application_densenet121 {keras3} | R Documentation |
Instantiates the Densenet121 architecture.
Description
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"
)
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 layers.Input() )
to use as image input for the model.
|
input_shape |
optional shape tuple, 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,
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 is FALSE .
-
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 is TRUE , and
if no weights argument is specified.
|
classifier_activation |
A str or callable.
The activation function to use
on the "top" layer. Ignored unless include_top=TRUE . Set
classifier_activation=NULL to return the logits
of the "top" layer. When loading pretrained weights,
classifier_activation can only be NULL or "softmax" .
|
Value
A Keras model instance.
Reference
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.
See Also
[Package
keras3 version 1.0.0
Index]