In AlfonsoRReyes/rDeepThought:

library(kerasR)

imdb <- load_imdb(num_words = 500, maxlen = 100)

X_train <- pad_sequences(imdb$X_train[1:4000], maxlen = 100)
Y_train <- imdb$Y_train[1:4000]
X_test <- pad_sequences(imdb$X_train[4001:5736], maxlen = 100)
Y_test <- imdb$Y_train[4001:5736]

mod <- Sequential()

mod$add(Embedding(500, 32, input_length = 100, input_shape = c(100)))
mod$add(Dropout(0.25))

mod$add(Flatten())

mod$add(Dense(256))
mod$add(Dropout(0.25))
mod$add(Activation('relu'))

mod$add(Dense(1))
mod$add(Activation('sigmoid'))

keras_compile(mod,  loss = 'binary_crossentropy', optimizer = RMSprop(lr = 0.00025))
keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 10, verbose = 1,
          validation_split = 0.1)

Y_test_hat <- keras_predict(mod, X_test)
table(Y_test, round(Y_test_hat))
mean(Y_test == as.numeric(round(Y_test_hat)))

mod <- Sequential()

mod$add(Embedding(500, 32, input_length = 100, input_shape = c(100)))
mod$add(Dropout(0.25))

mod$add(LSTM(32))

mod$add(Dense(256))
mod$add(Dropout(0.25))
mod$add(Activation('relu'))

mod$add(Dense(1))
mod$add(Activation('sigmoid'))

keras_compile(mod,  loss = 'binary_crossentropy', optimizer = RMSprop(lr = 0.00025))
keras_fit(mod, X_train, Y_train, batch_size = 32, epochs = 10, verbose = 1,
          validation_split = 0.1)

Y_test_hat <- keras_predict(mod, X_test)
mean(Y_test == as.numeric(round(Y_test_hat)))

Saving the model

The first saves the entire model, which is more than likely what most users would want, as a binary file. The second saves only the weights as a binary file; the actual model architecture would have to be created again in R. Finally, the last saves just a json description of the model. This is probably most helpful because it gives a human-readable description of your model architecture.

keras_save(mod, "full_model.h5")
keras_save_weights(mod, "weights_model.h5")
keras_model_to_json(mod, "model_architecture.json")

Read back the model

mod <- keras_load("full_model.h5")
keras_load_weights(mod, tf)
mod <- keras_model_to_json("model_architecture.json")

AlfonsoRReyes/rDeepThought documentation built on May 3, 2019, 6:42 p.m.