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Keras API reference /
Keras Applications /
NasNetLarge and NasNetMobile

`NASNetLarge`

function```
tf.keras.applications.NASNetLarge(
input_shape=None,
include_top=True,
weights="imagenet",
input_tensor=None,
pooling=None,
classes=1000,
)
```

Instantiates a NASNet model in ImageNet mode.

**Reference**

Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
the one specified in your Keras config at `~/.keras/keras.json`

.

Caution: Be sure to properly pre-process your inputs to the application.
Please see `applications.nasnet.preprocess_input`

for an example.

**Arguments**

**input_shape**: Optional shape tuple, only to be specified if`include_top`

is False (otherwise the input shape has to be`(331, 331, 3)`

for NASNetLarge. It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g.`(224, 224, 3)`

would be one valid value.**include_top**: Whether to include the fully-connected layer at the top of the network.**weights**:`None`

(random initialization) or`imagenet`

(ImageNet weights) For loading`imagenet`

weights,`input_shape`

should be (331, 331, 3)**input_tensor**: Optional Keras tensor (i.e. output of`layers.Input()`

) to use as image input for the model.**pooling**: Optional pooling mode for feature extraction when`include_top`

is`False`

. -`None`

means that the output of the model will be the 4D tensor output of the last convolutional layer. -`avg`

means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor. -`max`

means that global max pooling will be applied.**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.**RuntimeError**: If attempting to run this model with a backend that does not support separable convolutions.

`NASNetMobile`

function```
tf.keras.applications.NASNetMobile(
input_shape=None,
include_top=True,
weights="imagenet",
input_tensor=None,
pooling=None,
classes=1000,
)
```

Instantiates a Mobile NASNet model in ImageNet mode.

**Reference**

Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
the one specified in your Keras config at `~/.keras/keras.json`

.

Caution: Be sure to properly pre-process your inputs to the application.
Please see `applications.nasnet.preprocess_input`

for an example.

**Arguments**

**input_shape**: Optional shape tuple, only to be specified if`include_top`

is False (otherwise the input shape has to be`(224, 224, 3)`

for NASNetMobile It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g.`(224, 224, 3)`

would be one valid value.**include_top**: Whether to include the fully-connected layer at the top of the network.**weights**:`None`

(random initialization) or`imagenet`

(ImageNet weights) For loading`imagenet`

weights,`input_shape`

should be (224, 224, 3)**input_tensor**: Optional Keras tensor (i.e. output of`layers.Input()`

) to use as image input for the model.**pooling**: Optional pooling mode for feature extraction when`include_top`

is`False`

. -`None`

means that the output of the model will be the 4D tensor output of the last convolutional layer. -`avg`

means that global average pooling will be applied to the output of the last convolutional layer, and thus the output of the model will be a 2D tensor. -`max`

means that global max pooling will be applied.**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.**RuntimeError**: If attempting to run this model with a backend that does not support separable convolutions.