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.
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 `layer_input()`) to use as image input for the model.
input_shape	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.
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 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.
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.
x	a 3D or 4D array consists of RGB values within `[0, 255]`.
data_format	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.