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- model <- keras_model_sequential() %>%
- layer_gru(units = 32,
- dropout = 0.1,
- recurrent_dropout = 0.5,
- return_sequences = TRUE,
- input_shape = list(NULL, dim(data)[[-1]])) %>%
- layer_gru(units = 64, activation = "relu",
- dropout = 0.1,
- recurrent_dropout = 0.5) %>%
- layer_dense(units = 1)
- model %>% compile(
- optimizer = optimizer_rmsprop(),
- loss = "mae"
- )
- history <- model %>% fit_generator(
- train_gen,
- steps_per_epoch = 500,
- epochs = 40,
- validation_data = val_gen,
- validation_steps = val_steps
- )
- lookback <- 1440
- step <- 6
- delay <- 144
- batch_size <- 128
- train_gen <- generator(
- data,
- lookback = lookback,
- delay = delay,
- min_index = 1,
- max_index = 200000,
- shuffle = TRUE,
- step = step,
- batch_size = batch_size
- )
- val_gen = generator(
- data,
- lookback = lookback,
- delay = delay,
- min_index = 200001,
- max_index = 300000,
- step = step,
- batch_size = batch_size
- )
- test_gen <- generator(
- data,
- lookback = lookback,
- delay = delay,
- min_index = 300001,
- max_index = NULL,
- step = step,
- batch_size = batch_size
- )
- # How many steps to draw from val_gen in order to see the entire validation set
- val_steps <- (300000 - 200001 - lookback) / batch_size
- # How many steps to draw from test_gen in order to see the entire test set
- test_steps <- (nrow(data) - 300001 - lookback) / batch_size
- m <- model %>% evaluate_generator(test_gen, steps = test_steps)
- m
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