Instantiates a NASNet model in ImageNet mode.
application_nasnet_large(
input_shape = NULL,
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
name = "nasnet_large"
)A Keras model instance.
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.
Whether to include the fully-connected layer at the top of the network.
NULL (random initialization) or
imagenet (ImageNet weights). For loading imagenet weights,
input_shape should be (331, 331, 3)
Optional Keras tensor (i.e. output of
keras_input())
to use as image input for the model.
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.
Optional number of classes to classify images
into, only to be specified if include_top is TRUE, and
if no weights argument is specified.
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".
The name of the model (string).
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.