application_nasnetlarge: Instantiates a NASNet model in ImageNet mode.

In rstudio/keras: R Interface to 'Keras'

Instantiates a NASNet model in ImageNet mode.

Description

Instantiates a NASNet model in ImageNet mode.

Usage

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

Arguments

input_shape	Optional shape tuple, only to be specified if include_top is FALSE (otherwise the input shape has to be ⁠(331, 331, 3)⁠ for NASNetLarge. 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 (331, 331, 3)
input_tensor	Optional Keras tensor (i.e. output of layers.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".

Value

A Keras model instance.

Reference

• Learning Transferable Architectures for Scalable Image Recognition (CVPR 2018)

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

• https://keras.io/api/applications/nasnet#nasnetlarge-function

rstudio/keras documentation built on April 27, 2024, 10:11 p.m.