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",
name = NULL
)
Arguments
- input_shape
Optional shape tuple, only to be specified if
include_top
isFALSE
(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 toNULL
.input_shape
will be ignored if theinput_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 to1.0
.
- 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
keras_input()
) to use as image input for the model.input_tensor
is useful for sharing inputs between multiple different networks. Defaults toNULL
.- pooling
Optional pooling mode for feature extraction when
include_top
isFALSE
.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
isTRUE
, and if noweights
argument is specified. Defaults to1000
.- classifier_activation
A
str
or callable. The activation function to use on the "top" layer. Ignored unlessinclude_top=TRUE
. Setclassifier_activation=NULL
to return the logits of the "top" layer. When loading pretrained weights,classifier_activation
can only beNULL
or"softmax"
.- name
The name of the model (string).
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
.