# Use the layer functions to generate individual layer specifications
nnlayer_input(c(13, 13))
nnlayer_input(c(3, 7, 7), name = "pixels")
# Convolution layers automatically compute the output size and padding
nnlayer_conv(NULL, c(2, 2),
inputshape = c(13, 13),
name = "conv1",
inputname = "pixels"
)
nnlayer_conv(NULL,
c(2, 2),
inputshape = c(13, 13),
name = "conv1",
inputname = "pixels",
stride = c(2, 2)
)
nnlayer_conv(NULL,
c(1, 2, 2),
inputshape = c(3, 13, 13),
name = "conv1",
inputname = "pixels",
stride = c(1, 2, 2)
)
nnlayer_pool(NULL,
c(1, 2, 2),
inputshape = c(3, 13, 13),
name = "conv1",
inputname = "pixels",
stride = c(1, 2, 2)
)
# Specify the number of nodes in a fully connected layer
nnlayer_full(NULL, nodes = 100, name = "h3", inputname = "conv")
# Output layer
nnlayer_output(NULL, 6, name = "class", inputname = "h3")
# using magrittr pipes to connect layers ----------------------------------
require(magrittr)
nnlayer_input(c(3, 50, 50), name = "pixels") %>%
nnlayer_conv(
kernelshape = c(1, 5, 5),
name = "conv1",
stride = c(1, 2, 3)
)
nnlayer_input(c(3, 50, 50), name = "pixels") %>%
nnlayer_conv(
kernelshape = c(1, 5, 5),
name = "conv1",
stride = c(1, 2, 3)
) %>%
nnlayer_pool(
kernelshape = c(1, 5, 5),
name = "conv1",
stride = c(1, 2, 3)
)
nnlayer_norm(NULL, inputshape = c(3, 11, 5), kernelshape = c(1,5,5), name = "rnorm1", inputname = "conv")
nnlayer_input(c(3, 50, 50), name = "pixels") %>%
nnlayer_conv(
kernelshape = c(1, 5, 5),
name = "conv1",
stride = c(1, 2, 3)
) %>%
nnlayer_norm(
kernelshape = c(1, 5, 5),
name = "norm1",
stride = c(1, 2, 3),
alpha = 0.0001,
beta = 0.75
)
nnlayer_input(c(3, 50, 50), name = "pixels") %>%
nnlayer_conv(
kernelshape = c(3, 5, 5),
name = "conv1",
stride = c(1, 2, 2),
mapcount = 48
) %>%
nnlayer_conv(
kernelshape = c(1, 4, 4),
stride = c(1, 2, 2),
name = "conv2"
) %>%
nnlayer_full(nodes = 100, name = "hid1") %>%
nnlayer_full(nodes = 30, name = "hid2") %>%
nnlayer_output(nodes = 6, name = "class")
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