createFullyConvolutionalVggModel2D: 2-D implementation of the VGG deep learning architecture...

In ANTsX/ANTsRNet: Neural Networks for Medical Image Processing

2-D implementation of the VGG deep learning architecture without classification layers.

Description

Creates a keras model of the Vgg deep learning architecture for image recognition based on the paper

Usage

createFullyConvolutionalVggModel2D(
  inputImageSize,
  layers = c(1, 2, 3, 4, 4),
  lowestResolution = 64,
  convolutionKernelSize = c(3, 3),
  poolSize = c(2, 2),
  strides = c(2, 2),
  dropoutRate = 0,
  style = 19,
  activation = "relu"
)

Arguments

inputImageSize	Used for specifying the input tensor shape. The shape (or dimension) of that tensor is the image dimensions followed by the number of channels (e.g., red, green, and blue). The batch size (i.e., number of training images) is not specified a priori.
layers	a vector determining the number of filters defined at for each layer.
lowestResolution	number of filters at the beginning.
convolutionKernelSize	2-d vector definining the kernel size during the encoding path
poolSize	2-d vector defining the region for each pooling layer.
strides	2-d vector describing the stride length in each direction.
dropoutRate	float between 0 and 1 to use between dense layers.
style	⁠'16'⁠ or ⁠'19'⁠ for VGG16 or VGG19, respectively.
activation	activation function for convolutional layer.

Details

K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition

available here:

    \url{https://arxiv.org/abs/1409.1556}

This particular implementation was influenced by the following python implementation:

    \url{https://gist.github.com/baraldilorenzo/8d096f48a1be4a2d660d}

Value

a VGG keras model

Author(s)

Tustison NJ

ANTsX/ANTsRNet documentation built on April 23, 2024, 1:24 p.m.