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.