larq_zoo

BinaryAlexNet

BinaryAlexNet(include_top=True,
              weights='imagenet',
              input_tensor=None,
              input_shape=None,
              classes=1000)
Instantiates the BinaryAlexNet architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

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). 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.

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.

Interactive architecture diagram

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). 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

BinaryResNetE18

BinaryResNetE18(include_top=True,
                weights='imagenet',
                input_tensor=None,
                input_shape=None,
                classes=1000)
Instantiates the BinaryResNetE 18 architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

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). 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

BinaryDenseNet28

BinaryDenseNet28(include_top=True,
                 weights='imagenet',
                 input_tensor=None,
                 input_shape=None,
                 classes=1000)
Instantiates the BinaryDenseNet 28 architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

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). 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

BinaryDenseNet37

BinaryDenseNet37(include_top=True,
                 weights='imagenet',
                 input_tensor=None,
                 input_shape=None,
                 classes=1000)
Instantiates the BinaryDenseNet 37 architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

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). 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

BinaryDenseNet37Dilated

BinaryDenseNet37Dilated(include_top=True,
                        weights='imagenet',
                        input_tensor=None,
                        input_shape=None,
                        classes=1000)
Instantiates the Dilated BinaryDenseNet 37 architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

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). 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

BinaryDenseNet45

BinaryDenseNet45(include_top=True,
                 weights='imagenet',
                 input_tensor=None,
                 input_shape=None,
                 classes=1000)
Instantiates the Binary BinaryDenseNet 45 architecture.

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

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). 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

DoReFaNet

DoReFaNet(include_top=True,
          weights='imagenet',
          input_tensor=None,
          input_shape=None,
          classes=1000)
Instantiates the DoReFa-net architecture. Optionally loads weights pre-trained on ImageNet. Interactive architecture diagram
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). 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

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.

Interactive architecture diagram

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.