u_net: Creates a U-Net architecture.

In maju116/platypus: Tools for Computer Vision in R

Description

Creates a U-Net architecture.

Usage

u_net(
  net_h,
  net_w,
  grayscale,
  blocks = 4,
  n_class = 2,
  filters = 16,
  dropout = 0.1,
  batch_normalization = TRUE,
  kernel_initializer = "he_normal"
)

Arguments

net_h	Input layer height. Must be equal to '2^x, x - natural'..
net_w	Input layer width. Must be equal to '2^x, x - natural'.
grayscale	Defines input layer color channels - '1' if 'TRUE', '3' if 'FALSE'.
blocks	Number of blocks in the model.
n_class	Number of classes. Minimum is '2' (background + other object).
filters	Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
dropout	Dropout rate.
batch_normalization	Should batch normalization be used in the block.
kernel_initializer	Initializer for the kernel weights matrix.

Value

U-Net model.

maju116/platypus documentation built on Oct. 18, 2020, 9:40 a.m.