netDefinition <- "
const { T = true; F = false; }
input pixels [3, 50, 50];
hidden conv1 [48, 24, 24] rlinear from pixels convolve {
InputShape = [3, 50, 50];
KernelShape = [3, 5, 5];
Stride = [1, 2, 2];
LowerPad = [0, 1, 1];
Sharing = [T, T, T];
MapCount = 48;
}
hidden rnorm1 [48, 11, 11] from conv1 response norm {
InputShape = [48, 24, 24];
KernelShape = [1, 4, 4];
Stride = [1, 2, 2];
LowerPad = [0, 0, 0];
Alpha = 0.0001;
Beta = 0.75;
}
hidden pool1 [48, 9, 9] from rnorm1 max pool {
InputShape = [48, 11, 11];
KernelShape = [1, 3, 3];
Stride = [1, 1, 1];
}
hidden hid1 [256] rlinear from pool1 all;
hidden hid2 [256] rlinear from hid1 all;
output Class [6] from hid2 all;
"
library(RMLtools)
library(magrittr)
nn <- nnlayer_input(shape = c(3, 50, 50), name ="pixels") %>%
nnlayer_conv(
kernelshape = c(3, 5, 5),
stride = c(1, 2, 2),
sharing = c(1, 1, 1),
mapcount = 64,
name = "conv1"
) %>%
nnlayer_norm(
kernelshape = c(1, 4, 4),
stride = c(1, 2, 2),
name = "rnorm1",
alpha = 0.0001,
beta = 0.75
) %>%
nnlayer_pool(
kernelshape = c(1, 3, 3),
stride = c(1, 1, 1),
name = "pool1"
) %>%
nnlayer_full(
nodes = 256,
name = "hid1",
activation = "rlinear"
) %>%
nnlayer_full(
nodes = 256,
name = "hid2",
activation = "rlinear"
) %>%
nnlayer_output(
nodes = 6,
name = "Class"
)
print(nn)
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