Instantiates the MobileNet architecture.
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
)A model instance.
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.
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 for depthwise convolution.
This is called the resolution multiplier in the MobileNet paper.
Defaults to 1.0.
Dropout rate. Defaults to 0.001.
Boolean, whether to include the fully-connected layer
at the top of the network. Defaults to TRUE.
One of NULL (random initialization), "imagenet"
(pre-training on ImageNet), or the path to the weights file
to be loaded. Defaults to "imagenet".
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 to NULL.
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.
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.
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".
The name of the model (string).
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.