fit_generator 在第一个 epoch 后没有运行

Question

我正在练习在 R 上的 Keras 中实现 RNN 和 LSTM，我首先尝试运行 Chollet 的 Deep Learning With R book 中的一些示例；因为我正在处理时间序列，所以我决定从温度示例开始：

dir.create("~/Downloads/jena_climate", recursive = TRUE)
download.file(
  "https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip",
  "~/Downloads/jena_climate/jena_climate_2009_2016.csv.zip"
)
unzip(
  "~/Downloads/jena_climate/jena_climate_2009_2016.csv.zip",
  exdir = "~/Downloads/jena_climate"
)

library(tibble)
library(readr)

data_dir <- "~/Downloads/jena_climate"
fname <- file.path(data_dir, "jena_climate_2009_2016.csv")
data <- read_csv(fname)

glimpse(data)

data <- data.matrix(data[,-1])

train_data <- data[1:200000,]
mean <- apply(train_data, 2, mean)
std <- apply(train_data, 2, sd)
data <- scale(data, center = mean, scale = std)

generator <- function(data, lookback, delay, min_index, max_index,
                      shuffle = FALSE, batch_size = 128, step = 6) {
  if (is.null(max_index))
    max_index <- nrow(data) - delay - 1
  i <- min_index + lookback
  function() {
    if (shuffle) {
      rows <- sample(c((min_index+lookback):max_index), size = batch_size)
    } else {
      if (i + batch_size >= max_index)
        i <<- min_index + lookback
      rows <- c(i:min(i+batch_size-1, max_index))
      i <<- i + length(rows)
    }
    
    samples <- array(0, dim = c(length(rows),
                                lookback / step,
                                dim(data)[[-1]]))
    targets <- array(0, dim = c(length(rows)))
    
    for (j in 1:length(rows)) {
      indices <- seq(rows[[j]] - lookback, rows[[j]]-1,
                     length.out = dim(samples)[[2]])
      samples[j,,] <- data[indices,]
      targets[[j]] <- data[rows[[j]] + delay,2]
    }           
    list(samples, targets)
  }
}

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


model <- keras_model_sequential() %>% 
  layer_flatten(input_shape = c(lookback / step, dim(data)[-1])) %>% 
  layer_dense(units = 32, activation = "relu") %>% 
  layer_dense(units = 1)

model %>% compile(
  optimizer = optimizer_rmsprop(),
  loss = "mae"
)

history <- model %>% fit_generator(
  train_gen,
  steps_per_epoch = 500,
  epochs = 20,
  validation_data = val_gen,
  validation_steps = val_steps
)

在创建模型之前我没有任何问题，但是在运行 fit_generator 函数后，处理卡在第一个 epoch：

Epoch 1/20
  1/500 [..............................] - ETA: 0s - loss: 1.2643

我已经安装了 Keras 2.3.0.0 和 tensorflow 2.2.0。你现在如何解决这个问题？

Answer 1

我在使用 Rstudio 时遇到了类似的问题，但是当我只使用 R 时，它可以工作。 你能查一下吗？