Tensorflow 不允许在“tf.Tensor”上迭代：AutoGraph未转换此函数。这可能表示您正试图使用不受支持的功能

我正在尝试将其改编为我的音频数据集，该数据集位于本地文件夹中。我主要更改文件contractive.py以使方法_get_ssl_task_data（）适应我的新数据库

但是，我从model.fit（下面调用我的model.train_步骤（数据）方法）触发了一个错误。我试图通过修改train_步骤中的数据形状来修复此错误，但没有成功

我不确定这是否是由于形状或数据类型不兼容造成的错误，或者是因为我需要添加更多内容以适应我的图形。有人知道我的代码出了什么问题吗？如果这真的是一个问题，那么在我的例子中如何替换tf.Tensor的使用呢

以下是contractive.py的内容：

"""Self-supervised model for contrastive learning task."""

import os

import tensorflow as tf

import constants
import data
import network

import numpy as np
import librosa
import sys, os, glob


class ContrastiveModel:
  """Provides functionality for self-supervised constrastive learning model."""

  def __init__(self,
               strategy,
               ssl_dataset_name,
               ds_dataset_name,
               model_path,
               experiment_id,
               batch_size,
               epochs, learning_rate,
               embedding_dim,
               temperature,
               similarity_type,
               pooling_type,
               noise,
               steps_per_epoch = 1000):
    """Initializes a contrastive model object."""

    self._strategy = strategy
    self._ssl_dataset_name = ssl_dataset_name
    self._ds_dataset_name = ds_dataset_name
    self._model_path = model_path
    self._experiment_id = experiment_id

    self._batch_size = batch_size
    self._epochs = epochs
    self._learning_rate = learning_rate
    self._temperature = temperature
    self._embedding_dim = embedding_dim
    self._similarity_type = similarity_type
    self._pooling_type = pooling_type
    self._noise = noise

    self._steps_per_epoch = steps_per_epoch
    self._shuffle_buffer = 1000
    self._n_frames = None
    self._n_bands = 64
    self._n_channels = 1
    self._input_shape = (-1, self._n_frames, self._n_bands, self._n_channels)

  def _prepare_example(self, example):
    #Creates an example (anchor-positive) for instance discrimination.
    example = tf.cast(example, tf.float32) / float(tf.int16.max)
    x = tf.math.l2_normalize(example, epsilon=1e-9)

    waveform_a = data.extract_window(x)
    mels_a = data.extract_log_mel_spectrogram(waveform_a)
    frames_anchors = mels_a[Ellipsis, tf.newaxis]

    waveform_p = data.extract_window(x)
    waveform_p = waveform_p + (
        self._noise * tf.random.normal(tf.shape(waveform_p)))
    mels_p = data.extract_log_mel_spectrogram(waveform_p)
    frames_positives = mels_p[Ellipsis, tf.newaxis]

    return frames_anchors, frames_positives

  #my own added method to create dataset
  def file_load(self, wav_name, mono=False):
    try:
        return librosa.load(wav_name, sr=None, mono=mono)
    except:
        logger.error("file_broken or not exists!! : {}".format(wav_name))

  #my own added method to create dataset
  def make_data(self, folder_name):
    all_name = glob.glob(folder_name)
    files = []
    for name in all_name:
        files.append(self.file_load(name)[0])
    #return tf.map_fn(lambda file: _prepare_example(file), np.array(files))
    files = np.array(files, dtype=object)
    r = tf.TensorArray(tf.float32, 0, dynamic_size=True)
    for file in files:
        r = r.write(r.size(), self._prepare_example(file))
        #r = r.write(r.size(), file)
    return r.stack()

  #my adapted method to create dataset
  def _get_ssl_task_data(self):
    #Prepares a dataset for contrastive self-supervised task.
    data_dir='path/to/my/audio/folder'
    dataset = self.make_data(data_dir + "/*")
    dataset = tf.data.Dataset.from_tensor_slices(dataset)
    ds = dataset.repeat()
    ds = ds.shuffle(self._shuffle_buffer, reshuffle_each_iteration=True)
    #ds = ds.map(self._prepare_example, num_parallel_calls=tf.data.experimental.AUTOTUNE)
    ds = ds.batch(self._batch_size, drop_remainder=True)
    ds = ds.prefetch(tf.data.experimental.AUTOTUNE)
    return ds

  def train(self):
    """Trains a self-supervised model for contrastive learning."""

    train_dataset = self._get_ssl_task_data()
    train_dataset = self._strategy.experimental_distribute_dataset(train_dataset)

    with self._strategy.scope():
      contrastive_network = network.get_contrastive_network(
          embedding_dim=self._embedding_dim,
          temperature=self._temperature,
          pooling_type=self._pooling_type,
          similarity_type=self._similarity_type)
      contrastive_network.compile(
          optimizer=tf.keras.optimizers.Adam(self._learning_rate),
          loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
          metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])

    ssl_model_dir = f"{self._ssl_dataset_name.value}/{self._experiment_id}/"
    ckpt_path = os.path.join(self._model_path, ssl_model_dir, "ckpt_{epoch}")
    model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
        filepath=ckpt_path, save_weights_only=True, monitor="loss")

    backup_path = os.path.join(self._model_path, ssl_model_dir, "backup")
    backandrestore_callback = tf.keras.callbacks.experimental.BackupAndRestore(
        backup_dir=backup_path)

    log_dir = os.path.join(self._model_path, "log", self._experiment_id)
    tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir)

    contrastive_network.fit(
        train_dataset,
        epochs=self._epochs,
        batch_size=self._batch_size,#added
        steps_per_epoch=self._steps_per_epoch,
        verbose=2,
        callbacks=[
            model_checkpoint_callback,
            backandrestore_callback,
            tensorboard_callback,
        ])

下面是对比模型的代码：

class ContrastiveModel(tf.keras.Model):
  """Wrapper class for custom contrastive model."""

  def __init__(self, embedding_model, temperature, similarity_layer,
               similarity_type):
    super().__init__()
    self.embedding_model = embedding_model
    self._temperature = temperature
    self._similarity_layer = similarity_layer
    self._similarity_type = similarity_type

  def train_step(self, data):
    #tried to modify data shape but in vain
    # shape of received data from input is [batch_size, 2, 98, 64, 1]
    #data = tf.transpose(data, [1, 0, 2, 3, 4]) #failed attempt, with same error
    anchors, positives = data # This one alone doesn't work either

    with tf.GradientTape() as tape:
      inputs = tf.concat([anchors, positives], axis=0)
      embeddings = self.embedding_model(inputs, training=True)
      anchor_embeddings, positive_embeddings = tf.split(embeddings, 2, axis=0)

      # logits
      similarities = self._similarity_layer(anchor_embeddings,
                                            positive_embeddings)

      if self._similarity_type == constants.SimilarityMeasure.DOT:
        similarities /= self._temperature
      sparse_labels = tf.range(tf.shape(anchors)[0])

      loss = self.compiled_loss(sparse_labels, similarities)
      loss += sum(self.losses)

    trainable_vars = self.trainable_variables
    gradients = tape.gradient(loss, trainable_vars)
    self.optimizer.apply_gradients(zip(gradients, trainable_vars))
    self.compiled_metrics.update_state(sparse_labels, similarities)
    return {m.name: m.result() for m in self.metrics}


def get_efficient_net_encoder(input_shape, pooling):
  """Wrapper function for efficient net B0."""
  efficient_net = tf.keras.applications.EfficientNetB0(
      include_top=False, weights=None, input_shape=input_shape, pooling=pooling)
  # To set the name `encoder` as it is used by supervised module for
  # to trainable value.
  return tf.keras.Model(efficient_net.inputs, efficient_net.outputs, name="encoder")


def get_contrastive_network(embedding_dim,
                            temperature,
                            pooling_type="max",
                            similarity_type=constants.SimilarityMeasure.DOT,
                            input_shape=(None, 64, 1)):
  """Creates a model for contrastive learning task."""
  inputs = tf.keras.layers.Input(input_shape)
  encoder = get_efficient_net_encoder(input_shape, pooling_type)
  x = encoder(inputs)
  outputs = tf.keras.layers.Dense(embedding_dim, activation="linear")(x)
  if similarity_type == constants.SimilarityMeasure.BILINEAR:
    outputs = tf.keras.layers.LayerNormalization()(outputs)
    outputs = tf.keras.layers.Activation("tanh")(outputs)
  embedding_model = tf.keras.Model(inputs, outputs)
  if similarity_type == constants.SimilarityMeasure.BILINEAR:
    embedding_dim = embedding_model.output.shape[-1]
    similarity_layer = BilinearProduct(embedding_dim)
  else:
    similarity_layer = DotProduct()
  return ContrastiveModel(embedding_model, temperature, similarity_layer,
                          similarity_type)

当我运行上面代码中strategy=tf.distribute.MirroredStrategy（）分发的代码时，我得到以下完整错误：

INFO:tensorflow:Error reported to Coordinator: iterating over `tf.Tensor` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature.
Traceback (most recent call last):
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/training/coordinator.py", line 297, in stop_on_exception
    yield
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/distribute/mirrored_run.py", line 323, in run
    self.main_result = self.main_fn(*self.main_args, **self.main_kwargs)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py", line 667, in wrapper
    return converted_call(f, args, kwargs, options=options)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py", line 396, in converted_call
    return _call_unconverted(f, args, kwargs, options)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py", line 478, in _call_unconverted
    return f(*args, **kwargs)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 820, in run_step
    outputs = model.train_step(data)
  File "/lfs/eq/tim/project_sept2020/cola/network.py", line 66, in train_step
    anchors, positives = data
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 505, in __iter__
    self._disallow_iteration()
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 498, in _disallow_iteration
    self._disallow_when_autograph_enabled("iterating over `tf.Tensor`")
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 474, in _disallow_when_autograph_enabled
    raise errors.OperatorNotAllowedInGraphError(
tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: iterating over `tf.Tensor` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature.
I1129 04:56:14.756884 140640742516480 coordinator.py:217] Error reported to Coordinator: iterating over `tf.Tensor` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature.
Traceback (most recent call last):
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/training/coordinator.py", line 297, in stop_on_exception
    yield
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/distribute/mirrored_run.py", line 323, in run
    self.main_result = self.main_fn(*self.main_args, **self.main_kwargs)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py", line 667, in wrapper
    return converted_call(f, args, kwargs, options=options)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py", line 396, in converted_call
    return _call_unconverted(f, args, kwargs, options)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/autograph/impl/api.py", line 478, in _call_unconverted
    return f(*args, **kwargs)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 820, in run_step
    outputs = model.train_step(data)
  File "/lfs/eq/tim/project_sept2020/cola/network.py", line 66, in train_step
    anchors, positives = data
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 505, in __iter__
    self._disallow_iteration()
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 498, in _disallow_iteration
    self._disallow_when_autograph_enabled("iterating over `tf.Tensor`")
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 474, in _disallow_when_autograph_enabled
    raise errors.OperatorNotAllowedInGraphError(
tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: iterating over `tf.Tensor` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature.
Traceback (most recent call last):
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/lfs/eq/tim/project_sept2020/cola/main.py", line 154, in <module>
    app.run(main)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/absl/app.py", line 300, in run
    _run_main(main, args)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/absl/app.py", line 251, in _run_main
    sys.exit(main(argv))
  File "/lfs/eq/tim/project_sept2020/cola/main.py", line 108, in main
    model.train()
  File "/lfs/eq/tim/project_sept2020/cola/contrastive.py", line 194, in train
    contrastive_network.fit(
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1132, in fit
    tmp_logs = self.train_function(iterator)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 784, in __call__
    result = self._call(*args, **kwds)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 827, in _call
    self._initialize(args, kwds, add_initializers_to=initializers)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 681, in _initialize
    self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2998, in _get_concrete_function_internal_garbage_collected
    graph_function, _ = self._maybe_define_function(args, kwargs)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 3390, in _maybe_define_function
    graph_function = self._create_graph_function(args, kwargs)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 3225, in _create_graph_function
    func_graph_module.func_graph_from_py_func(
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 998, in func_graph_from_py_func
    func_outputs = python_func(*func_args, **func_kwargs)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 590, in wrapped_fn
    out = weak_wrapped_fn().__wrapped__(*args, **kwds)
  File "/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 985, in wrapper
    raise e.ag_error_metadata.to_exception(e)
tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: in user code:

    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:837 train_function  *
        return step_function(self, iterator)
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:827 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2731 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/distribute/mirrored_strategy.py:628 _call_for_each_replica
        return mirrored_run.call_for_each_replica(
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/distribute/mirrored_run.py:93 call_for_each_replica
        return _call_for_each_replica(strategy, fn, args, kwargs)
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/distribute/mirrored_run.py:234 _call_for_each_replica
        coord.join(threads)
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/training/coordinator.py:389 join
        six.reraise(*self._exc_info_to_raise)
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/six.py:703 reraise
        raise value
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/training/coordinator.py:297 stop_on_exception
        yield
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/distribute/mirrored_run.py:323 run
        self.main_result = self.main_fn(*self.main_args, **self.main_kwargs)
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:820 run_step  **
        outputs = model.train_step(data)
    /lfs/eq/tim/project_sept2020/cola/network.py:66 train_step
        anchors, positives = data
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:505 __iter__
        self._disallow_iteration()
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:498 _disallow_iteration
        self._disallow_when_autograph_enabled("iterating over `tf.Tensor`")
    /misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:474 _disallow_when_autograph_enabled
        raise errors.OperatorNotAllowedInGraphError(

    OperatorNotAllowedInGraphError: iterating over `tf.Tensor` is not allowed: AutoGraph did convert this function. This might indicate you are trying to use an unsupported feature.

Epoch 1/100

INFO:tensorflow:向协调器报告的错误：不允许在“tf.Tensor”上迭代：AutoGraph未转换此函数。这可能表示您正试图使用不受支持的功能。
回溯（最近一次呼叫最后一次）：
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/training/coordinator.py”，第297行，在stop\u on\u异常中
产量
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/distribute/mirrored_run.py”，第323行，运行中
self.main_结果=self.main_fn（*self.main_参数，**self.main_参数）
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/autograph/impl/api.py”，第667行，在包装器中
返回转换的调用（f、args、kwargs、options=options）
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/autograph/impl/api.py”，第396行，在转换的调用中
返回-调用未转换（f、args、kwargs、options）
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/autograph/impl/api.py”，第478行，在“调用未转换”中
返回f（*args，**kwargs）
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/keras/engine/training.py”，第820行，运行步骤
输出=型号列车步进（数据）
文件“/lfs/eq/tim/project_sept2020/cola/network.py”，第66行，列车步骤中
锚定，正=数据
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/framework/ops.py”，第505行，在__
self.\u不允许\u迭代（）
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/framework/ops.py”，第498行，不允许迭代
self.\u在启用“签名”时不允许（“在`tf.Tensor`上迭代”）
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/framework/ops.py”，第474行，启用自动签名时不允许
引发错误。运算符或不允许引发错误(
tensorflow.python.framework.errors\u impl.operator不允许在`tf.Tensor`上迭代：AutoGraph未转换此函数。这可能表示您正在尝试使用不受支持的功能。
I1129 04:56:14.756884 140640742516480 coordinator.py:217]向coordinator报告的错误：不允许在“tf.Tensor”上迭代：AutoGraph未转换此函数。这可能表示您正在尝试使用不受支持的功能。
回溯（最近一次呼叫最后一次）：
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site-packages/tensorflow/python/training/coordinator.py”，第297行，在stop\u on\u异常中
产量
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/distribute/mirrored_run.py”，第323行，运行中
self.main_结果=self.main_fn（*self.main_参数，**self.main_参数）
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/autograph/impl/api.py”，第667行，在包装器中
返回转换的调用（f、args、kwargs、options=options）
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/autograph/impl/api.py”，第396行，在转换的调用中
返回-调用未转换（f、args、kwargs、options）
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/autograph/impl/api.py”，第478行，在“调用未转换”中
返回f（*args，**kwargs）
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/keras/engine/training.py”，第820行，运行步骤
输出=型号列车步进（数据）
文件“/lfs/eq/tim/project_sept2020/cola/network.py”，第66行，列车步骤中
锚定，正=数据
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/framework/ops.py”，第505行，在__
self.\u不允许\u迭代（）
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/framework/ops.py”，第498行，不允许迭代
self.\u在启用“签名”时不允许（“在`tf.Tensor`上迭代”）
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/site packages/tensorflow/python/framework/ops.py”，第474行，启用自动签名时不允许
引发错误。运算符或不允许引发错误(
tensorflow.python.framework.errors\u impl.operator不允许在`tf.Tensor`上迭代：AutoGraph未转换此函数。这可能表示您正在尝试使用不受支持的功能。
回溯（最近一次呼叫最后一次）：
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/runpy.py”，第194行，位于主模块中
返回运行代码（代码、主全局、无、，
文件“/misc/home/rc/tim/anaconda3/envs/tfenv2cola/lib/python3.8/runpy.py”，第87行，在
def load_dataset(folder):

  # Get the files
  filenames = glob.glob(folder)
  files_ds = tf.data.Dataset.from_tensor_slices(filenames)

  # Load waveforms
  dataset = files_ds.map(
  lambda x: tf.numpy_function(
    func=lambda y: np.array(file_load(y.decode("utf-8"))[0]),
    inp=[x],
    Tout=tf.float32,
  )
  )
  return dataset