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)
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.
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
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
Number of filters in the initial stem block
Whether to skip the reduction step at the tail end
of the network. Set to FALSE for CIFAR models.
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.
Whether to include the fully-connected layer at the top of the network.
NULL (random initialization) or imagenet (ImageNet weights)
Optional Keras tensor (i.e. output of layer_input())
to use as image input for the model.
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.
Optional number of classes to classify images into, only to be
specified if include_top is TRUE, and if no weights argument is
specified.
Specifies the default image size of the model
a 4D array consists of RGB values within [0, 255].