Instantiates the Densenet121 architecture.
Usage
application_densenet121(
include_top = TRUE,
weights = "imagenet",
input_tensor = NULL,
input_shape = NULL,
pooling = NULL,
classes = 1000L,
classifier_activation = "softmax",
name = "densenet121"
)
Arguments
- 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
keras_input()
) to use as image input for the model.- input_shape
optional shape tuple, only to be specified if
include_top
isFALSE
(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, and width and height should be no smaller than 32. E.g.(200, 200, 3)
would be one valid value.- pooling
Optional pooling mode for feature extraction when
include_top
isFALSE
.NULL
means that the output of the model will be the 4D tensor output of the last convolutional block.avg
means that global average pooling will be applied to the output of the last convolutional block, 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
isTRUE
, and if noweights
argument is specified.- classifier_activation
A
str
or callable. The activation function to use on the "top" layer. Ignored unlessinclude_top=TRUE
. Setclassifier_activation=NULL
to return the logits of the "top" layer. When loading pretrained weights,classifier_activation
can only beNULL
or"softmax"
.- name
The name of the model (string).
Reference
Densely Connected Convolutional Networks (CVPR 2017)
Optionally loads weights pre-trained on ImageNet.
Note that the data format convention used by the model is
the one specified in your Keras config at ~/.keras/keras.json
.
Note
Each Keras Application expects a specific kind of input preprocessing.
For DenseNet, call application_preprocess_inputs()
on your inputs before passing them to the model.