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 (331, 331, 3) for NASNetLarge and (224, 224, 3) for NASNetMobile 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. penultimate_filters Number of filters in the penultimate layer. NASNet models use the notation NASNet (N @ P), where: - N is the number of blocks - P is the number of penultimate filters num_blocks Number of repeated blocks of the NASNet model. NASNet models use the notation NASNet (N @ P), where: - N is the number of blocks - P is the number of penultimate filters 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 FALSE for CIFAR models. filter_multiplier Controls the width of the network. If filter_multiplier < 1.0, proportionally decreases the number of filters in each layer. If filter_multiplier > 1.0, proportionally increases the number of filters in each layer. - If filter_multiplier = 1, default number of filters from the paper are used at each layer. include_top Whether to include the fully-connected layer at the top of the network. weights NULL (random initialization) or imagenet (ImageNet weights) input_tensor Optional Keras tensor (i.e. output of layer_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. default_size Specifies the default image size of the model x a 4D array consists of RGB values within [0, 255].