application_mobilenet {keras3}

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",
  name = NULL
)

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 keras_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".

name

The name of the model (string).

Value

A model instance.

Reference

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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

• https://keras.io/api/applications/mobilenet#mobilenet-function