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)

## Arguments

blocks 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.

## Details

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.