application_densenet169 {keras3}

Instantiates the Densenet169 architecture.

Description

Instantiates the Densenet169 architecture.

Usage

application_densenet169(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000L,
  classifier_activation = "softmax",
  name = "densenet169"
)

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 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".

name

The name of the model (string).

Value

A Keras model instance.

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.

See Also

• https://keras.io/api/applications/densenet#densenet169-function