application_nasnet
Instantiates a NASNet model.
Description
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
.
Usage
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
Arguments | Description |
---|---|
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] . |