application_mobilenet {keras3} | R Documentation |
Instantiates the MobileNet architecture.
Description
Instantiates the MobileNet architecture.
Usage
application_mobilenet(
input_shape = NULL,
alpha = 1,
depth_multiplier = 1L,
dropout = 0.001,
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)
(with "channels_last" data format) or (3, 224, 224)
(with "channels_first" data format).
It should have exactly 3 inputs channels, and width and
height should be no smaller than 32. E.g. (200, 200, 3) would
be one valid value. Defaults to NULL .
input_shape will be ignored if the input_tensor is provided.
|
alpha |
Controls the width of the network. This is known as the width
multiplier in the MobileNet paper.
If alpha < 1.0 , proportionally decreases the number
of filters in each layer.
If alpha > 1.0 , proportionally increases the number
of filters in each layer.
If alpha == 1 , default number of filters from the paper
are used at each layer. Defaults to 1.0 .
|
depth_multiplier |
Depth multiplier for depthwise convolution.
This is called the resolution multiplier in the MobileNet paper.
Defaults to 1.0 .
|
dropout |
Dropout rate. Defaults to 0.001 .
|
include_top |
Boolean, whether to include the fully-connected layer
at the top of the network. Defaults to TRUE .
|
weights |
One of NULL (random initialization), "imagenet"
(pre-training on ImageNet), or the path to the weights file
to be loaded. Defaults to "imagenet" .
|
input_tensor |
Optional Keras tensor (i.e. output of layers.Input() )
to use as image input for the model. input_tensor is useful
for sharing inputs between multiple different networks.
Defaults to NULL .
|
pooling |
Optional pooling mode for feature extraction when include_top
is FALSE .
-
NULL (default) means that the output of the model will be
the 4D tensor output of the last convolutional block.
-
avg means that global average pooling
will be applied to the output of the
last convolutional block, 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. Defaults to 1000 .
|
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 model instance.
Reference
This function returns a Keras image classification model,
optionally loaded with weights pre-trained on ImageNet.
For image classification use cases, see
this page for detailed examples.
For transfer learning use cases, make sure to read the
guide to transfer learning & fine-tuning.
Note
Each Keras Application expects a specific kind of input preprocessing.
For MobileNet, call application_preprocess_inputs()
on your inputs before passing them to the model.
application_preprocess_inputs()
will scale input pixels between -1
and 1
.
See Also
[Package
keras3 version 1.0.0
Index]