application_inception_resnet_v2: Inception-ResNet v2 model, with weights trained on ImageNet

In dfalbel/keras: R Interface to 'Keras'

Description

Inception-ResNet v2 model, with weights trained on ImageNet

Usage

application_inception_resnet_v2(
  include_top = TRUE,
  weights = "imagenet",
  input_tensor = NULL,
  input_shape = NULL,
  pooling = NULL,
  classes = 1000
)

inception_resnet_v2_preprocess_input(x)

Arguments

include_top	whether to include the fully-connected layer at the top of the network.
weights	NULL (random initialization), imagenet (ImageNet weights), or the path to the weights file to be loaded.
input_tensor	optional Keras tensor 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 (299, 299, 3). It should have exactly 3 inputs channels, and width and height should be no smaller than 75. E.g. (150, 150, 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 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	Input tensor for preprocessing

Details

Do note that the input image format for this model is different than for the VGG16 and ResNet models (299x299 instead of 224x224).

The inception_resnet_v2_preprocess_input() function should be used for image preprocessing.

Value

A Keras model instance.

Reference

• Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning(http://arxiv.org/abs/1512.00567)

dfalbel/keras documentation built on Nov. 27, 2019, 8:16 p.m.