Instantiates a NASNet model.
Note that only TensorFlow is supported for now,
therefore it only works with the data format
image_data_format='channels_last'
in your Keras config
at ~/.keras/keras.json
.
application_nasnet(input_shape = NULL, penultimate_filters = 4032L,
num_blocks = 6L, stem_block_filters = 96L, skip_reduction = TRUE,
filter_multiplier = 2L, include_top = TRUE, weights = NULL,
input_tensor = NULL, pooling = NULL, classes = 1000,
default_size = NULL)
application_nasnetlarge(input_shape = NULL, include_top = TRUE,
weights = NULL, input_tensor = NULL, pooling = NULL,
classes = 1000)
application_nasnetmobile(input_shape = NULL, include_top = TRUE,
weights = NULL, input_tensor = NULL, pooling = NULL,
classes = 1000)
nasnet_preprocess_input(x)
Arguments
input_shape  Optional shape list, the input shape is by default 
penultimate_filters  Number of filters in the penultimate layer.
NASNet models use the notation 
num_blocks  Number of repeated blocks of the NASNet model. NASNet
models use the notation 
stem_block_filters  Number of filters in the initial stem block 
skip_reduction  Whether to skip the reduction step at the tail end
of the network. Set to 
filter_multiplier  Controls the width of the network.

include_top  Whether to include the fullyconnected layer at the top of the network. 
weights 

input_tensor  Optional Keras tensor (i.e. output of 
pooling  Optional pooling mode for feature extraction when

classes  Optional number of classes to classify images into, only to be
specified if 
default_size  Specifies the default image size of the model 
x  a 4D array consists of RGB values within 