application_mobilenet {keras} | R Documentation

## MobileNet model architecture.

### Description

MobileNet model architecture.

### Usage

    application_mobilenet(input_shape = NULL, alpha = 1,
      depth_multiplier = 1, dropout = 0.001, include_top = TRUE,
      weights = "imagenet", input_tensor = NULL, pooling = NULL,
      classes = 1000)

    mobilenet_preprocess_input(x)

    mobilenet_decode_predictions(preds, top = 5)

    mobilenet_load_model_hdf5(filepath)

### Arguments

`input_shape` | optional shape list, 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.
`alpha` | controls the width of the network. 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.
`depth_multiplier` | depth multiplier for depthwise convolution (also called the resolution multiplier)
`dropout` | dropout rate
`include_top` | whether to include the fully-connected layer at the top of the network.
`weights` | `NULL` (random initialization), `imagenet` (ImageNet weights), or the path to the weights file to be loaded.
`input_tensor` | optional Keras tensor (i.e. output of `layer_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.
`x` | input tensor, 4D
`preds` | Tensor encoding a batch of predictions.
`top` | integer, how many top-guesses to return.
`filepath` | File path

### Details

The `mobilenet_preprocess_input()` function should be used for image preprocessing. To load a saved instance of a MobileNet model use the `mobilenet_load_model_hdf5()` function. To prepare image input for MobileNet use `mobilenet_preprocess_input()`. To decode predictions use `mobilenet_decode_predictions()`.

### Value

`application_mobilenet()` and `mobilenet_load_model_hdf5()` return a Keras model instance. `mobilenet_preprocess_input()` returns image input suitable for feeding into a mobilenet model. `mobilenet_decode_predictions()` returns a list of data frames with variables `class_name`, `class_description`, and `score` (one data frame per sample in batch input).

### Reference

- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

[Package keras version 2.2.4 Index]