# larq_zoo.literature¶

## BinaryAlexNet¶

BinaryAlexNet(input_shape=None,
input_tensor=None,
weights='imagenet',
include_top=True,
num_classes=1000)

Instantiates the BinaryAlexNet architecture.

Optionally loads weights pre-trained on ImageNet.

Arguments

• input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
• input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
• include_top: whether to include the fully-connected layer at the top of the network.
• num_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(input_shape=None,
input_tensor=None,
weights='imagenet',
include_top=True,
num_classes=1000)

Instantiates the Bi-Real Net architecture.

Optionally loads weights pre-trained on ImageNet.

Arguments

• input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
• input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
• include_top: whether to include the fully-connected layer at the top of the network.
• num_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(input_shape=None,
input_tensor=None,
weights='imagenet',
include_top=True,
num_classes=1000)

Instantiates the BinaryResNetE 18 architecture.

Optionally loads weights pre-trained on ImageNet.

Arguments

• input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
• input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
• include_top: whether to include the fully-connected layer at the top of the network.
• num_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(input_shape=None,
input_tensor=None,
weights='imagenet',
include_top=True,
num_classes=1000)

Instantiates the BinaryDenseNet 28 architecture.

Optionally loads weights pre-trained on ImageNet.

Arguments

• input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
• input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
• include_top: whether to include the fully-connected layer at the top of the network.
• num_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(input_shape=None,
input_tensor=None,
weights='imagenet',
include_top=True,
num_classes=1000)

Instantiates the BinaryDenseNet 37 architecture.

Optionally loads weights pre-trained on ImageNet.

Arguments

• input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
• input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
• include_top: whether to include the fully-connected layer at the top of the network.
• num_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(input_shape=None,
input_tensor=None,
weights='imagenet',
include_top=True,
num_classes=1000)

Instantiates the BinaryDenseNet 37Dilated architecture.

Optionally loads weights pre-trained on ImageNet.

Arguments

• input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
• input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
• include_top: whether to include the fully-connected layer at the top of the network.
• num_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(input_shape=None,
input_tensor=None,
weights='imagenet',
include_top=True,
num_classes=1000)

Instantiates the BinaryDenseNet 45 architecture.

Optionally loads weights pre-trained on ImageNet.

Arguments

• input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
• input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
• include_top: whether to include the fully-connected layer at the top of the network.
• num_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(input_shape=None,
input_tensor=None,
weights='imagenet',
include_top=True,
num_classes=1000)

Instantiates the DoReFa-net architecture.

Optionally loads weights pre-trained on ImageNet.

Arguments

• input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
• input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
• include_top: whether to include the fully-connected layer at the top of the network.
• num_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(input_shape=None,
input_tensor=None,
weights='imagenet',
include_top=True,
num_classes=1000)

Instantiates the XNOR-Net architecture.

Optionally loads weights pre-trained on ImageNet.

Arguments

• input_shape: Optional shape tuple, to be specified if you would like to use a model with an input image resolution that is not (224, 224, 3). It should have exactly 3 inputs channels.
• input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.
• weights: one of None (random initialization), "imagenet" (pre-training on ImageNet), or the path to the weights file to be loaded.
• include_top: whether to include the fully-connected layer at the top of the network.
• num_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