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Larq Zoo API Documentation

BinaryAlexNet

BinaryAlexNet(include_top=True,
              weights="imagenet",
              input_tensor=None,
              input_shape=None,
              classes=1000)

Instantiates the Binary AlexNet architecture.

Optionally loads weights pre-trained on ImageNet.

Arguments

  • include_top: whether to include the fully-connected layers at the top of the network.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • input_tensor: optional Keras Tensor (i.e. output of layers.Input()) to use as image input for the model.
  • input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with channels_last data format) or (3, 224, 224) (with channels_first data format). It should have exactly 3 inputs channels.
  • classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.

Returns

A Keras model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape.

References

BiRealNet

BiRealNet(include_top=True,
          weights="imagenet",
          input_tensor=None,
          input_shape=None,
          classes=1000)

Instantiates the Bi-Real Net architecture.

Optionally loads weights pre-trained on ImageNet.

Arguments

  • include_top: whether to include the fully-connected layer at the top of the network.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • input_tensor: optional Keras Tensor (i.e. output of layers.Input()) to use as image input for the model.
  • input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with channels_last data format) or (3, 224, 224) (with channels_first data format). It should have exactly 3 inputs channels.
  • classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.

Returns

A Keras model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape.

References

XNORNet

XNORNet(include_top=True,
        weights="imagenet",
        input_tensor=None,
        input_shape=None,
        classes=1000)

Instantiates the XNOR-Net architecture.

Optionally loads weights pre-trained on ImageNet.

Arguments

  • include_top: whether to include the fully-connected layer at the top of the network.
  • weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
  • input_tensor: optional Keras Tensor (i.e. output of layers.Input()) to use as image input for the model.
  • input_shape: optional shape tuple, only to be specified if include_top is False (otherwise the input shape has to be (224, 224, 3) (with channels_last data format) or (3, 224, 224) (with channels_first data format). It should have exactly 3 inputs channels.
  • classes: optional number of classes to classify images into, only to be specified if include_top is True, and if no weights argument is specified.

Returns

A Keras model instance.

Raises

  • ValueError: in case of invalid argument for weights, or invalid input shape.

References

decode_predictions

decode_predictions(preds, top=5, **kwargs)

Decodes the prediction of an ImageNet model.

Arguments

  • preds: Numpy Tensor encoding a batch of predictions.
  • top: Integer, how many top-guesses to return.

Returns

A list of lists of top class prediction tuples (class_name, class_description, score). One list of tuples per sample in batch input.

Raises

  • ValueError: In case of invalid shape of the pred array (must be 2D).

preprocess_input

preprocess_input(image)

Preprocesses a Tensor or Numpy array encoding a image.

Arguments

  • image: Numpy array or symbolic Tensor, 3D.

Returns

Preprocessed Tensor or Numpy array.