# larq_zoo¶

## BinaryAlexNet¶

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

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)

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)

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)

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)

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)

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)

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)

**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**

## XNORNet¶

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

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)

**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)

**Arguments**

`image`

: Numpy array or symbolic Tensor, 3D.

**Returns**

Preprocessed Tensor or Numpy array.