keras: application_mobilenet_v2 – R documentation

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keras

application_mobilenet_v2

MobileNetV2 model architecture

Description

MobileNetV2 model architecture

Usage

application_mobilenet_v2(
  input_shape = NULL,
  alpha = 1,
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  pooling = NULL,
  classes = 1000
)

mobilenet_v2_preprocess_input(x)

mobilenet_v2_decode_predictions(preds, top = 5)

mobilenet_v2_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.
`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

Value

application_mobilenet_v2() and mobilenet_v2_load_model_hdf5() return a Keras model instance. mobilenet_v2_preprocess_input() returns image input suitable for feeding into a mobilenet v2 model. mobilenet_v2_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

MobileNetV2: Inverted Residuals and Linear Bottlenecks