application_densenet function

Instantiates the DenseNet architecture.

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.

  • Maintainer: Tomasz Kalinowski
  • License: MIT + file LICENSE
  • Last published: 2024-04-20