# larq_zoo.literature¶

## BinaryAlexNet¶

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

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

**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)
```

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

**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)
```

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

**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)
```

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

**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)
```

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

**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)
```

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

**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)
```

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

**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)
```

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

**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)
```

Optionally loads weights pre-trained on ImageNet.

Interactive architecture diagram

**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**