MobileNet model architecture.


MobileNet model architecture.


  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_decode_predictions(preds, top = 5) 


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


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().




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).