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].