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Instantiates a Mobile NASNet model in ImageNet mode.

Source: R/applications.R
application_nasnet_mobile.Rd

Instantiates a Mobile NASNet model in ImageNet mode.

Usage

application_nasnet_mobile(
  input_shape = NULL,
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  name = "nasnet_mobile"
)

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) for NASNetMobile It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. (224, 224, 3) would be one valid value.

include_top

Whether to include the fully-connected layer at the top of the network.

weights

NULL (random initialization) or imagenet (ImageNet weights). For loading imagenet weights, input_shape should be (224, 224, 3)

input_tensor

Optional Keras tensor (i.e. output of keras_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 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 Keras model instance.

Reference

Optionally loads weights pre-trained on ImageNet. Note that the data format convention used by the model is the one specified in your Keras config at ~/.keras/keras.json.

Note

Each Keras Application expects a specific kind of input preprocessing. For NASNet, call application_preprocess_inputs() on your inputs before passing them to the model.

See also