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
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)
Optional shape list, the input shape is by default
Number of filters in the penultimate layer.
NASNet models use the notation
Number of repeated blocks of the NASNet model. NASNet
models use the notation
Number of filters in the initial stem block
Whether to skip the reduction step at the tail end
of the network. Set to
Controls the width of the network.
Whether to include the fully-connected layer at the top of the network.
Optional Keras tensor (i.e. output of
Optional pooling mode for feature extraction when
Optional number of classes to classify images into, only to be
Specifies the default image size of the model
a 4D array consists of RGB values within