Question

我创建了一个Tensorflow模型，并试图使其适合我的两个训练集。

modelA <- keras_model_sequential()
modelA %>% 
  layer_dense(units = 2, activation = 'relu', input_shape = c(13)) %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 128, activation = 'relu') %>%
  layer_dropout(rate = 0.3) %>%
  layer_dense(units = 10, activation = 'softmax')

modelA %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = optimizer_rmsprop(),
  metrics = c('accuracy')
)

但是，我不断收到此错误，我也不知道它是从哪里来的。

fitting <- modelA %>% fit(
  neg_train, pos_train, 
  epochs = 30, batch_size = 128, 
  validation_split = 0.2
)

Error in py_call_impl(callable, dots$args, dots$keywords) : 
  InvalidArgumentError: indices[637] = 637 is not in [0, 637) [Op:GatherV2]

637是pos_train数据帧中的行数，但是我不知道该索引命令来自何处。有谁知道这里发生了什么或我如何解决？如果有帮助的话，这里是回溯。

25.
stop(structure(list(message = "InvalidArgumentError: indices[637] = 637 is not in [0, 637) [Op:GatherV2]", 
    call = py_call_impl(callable, dots$args, dots$keywords), 
    cppstack = structure(list(file = "", line = -1L, stack = c("1   reticulate.so                       0x00000001831af98e _ZN4Rcpp9exceptionC2EPKcb + 222", 
    "2   reticulate.so                       0x00000001831b7d05 _ZN4Rcpp4stopERKNSt3__112basic_stringIcNS0_11char_traitsIcEENS0_9allocatorIcEEEE + 53",  ... 
24.
raise_from at <string>#3
23.
raise_from_not_ok_status at ops.py#6653
22.
gather_v2 at gen_array_ops.py#3755
21.
gather at array_ops.py#4524
20.
wrapper at dispatch.py#180
19.
gather_v2 at array_ops.py#4541
18.
wrapper at dispatch.py#180
17.
_split at data_adapter.py#1335
16.
map_structure at nest.py#617
15.
train_validation_split at data_adapter.py#1338
14.
fit at training.py#797
13.
_method_wrapper at training.py#66
12.
(structure(function (...) 
{
    dots <- py_resolve_dots(list(...))
    result <- py_call_impl(callable, dots$args, dots$keywords) ... 
11.
do.call(object$fit, args) 
10.
fit.keras.engine.training.Model(., neg_train, pos_train, epochs = 30, 
    batch_size = 128, validation_split = 0.2) 
9.
fit(., neg_train, pos_train, epochs = 30, batch_size = 128, validation_split = 0.2) 
8.
function_list[[k]](value) 
7.
withVisible(function_list[[k]](value)) 
6.
freduce(value, `_function_list`) 
5.
`_fseq`(`_lhs`) 
4.
eval(quote(`_fseq`(`_lhs`)), env, env) 
3.
eval(quote(`_fseq`(`_lhs`)), env, env) 
2.
withVisible(eval(quote(`_fseq`(`_lhs`)), env, env)) 
1.
modelA %>% fit(neg_train, pos_train, epochs = 30, batch_size = 128, 
    validation_split = 0.2)

谢谢！

如何通过将数据拟合到Tensorflow模型来解决错误？

0 个答案: