ValueError：在 R 中使用 keras 模型时出现在用户代码中

Question

我正在尝试使用

keras

包在 R 中运行一维 CNN。我正在使用以下代码

library(MASS)
library(keras)

##Create some data
data("Boston")
data <- Boston
# create a list of 70% of the rows in the original dataset we can use for training
set.seed(123)
training <- sample(nrow(data), 0.7 * nrow(data))

dataTrain <- data[training,]
dataTest <- data[-training,]

dataTrain_y <- as.matrix(dataTrain$medv)
dataTrain_x <- as.matrix(subset(dataTrain, select = -c(medv)))

dataTest_y <- as.matrix(dataTest$medv)
dataTest_x <- as.matrix(subset(dataTest, select = -c(medv)))

#Reshaping the data for CNN
dataTrain_x <- array_reshape(dataTrain_x, c(ncol(dataTrain_x), nrow(dataTrain_x), 1))
dataTest_x <- array_reshape(dataTest_x, c(ncol(dataTest_x), nrow(dataTest_x), 1))

#CNN model
model <- keras_model_sequential() %>%
  layer_conv_1d(filters=32, kernel_size=4, activation="relu", 
                input_shape=c(ncol(dataTrain_x), nrow(dataTrain_x))) %>%
  layer_max_pooling_1d(pool_size=2) %>%
  layer_conv_1d(filters=64, kernel_size=2, activation="relu") %>%
  layer_max_pooling_1d(pool_size=2) %>%
  layer_dropout(rate=0.4) %>%
  layer_flatten() %>%
  layer_dense(units=100, activation="relu") %>%
  layer_dropout(rate=0.2) %>%
  layer_dense(units=1, activation="linear")

model %>% compile(
  loss = "mse",
  optimizer =  "adam", #'sgd' can also be used
  metrics = list("mean_absolute_error")
)

model %>% summary()

history <- model %>% fit(dataTrain_x, dataTrain_y, 
                         epochs = 100, batch_size = 50, 
                         #callbacks = callback_tensorboard("logs/run_a"),
                         validation_split = 0.2)

但它返回给我以下错误

Error in py_call_impl(callable, dots$args, dots$keywords) : 
  ValueError: in user code:
    C:\Python37\lib\site-packages\tensorflow\python\keras\engine\training.py:805 train_function  *
        return step_function(self, iterator)
    C:\Python37\lib\site-packages\tensorflow\python\keras\engine\training.py:795 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    C:\Python37\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    C:\Python37\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    C:\Python37\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
        return fn(*args, **kwargs)
    C:\Python37\lib\site-packages\tensorflow\python\keras\engine\training.py:788 run_step  **
        outputs = model.train_step(data)
    C:\Python37\lib\site-pac 

现在我该如何解决这个错误？

Answer 1

错误消息已被截断，因此没有太大帮助，但在我看来，您有一些小错别字。如果您进行建议的更改（“###”注释），模型似乎会按预期进行编译和训练，例如

library(MASS)
library(keras)

##Create some data
data("Boston")
data <- Boston
# create a list of 70% of the rows in the original dataset we can use for training
set.seed(123)
training <- sample(nrow(data), 0.7 * nrow(data))

dataTrain <- data[training,]
dataTest <- data[-training,]

dataTrain_y <- as.matrix(dataTrain$medv)
dataTrain_x <- as.matrix(subset(dataTrain, select = -c(medv)))

dataTest_y <- as.matrix(dataTest$medv)
dataTest_x <- as.matrix(subset(dataTest, select = -c(medv)))

#Reshaping the data for CNN
### These dimensions don't look correct; switch ncol() with nrow()
dataTrain_x <- array_reshape(dataTrain_x, c(nrow(dataTrain_x), ncol(dataTrain_x), 1))
dataTest_x <- array_reshape(dataTest_x, c(nrow(dataTest_x), ncol(dataTest_x), 1))

#CNN model
model <- keras_model_sequential() %>%
  layer_conv_1d(filters=32, kernel_size=4, activation="relu", 
                ### The input shape doesn't look correct; instead of 
                ### `c(ncol(dataTrain_x), nrow(dataTrain_x))` (354, 13)
                ### I believe you want `dim(dataTest_x)` (13, 1)
                input_shape=c(ncol(dataTrain_x), 1)) %>%
  layer_max_pooling_1d(pool_size=2) %>%
  layer_conv_1d(filters=64, kernel_size=2, activation="relu") %>%
  layer_max_pooling_1d(pool_size=2) %>%
  layer_dropout(rate=0.4) %>%
  layer_flatten() %>%
  layer_dense(units=100, activation="relu") %>%
  layer_dropout(rate=0.2) %>%
  layer_dense(units=1, activation="linear")

model %>% compile(
  loss = "mse",
  optimizer =  "adam", #'sgd' can also be used
  metrics = list("mean_absolute_error")
)

model %>% summary()
#> Model: "sequential"
#> ________________________________________________________________________________
#> Layer (type)                        Output Shape                    Param #     
#> ================================================================================
#> conv1d_1 (Conv1D)                   (None, 10, 32)                  160         
#> ________________________________________________________________________________
#> max_pooling1d_1 (MaxPooling1D)      (None, 5, 32)                   0           
#> ________________________________________________________________________________
#> conv1d (Conv1D)                     (None, 4, 64)                   4160        
#> ________________________________________________________________________________
#> max_pooling1d (MaxPooling1D)        (None, 2, 64)                   0           
#> ________________________________________________________________________________
#> dropout_1 (Dropout)                 (None, 2, 64)                   0           
#> ________________________________________________________________________________
#> flatten (Flatten)                   (None, 128)                     0           
#> ________________________________________________________________________________
#> dense_1 (Dense)                     (None, 100)                     12900       
#> ________________________________________________________________________________
#> dropout (Dropout)                   (None, 100)                     0           
#> ________________________________________________________________________________
#> dense (Dense)                       (None, 1)                       101         
#> ================================================================================
#> Total params: 17,321
#> Trainable params: 17,321
#> Non-trainable params: 0
#> ________________________________________________________________________________

history <- model %>% fit(dataTrain_x, dataTrain_y, 
                         epochs = 100, batch_size = 50, 
                         #callbacks = callback_tensorboard("logs/run_a"),
                         validation_split = 0.2)

^{由 reprex 包于 2021-07-20 创建（v2.0.0）}