application_nasnetmobile {keras3} | R Documentation |
Instantiates a Mobile NASNet model in ImageNet mode.
Description
Instantiates a Mobile NASNet model in ImageNet mode.
Usage
application_nasnetmobile(
input_shape = NULL,
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax"
)
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 |
NULL (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 .
-
NULL 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.
|
classifier_activation |
A str or callable. The activation function to
use on the "top" layer. Ignored unless include_top=TRUE . Set
classifier_activation=NULL to return the logits of the "top"
layer. When loading pretrained weights, classifier_activation can
only be NULL or "softmax" .
|
Value
A Keras model instance.
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
.
Note
Each Keras Application expects a specific kind of input preprocessing.
For NASNet, call application_preprocess_inputs()
on your
inputs before passing them to the model.
See Also
[Package
keras3 version 1.0.0
Index]