marshal.keras.engine.base_layer.Layer | R Documentation |
Marshalling of 'keras' objects
## S3 method for class 'keras.engine.base_layer.Layer'
marshal(model, ...)
## S3 method for class 'keras.engine.base_layer.Layer'
marshallable(...)
model |
A keras:keras.engine.base_layer.Layer object. |
... |
Not used. |
keras::serialize_model()
is used to produce a marshalled version
of the original object.
keras::unserialize_model()
is used to reconstruct a version of the
original object from the marshalled object.
A marshalled
object as described in marshal()
.
## Run only in interactive mode, because example takes > 5 seconds,
## which is longer than what is allowed on CRAN
if (interactive() && requireNamespace("keras", quietly = TRUE)) {
library(keras)
## Create a keras model (adopted from {keras} vignette)
inputs <- layer_input(shape = shape(32))
outputs <- layer_dense(inputs, units = 1L)
model <- keras_model(inputs, outputs)
model <- compile(model, optimizer = "adam", loss = "mean_squared_error")
print(model)
## Not needed anymore
rm(list = c("inputs", "outputs"))
## Marshal
model_ <- marshal(model)
## Unmarshal
model2 <- unmarshal(model_)
stopifnot(
identical(summary(model2), summary(model))
)
## Fitted keras model (adopted from {keras} vignette)
test_input <- array(runif(128 * 32), dim = c(128, 32))
test_target <- array(runif(128), dim = c(128, 1))
hist <- fit(model, test_input, test_target)
print(hist)
print(model)
## Not needed anymore
rm(list = "test_target")
## Marshal
model_ <- marshal(model)
## Unmarshal
model2 <- unmarshal(model_)
stopifnot(
identical(summary(model2), summary(model)),
identical(stats::predict(model2, test_input), stats::predict(model, test_input))
)
}
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