Load and save keras models
These functions provide methods for loading and saving a keras model. As python objects, R functions such as readRDS will not work correctly. We have keras_save and keras_load to save and load the entire object, keras_save_weights and keras_load_weights to store only the weights, and keras_model_to_json and keras_model_from_json to store only the model architecture. It is also possible to use the get_weights and set_weights methods to manually extract and set weights from R objects (returned weights can be saved as an R data file).
keras_save(model, path = "model.h5") keras_load(path = "model.h5") keras_save_weights(model, path = "model.h5") keras_load_weights(model, path = "model.h5") keras_model_to_json(model, path = "model.json") keras_model_from_json(path = "model.json")
model |
keras model object to save; or, for keras_load_weights the the model in which to load the weights |
path |
local path to save or load the data from |
Taylor B. Arnold, taylor.arnold@acm.org
Chollet, Francois. 2015. Keras: Deep Learning library for Theano and TensorFlow.
Other models: Predict,
Sequential, keras_compile,
keras_fit
if (keras_available()) {
# X_train <- matrix(rnorm(100 * 10), nrow = 100)
# Y_train <- to_categorical(matrix(sample(0:2, 100, TRUE), ncol = 1), 3)
mod <- Sequential()
mod$add(Dense(units = 50, input_shape = 10))
mod$add(Dropout(rate = 0.5))
mod$add(Activation("relu"))
mod$add(Dense(units = 3))
mod$add(ActivityRegularization(l1 = 1))
mod$add(Activation("softmax"))
keras_compile(mod, loss = 'categorical_crossentropy', optimizer = RMSprop())
# keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 5,
# verbose = 0, validation_split = 0.2)
# save/load the entire model object
keras_save(mod, tf <- tempfile())
mod2 <- keras_load(tf)
# save/load just the weights file
keras_save_weights(mod, tf <- tempfile())
keras_load_weights(mod, tf)
# save/load just the architecture (as human readable json)
tf <- tempfile(fileext = ".json")
keras_model_to_json(mod, tf)
cat(readLines(tf))
mod3 <- keras_model_from_json(tf)
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