Applications: Load pre-trained models

In kerasR: R Interface to the Keras Deep Learning Library

Load pre-trained models

Description

These models can be used for prediction, feature extraction, and fine-tuning. Weights are downloaded automatically when instantiating a model.

Usage

Xception(include_top = TRUE, weights = "imagenet", input_tensor = NULL,
  input_shape = NULL, pooling = NULL, classes = 1000)

VGG16(include_top = TRUE, weights = "imagenet", input_tensor = NULL,
  input_shape = NULL, pooling = NULL, classes = 1000)

VGG19(include_top = TRUE, weights = "imagenet", input_tensor = NULL,
  input_shape = NULL, pooling = NULL, classes = 1000)

ResNet50(include_top = TRUE, weights = "imagenet", input_tensor = NULL,
  input_shape = NULL, pooling = NULL, classes = 1000)

InceptionV3(include_top = TRUE, weights = "imagenet", input_tensor = NULL,
  input_shape = NULL, pooling = NULL, classes = 1000)

Arguments

include_top	whether to include the fully-connected layer at the top of the network.
weights	one of NULL (random initialization) or "imagenet" (pre-training on ImageNet).
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
pooling	optional pooling mode for feature extraction when include_top is False. None 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.

Author(s)

Taylor B. Arnold, [e-mail]

References

Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.

kerasR documentation built on Aug. 17, 2022, 5:06 p.m.