url for the model, or an io.IOHandler.
Optional loadOptions: LoadOptionsOptional tfio: __moduleExecutes inference for the model for given input tensors.
tensor, tensor array or tensor map of the inputs for the model, keyed by the input node names.
Optional outputs: string | string[]output node name from the TensorFlow model, if no outputs are specified, the default outputs of the model would be used. You can inspect intermediate nodes of the model by adding them to the outputs array.
A single tensor if provided with a single output or no outputs are provided and there is only one default output, otherwise return a tensor array. The order of the tensor array is the same as the outputs if provided, otherwise the order of outputNodes attribute of the model.
Executes inference for the model for given input tensors in async fashion, use this method when your model contains control flow ops.
tensor, tensor array or tensor map of the inputs for the model, keyed by the input node names.
Optional outputs: string | string[]output node name from the TensorFlow model, if no outputs are specified, the default outputs of the model would be used. You can inspect intermediate nodes of the model by adding them to the outputs array.
A Promise of single tensor if provided with a single output or no outputs are provided and there is only one default output, otherwise return a tensor map.
Execute the inference for the input tensors.
Inference result tensors. If the model is converted and it
originally had structured_outputs in tensorflow, then a NamedTensorMap
will be returned matching the structured_outputs. If no structured_outputs
are present, the output will be single tf.Tensor if the model has single
output node, otherwise Tensor[].
You can also feed any intermediate nodes using the NamedTensorMap as the input type. For example, given the graph InputNode => Intermediate => OutputNode, you can execute the subgraph Intermediate => OutputNode by calling model.execute('IntermediateNode' : tf.tensor(...));
This is useful for models that uses tf.dynamic_rnn, where the intermediate state needs to be fed manually.
For batch inference execution, the tensors for each input need to be concatenated together. For example with mobilenet, the required input shape is [1, 244, 244, 3], which represents the [batch, height, width, channel]. If we are provide a batched data of 100 images, the input tensor should be in the shape of [100, 244, 244, 3].
Execute the inference for the input tensors in async fashion, use this method when your model contains control flow ops.
A Promise of inference result tensors. If the model is converted
and it originally had structured_outputs in tensorflow, then a
NamedTensorMap will be returned matching the structured_outputs. If no
structured_outputs are present, the output will be single tf.Tensor if
the model has single output node, otherwise Tensor[].
You can also feed any intermediate nodes using the NamedTensorMap as the input type. For example, given the graph InputNode => Intermediate => OutputNode, you can execute the subgraph Intermediate => OutputNode by calling model.execute('IntermediateNode' : tf.tensor(...));
This is useful for models that uses tf.dynamic_rnn, where the intermediate state needs to be fed manually.
For batch inference execution, the tensors for each input need to be concatenated together. For example with mobilenet, the required input shape is [1, 244, 244, 3], which represents the [batch, height, width, channel]. If we are provide a batched data of 100 images, the input tensor should be in the shape of [100, 244, 244, 3].
Save the configuration and/or weights of the GraphModel.
An IOHandler is an object that has a save method of the proper
signature defined. The save method manages the storing or
transmission of serialized data ("artifacts") that represent the
model's topology and weights onto or via a specific medium, such as
file downloads, local storage, IndexedDB in the web browser and HTTP
requests to a server. TensorFlow.js provides IOHandler
implementations for a number of frequently used saving mediums, such as
tf.io.browserDownloads and tf.io.browserLocalStorage. See tf.io
for more details.
This method also allows you to refer to certain types of IOHandlers
as URL-like string shortcuts, such as 'localstorage://' and
'indexeddb://'.
Example 1: Save model's topology and weights to browser local
storage;
then load it back.
const modelUrl =
'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
const model = await tf.loadGraphModel(modelUrl);
const zeros = tf.zeros([1, 224, 224, 3]);
model.predict(zeros).print();
const saveResults = await model.save('localstorage://my-model-1');
const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
console.log('Prediction from loaded model:');
model.predict(zeros).print();
An instance of IOHandler or a URL-like,
scheme-based string shortcut for IOHandler.
Optional config: SaveConfigOptions for saving the model.
A Promise of SaveResult, which summarizes the result of
the saving, such as byte sizes of the saved artifacts for the model's
topology and weight values.
A
tf.GraphModelis a directed, acyclic graph built from a SavedModel GraphDef and allows inference execution.A
tf.GraphModelcan only be created by loading from a model converted from a TensorFlow SavedModel using the command line converter tool and loaded viatf.loadGraphModel.Doc