Instantiates the DenseNet architecture.
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)
numbers of building blocks for the four dense layers.
whether to include the fully-connected layer at the top of the network.
one of NULL
(random initialization), 'imagenet'
(pre-training on ImageNet), or the path to the weights file to be loaded.
optional Keras tensor (i.e. output of layer_input()
)
to use as image input for the model.
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.
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.
a 3D or 4D array consists of RGB values within [0, 255]
.
data format of the image tensor.
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.