application_densenet {keras} | R Documentation |
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)
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 |
input_tensor |
optional Keras tensor (i.e. output of |
input_shape |
optional shape list, only to be specified if |
pooling |
optional pooling mode for feature extraction when
|
classes |
optional number of classes to classify images into, only to be
specified if |
x |
a 3D or 4D array consists of RGB values within |
data_format |
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.