application_densenet
Instantiates the DenseNet architecture.
Description
Instantiates the DenseNet architecture.
Usage
application_densenet(
blocks, include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000
)
application_densenet121(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000
)
application_densenet169(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000
)
application_densenet201(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000
)
densenet_preprocess_input(x, data_format = NULL)
Arguments
Arguments | Description |
---|---|
blocks | numbers of building blocks for the four dense layers. |
include_top | whether to include the fully-connected layer at the top of the network. |
weights | one of NULL (random initialization), ‘imagenet’ (pre-training on ImageNet), or the path to the weights file to be loaded. |
input_tensor | optional Keras tensor (i.e. output of layer_input() ) to use as image input for the model. |
input_shape | optional shape list, only to be specified if include_top is FALSE (otherwise the input shape has to be (224, 224, 3) (with channels_last data format) or (3, 224, 224) (with channels_first data format). It should have exactly 3 inputs channels. |
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. |
x | a 3D or 4D array consists of RGB values within [0, 255] . |
data_format | data format of the image tensor. |
Details
Optionally loads weights pre-trained on ImageNet. Note that when using TensorFlow, for best performance you should set image_data_format='channels_last'
in your Keras config at ~/.keras/keras.json. The model and the weights are compatible with TensorFlow, Theano, and CNTK. The data format convention used by the model is the one specified in your Keras config file.