![]() ![]() Serializes all the model configuration (all layers and weights) to a bytes buffer. Saves all the model configuration (all layers and weights) to a file on disk. Predicts the class labels for the specified inputs. Predicts a set of inputs through the model. Remove the last layer of the current model. Map the model to a Device using a target backend. Instantiate a Model from a JSON representation Instantiate a Model from a dict representation Select and prepare the optimizer for learning of the last layer.įorwards a set of inputs through the model. If the list does not start with an input layer,Īdds classes to the last layer of the model. Layers ( list, optional) – list of layers that will be copied If None, an empty sequential model will be created, or filled Parametersįilename ( str, optional) – path to the serialized Model. The Model output is an int8 our uint8 numpy array if activationsĪre enabled for the last layer, otherwise it is an int32 numpy array. If the inputs are 8-bit, then the first layer of the Model must be aĬonvolutional layer with either 1 or 3 input channels. The Model accepts only uint8 tensors as inputs, whose values areĮncoded using either 1, 2, 4 or 8-bit precision (i.e. The Model input and output shapes have 4 dimensions, the first one being It provides methods to instantiate, train, test and save models. To create a new Model from a list of layers taken from an existing To reload a full Model from a serialized file or a memory buffer, To create an empty Model to which you can add layers programmatically The Model class is the main interface to Akida and allows: Model Īn Akida neural Model, represented as a hierarchy of layers. Returns the current version of the akida module. Akida edge learning for keyword spotting.Transfer learning with AkidaNet for PlantVillage.Upgrading models with legacy quantizers.Command-line interface to evaluate model MACS.Command-line interface for model evaluation.Command-line interface for model training.Command-line interface for model creation.Advanced Mapping Details and Hardware Devices Usage.
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