python - 不支持将字符串转换为 int64

Question

大家好，当我在下面运行以下代码时，我遇到了“UnimplementedError:不支持将字符串转换为 int64”的问题。你们能帮我解决这个问题吗？谢谢。

import tensorflow as tf

model = tf.keras.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(15, activation=tf.nn.softmax)
    ])
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), 
metrics=['accuracy'])
X_train = tf.zeros([3000, 45, 45], tf.float32, '3d')
y_list = list()
for i in range(0,3000):
    y_list.append(str(i))
y_train = tf.convert_to_tensor(y_list, tf.string)
model.fit(X_train, y_train, epochs=3)

这是错误消息(我在 Jupyter Notebook 上运行，2.1.0 版本的 tensorflow，我实际程序中的实际 X_train 应该是用于图像的，但是我觉得这个创建的张量很好):

Train on 3000 samples
Epoch 1/3
  32/3000 [..............................] - ETA: 4:22
---------------------------------------------------------------------------
UnimplementedError                        Traceback (most recent call last)
<ipython-input-111-1a89f1d94518> in <module>
     14     y_list.append(str(i))
     15 y_train = tf.convert_to_tensor(y_list, tf.string)
---> 16 model.fit(X_train, y_train, epochs=3)

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    817         max_queue_size=max_queue_size,
    818         workers=workers,
--> 819         use_multiprocessing=use_multiprocessing)
    820 
    821   def evaluate(self,

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    340                 mode=ModeKeys.TRAIN,
    341                 training_context=training_context,
--> 342                 total_epochs=epochs)
    343             cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
    344 

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
    126         step=step, mode=mode, size=current_batch_size) as batch_logs:
    127       try:
--> 128         batch_outs = execution_function(iterator)
    129       except (StopIteration, errors.OutOfRangeError):
    130         # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
     96     # `numpy` translates Tensors to values in Eager mode.
     97     return nest.map_structure(_non_none_constant_value,
---> 98                               distributed_function(input_fn))
     99 
    100   return execution_function

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
    566         xla_context.Exit()
    567     else:
--> 568       result = self._call(*args, **kwds)
    569 
    570     if tracing_count == self._get_tracing_count():

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
    630         # Lifting succeeded, so variables are initialized and we can run the
    631         # stateless function.
--> 632         return self._stateless_fn(*args, **kwds)
    633     else:
    634       canon_args, canon_kwds = \

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\eager\function.py in __call__(self, *args, **kwargs)
   2361     with self._lock:
   2362       graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2363     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
   2364 
   2365   @property

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs)
   1609          if isinstance(t, (ops.Tensor,
   1610                            resource_variable_ops.BaseResourceVariable))),
-> 1611         self.captured_inputs)
   1612 
   1613   def _call_flat(self, args, captured_inputs, cancellation_manager=None):

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1690       # No tape is watching; skip to running the function.
   1691       return self._build_call_outputs(self._inference_function.call(
-> 1692           ctx, args, cancellation_manager=cancellation_manager))
   1693     forward_backward = self._select_forward_and_backward_functions(
   1694         args,

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager)
    543               inputs=args,
    544               attrs=("executor_type", executor_type, "config_proto", config),
--> 545               ctx=ctx)
    546         else:
    547           outputs = execute.execute_with_cancellation(

~\Anaconda3\envs\tf2\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     65     else:
     66       message = e.message
---> 67     six.raise_from(core._status_to_exception(e.code, message), None)
     68   except TypeError as e:
     69     keras_symbolic_tensors = [

~\Anaconda3\envs\tf2\lib\site-packages\six.py in raise_from(value, from_value)

UnimplementedError:  Cast string to int64 is not supported
     [[node loss/output_1_loss/Cast (defined at <ipython-input-111-1a89f1d94518>:16) ]] [Op:__inference_distributed_function_544280]

Function call stack:
distributed_function

Answer 1

原来我应该将标签设为整数，然后让这些整数对应于字符串标签。有了这个，我的整数有 0 到 13，然后是一个大小为 13 的数组，其中包含字符串值。