Instantiates a NASNet model in ImageNet mode.

Usage

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

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 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

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