application_mobilenet

MobileNet model architecture.

Description

MobileNet model 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", 
  ... 
) 
 
mobilenet_preprocess_input(x) 
 
mobilenet_decode_predictions(preds, top = 5) 
 
mobilenet_load_model_hdf5(filepath)

Arguments

Arguments	Description
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.
classifier_activation	A string 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. Defaults to `'softmax'`. When loading pretrained weights, `classifier_activation` can only be `NULL` or `"softmax"`.
…	For backwards and forwards compatibility
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().

Section

Reference

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

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).