keras

keras Package

Deep learning with TensorFlow backend.

Setup

library(keras)
# install_keras()  # First time only

Sequential Model

model <- keras_model_sequential() %>%
  layer_dense(units = 64, activation = "relu", input_shape = c(10)) %>%
  layer_dropout(rate = 0.3) %>%
  layer_dense(units = 32, activation = "relu") %>%
  layer_dropout(rate = 0.3) %>%
  layer_dense(units = 1, activation = "sigmoid")

model %>% compile(
  optimizer = "adam",
  loss = "binary_crossentropy",
  metrics = c("accuracy")
)

summary(model)

Training

history <- model %>% fit(
  x_train, y_train,
  epochs = 50,
  batch_size = 32,
  validation_split = 0.2,
  callbacks = list(
    callback_early_stopping(patience = 5),
    callback_model_checkpoint("best_model.h5", save_best_only = TRUE)
  )
)

plot(history)

Evaluation

model %>% evaluate(x_test, y_test)
predictions <- model %>% predict(x_test)

CNN for Images

model <- keras_model_sequential() %>%
  layer_conv_2d(filters = 32, kernel_size = c(3, 3), activation = "relu",
    input_shape = c(28, 28, 1)) %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_conv_2d(filters = 64, kernel_size = c(3, 3), activation = "relu") %>%
  layer_max_pooling_2d(pool_size = c(2, 2)) %>%
  layer_flatten() %>%
  layer_dense(units = 64, activation = "relu") %>%
  layer_dense(units = 10, activation = "softmax")

LSTM for Sequences

model <- keras_model_sequential() %>%
  layer_lstm(units = 50, return_sequences = TRUE, input_shape = c(timesteps, features)) %>%
  layer_lstm(units = 50) %>%
  layer_dense(units = 1)

Save/Load

save_model_hdf5(model, "model.h5")
model <- load_model_hdf5("model.h5")