><a href="ml_v1beta1.projects.models.html">models()</a></code>
84</p>
85<p class="firstline">Returns the models Resource.</p>
86
87<p class="toc_element">
88 <code><a href="ml_v1beta1.projects.operations.html">operations()</a></code>
89</p>
90<p class="firstline">Returns the operations Resource.</p>
91
92<p class="toc_element">
93 <code><a href="#getConfig">getConfig(name=None, x__xgafv=None)</a></code></p>
94<p class="firstline">Get the service account information associated with your project. You need</p>
95<p class="toc_element">
96 <code><a href="#predict">predict(name=None, body, x__xgafv=None)</a></code></p>
97<p class="firstline">Performs prediction on the data in the request.</p>
98<h3>Method Details</h3>
99<div class="method">
100 <code class="details" id="getConfig">getConfig(name=None, x__xgafv=None)</code>
101 <pre>Get the service account information associated with your project. You need
102this information in order to grant the service account persmissions for
103the Google Cloud Storage location where you put your model training code
104for training the model with Google Cloud Machine Learning.
105
106Args:
107 name: string, Required. The project name.
108
109Authorization: requires `Viewer` role on the specified project. (required)
110 x__xgafv: string, V1 error format.
111 Allowed values
112 1 - v1 error format
113 2 - v2 error format
114
115Returns:
116 An object of the form:
117
118 { # Returns service account information associated with a project.
119 "serviceAccountProject": "A String", # The project number for `service_account`.
120 "serviceAccount": "A String", # The service account Cloud ML uses to access resources in the project.
121 }</pre>
122</div>
123
124<div class="method">
125 <code class="details" id="predict">predict(name=None, body, x__xgafv=None)</code>
126 <pre>Performs prediction on the data in the request.
127
128Responses are very similar to requests. There are two top-level fields,
129each of which are JSON lists:
130
131<dl>
132 <dt>predictions</dt>
133 <dd>The list of predictions, one per instance in the request.</dd>
134 <dt>error</dt>
135 <dd>An error message returned instead of a prediction list if any
136 instance produced an error.</dd>
137</dl>
138
139If the call is successful, the response body will contain one prediction
140entry per instance in the request body. If prediction fails for any
141instance, the response body will contain no predictions and will contian
142a single error entry instead.
143
144Even though there is one prediction per instance, the format of a
145prediction is not directly related to the format of an instance.
146Predictions take whatever format is specified in the outputs collection
147defined in the model. The collection of predictions is returned in a JSON
148list. Each member of the list can be a simple value, a list, or a JSON
149object of any complexity. If your model has more than one output tensor,
150each prediction will be a JSON object containing a name/value pair for each
151output. The names identify the output aliases in the graph.
152
153The following examples show some possible responses:
154
155A simple set of predictions for three input instances, where each
156prediction is an integer value:
157<pre>
158{"predictions": [5, 4, 3]}
159</pre>
160A more complex set of predictions, each containing two named values that
161correspond to output tensors, named **label** and **scores** respectively.
162The value of **label** is the predicted category ("car" or "beach") and
163**scores** contains a list of probabilities for that instance across the
164possible categories.
165<pre>
166{"predictions": [{"label": "beach", "scores": [0.1, 0.9]},
167 {"label": "car", "scores": [0.75, 0.25]}]}
168</pre>
169A response when there is an error processing an input instance:
170<pre>
171{"error": "Divide by zero"}
172</pre>
173
174Args:
175 name: string, Required. The resource name of a model or a version.
176
177Authorization: requires `Viewer` role on the parent project. (required)
178 body: object, The request body. (required)
179 The object takes the form of:
180
181{ # Request for predictions to be issued against a trained model.
182 #
183 # The body of the request is a single JSON object with a single top-level
184 # field:
185 #
186 # <dl>
187 # <dt>instances</dt>
188 # <dd>A JSON array containing values representing the instances to use for
189 # prediction.</dd>
190 # </dl>
191 #
192 # The structure of each element of the instances list is determined by your
193 # model's input definition. Instances can include named inputs or can contain
194 # only unlabeled values.
195 #
196 # Most data does not include named inputs. Some instances will be simple
197 # JSON values (boolean, number, or string). However, instances are often lists
198 # of simple values, or complex nested lists. Here are some examples of request
199 # bodies:
200 #
201 # CSV data with each row encoded as a string value:
202 # <pre>
203 # {"instances": ["1.0,true,\\"x\\"", "-2.0,false,\\"y\\""]}
204 # </pre>
205 # Plain text:
206 # <pre>
207 # {"instances": ["the quick brown fox", "la bruja le dio"]}
208 # </pre>
209 # Sentences encoded as lists of words (vectors of strings):
210 # <pre>
211 # {"instances": [["the","quick","brown"], ["la","bruja","le"]]}
212 # </pre>
213 # Floating point scalar values:
214 # <pre>
215 # {"instances": [0.0, 1.1, 2.2]}
216 # </pre>
217 # Vectors of integers:
218 # <pre>
219 # {"instances": [[0, 1, 2], [3, 4, 5],...]}
220 # </pre>
221 # Tensors (in this case, two-dimensional tensors):
222 # <pre>
223 # {"instances": [[[0, 1, 2], [3, 4, 5]], ...]}
224 # </pre>
225 # Images represented as a three-dimensional list. In this encoding scheme the
226 # first two dimensions represent the rows and columns of the image, and the
227 # third contains the R, G, and B values for each pixel.
228 # <pre>
229 # {"instances": [[[[138, 30, 66], [130, 20, 56], ...]]]]}
230 # </pre>
231 # Data must be encoded as UTF-8. If your data uses another character encoding,
232 # you must base64 encode the data and mark it as binary. To mark a JSON string
233 # as binary, replace it with an object with a single attribute named `b`:
234 # <pre>{"b": "..."} </pre>
235 # For example:
236 #
237 # Two Serialized tf.Examples (fake data, for illustrative purposes only):
238 # <pre>
239 # {"instances": [{"b64": "X5ad6u"}, {"b64": "IA9j4nx"}]}
240 # </pre>
241 # Two JPEG image byte strings (fake data, for illustrative purposes only):
242 # <pre>
243 # {"instances": [{"b64": "ASa8asdf"}, {"b64": "JLK7ljk3"}]}
244 # </pre>
245 # If your data includes named references, format each instance as a JSON object
246 # with the named references as the keys:
247 #
248 # JSON input data to be preprocessed:
249 # <pre>
250 # {"instances": [{"a": 1.0, "b": true, "c": "x"},
251 # {"a": -2.0, "b": false, "c": "y"}]}
252 # </pre>
253 # Some models have an underlying TensorFlow graph that accepts multiple input
254 # tensors. In this case, you should use the names of JSON name/value pairs to
255 # identify the input tensors, as shown in the following exmaples:
256 #
257 # For a graph with input tensor aliases "tag" (string) and "image"
258 # (base64-encoded string):
259 # <pre>
260 # {"instances": [{"tag": "beach", "image": {"b64": "ASa8asdf"}},
261 # {"tag": "car", "image": {"b64": "JLK7ljk3"}}]}
262 # </pre>
263 # For a graph with input tensor aliases "tag" (string) and "image"
264 # (3-dimensional array of 8-bit ints):
265 # <pre>
266 # {"instances": [{"tag": "beach", "image": [[[263, 1, 10], [262, 2, 11], ...]]},
267 # {"tag": "car", "image": [[[10, 11, 24], [23, 10, 15], ...]]}]}
268 # </pre>
269 # If the call is successful, the response body will contain one prediction
270 # entry per instance in the request body. If prediction fails for any
271 # instance, the response body will contain no predictions and will contian
272 # a single error entry instead.
273 "httpBody": { # Message that represents an arbitrary HTTP body. It should only be used for #
274 # Required. The prediction request body.
275 # payload formats that can't be represented as JSON, such as raw binary or
276 # an HTML page.
277 #
278 #
279 # This message can be used both in streaming and non-streaming API methods in
280 # the request as well as the response.
281 #
282 # It can be used as a top-level request field, which is convenient if one
283 # wants to extract parameters from either the URL or HTTP template into the
284 # request fields and also want access to the raw HTTP body.
285 #
286 # Example:
287 #
288 # message GetResourceRequest {
289 # // A unique request id.
290 # string request_id = 1;
291 #
292 # // The raw HTTP body is bound to this field.
293 # google.api.HttpBody http_body = 2;
294 # }
295 #
296 # service ResourceService {
297 # rpc GetResource(GetResourceRequest) returns (google.api.HttpBody);
298 # rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty);
299 # }
300 #
301 # Example with streaming methods:
302 #
303 # service CaldavService {
304 # rpc GetCalendar(stream google.api.HttpBody)
305 # returns (stream google.api.HttpBody);
306 # rpc UpdateCalendar(stream google.api.HttpBody)
307 # returns (stream google.api.HttpBody);
308 # }
309 #
310 # Use of this type only changes how the request and response bodies are
311 # handled, all other features will continue to work unchanged.
312 "contentType": "A String", # The HTTP Content-Type string representing the content type of the body.
313 "data": "A String", # HTTP body binary data.
314 },
315 }
316
317 x__xgafv: string, V1 error format.
318 Allowed values
319 1 - v1 error format
320 2 - v2 error format
321
322Returns:
323 An object of the form:
324
325 { # Message that represents an arbitrary HTTP body. It should only be used for
326 # payload formats that can't be represented as JSON, such as raw binary or
327 # an HTML page.
328 #
329 #
330 # This message can be used both in streaming and non-streaming API methods in
331 # the request as well as the response.
332 #
333 # It can be used as a top-level request field, which is convenient if one
334 # wants to extract parameters from either the URL or HTTP template into the
335 # request fields and also want access to the raw HTTP body.
336 #
337 # Example:
338 #
339 # message GetResourceRequest {
340 # // A unique request id.
341 # string request_id = 1;
342 #
343 # // The raw HTTP body is bound to this field.
344 # google.api.HttpBody http_body = 2;
345 # }
346 #
347 # service ResourceService {
348 # rpc GetResource(GetResourceRequest) returns (google.api.HttpBody);
349 # rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty);
350 # }
351 #
352 # Example with streaming methods:
353 #
354 # service CaldavService {
355 # rpc GetCalendar(stream google.api.HttpBody)
356 # returns (stream google.api.HttpBody);
357 # rpc UpdateCalendar(stream google.api.HttpBody)
358 # returns (stream google.api.HttpBody);
359 # }
360 #
361 # Use of this type only changes how the request and response bodies are
362 # handled, all other features will continue to work unchanged.
363 "contentType": "A String", # The HTTP Content-Type string representing the content type of the body.
364 "data": "A String", # HTTP body binary data.
365 }</pre>
366</div>
367
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