预测多元时间序列（在《机器学习实践》一书第15章中）错误

Question

我正在使用 Aurélien Géron 所著的《使用 Scikit-Learn、Keras 和 TensorFlow 进行机器学习实践》一书（第三版）第 15 章的 Jupyter Notebook。我在笔记本的 43 单元格中遇到错误。它位于多元时间序列段落中。原代码如下：

tf.random.set_seed(42)  # extra code – ensures reproducibility

train_mulvar_ds = tf.keras.utils.timeseries_dataset_from_array(
    mulvar_train.to_numpy(),  # use all 5 columns as input
    targets=mulvar_train["rail"][seq_length:],  # forecast only the rail series
    sequence_length=seq_length,
    batch_size=32,
    shuffle=True,
    seed=42
)

错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[44], line 3
      1 tf.random.set_seed(42)  # extra code – ensures reproducibility
----> 3 train_mulvar_ds = tf.keras.utils.timeseries_dataset_from_array(
      4     mulvar_train.to_numpy(),  # use all 5 columns as input
      5     targets=mulvar_train["rail"][seq_length:],  # forecast only the rail series
      6     sequence_length=seq_length,
      7     batch_size=32,
      8     shuffle=True,
      9     seed=42
     10 )
     11 valid_mulvar_ds = tf.keras.utils.timeseries_dataset_from_array(
     12     mulvar_valid.to_numpy(),
     13     targets=mulvar_valid["rail"][seq_length:],
     14     sequence_length=seq_length,
     15     batch_size=32
     16 )

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/keras/src/utils/timeseries_dataset.py:245, in timeseries_dataset_from_array(data, targets, sequence_length, sequence_stride, sampling_rate, batch_size, shuffle, seed, start_index, end_index)
    233 # For each initial window position, generates indices of the window elements
    234 indices = tf.data.Dataset.zip(
    235     (tf.data.Dataset.range(len(start_positions)), positions_ds)
    236 ).map(
   (...)
    242     num_parallel_calls=tf.data.AUTOTUNE,
    243 )
--> 245 dataset = sequences_from_indices(data, indices, start_index, end_index)
    246 if targets is not None:
    247     indices = tf.data.Dataset.zip(
    248         (tf.data.Dataset.range(len(start_positions)), positions_ds)
    249     ).map(
    250         lambda i, positions: positions[i],
    251         num_parallel_calls=tf.data.AUTOTUNE,
    252     )

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/keras/src/utils/timeseries_dataset.py:270, in sequences_from_indices(array, indices_ds, start_index, end_index)
    269 def sequences_from_indices(array, indices_ds, start_index, end_index):
--> 270     dataset = tf.data.Dataset.from_tensors(array[start_index:end_index])
    271     dataset = tf.data.Dataset.zip((dataset.repeat(), indices_ds)).map(
    272         lambda steps, inds: tf.gather(steps, inds),
    273         num_parallel_calls=tf.data.AUTOTUNE,
    274     )
    275     return dataset

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/tensorflow/python/data/ops/dataset_ops.py:746, in DatasetV2.from_tensors(tensors, name)
    742 # Loaded lazily due to a circular dependency (dataset_ops ->
    743 # from_tensors_op -> dataset_ops).
    744 # pylint: disable=g-import-not-at-top,protected-access
    745 from tensorflow.python.data.ops import from_tensors_op
--> 746 return from_tensors_op._from_tensors(tensors, name)

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/tensorflow/python/data/ops/from_tensors_op.py:23, in _from_tensors(tensors, name)
     22 def _from_tensors(tensors, name):  # pylint: disable=unused-private-name
---> 23   return _TensorDataset(tensors, name)

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/tensorflow/python/data/ops/from_tensors_op.py:31, in _TensorDataset.__init__(self, element, name)
     29 def __init__(self, element, name=None):
     30   """See `tf.data.Dataset.from_tensors` for details."""
---> 31   element = structure.normalize_element(element)
     32   self._structure = structure.type_spec_from_value(element)
     33   self._tensors = structure.to_tensor_list(self._structure, element)

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/tensorflow/python/data/util/structure.py:133, in normalize_element(element, element_signature)
    130       else:
    131         dtype = getattr(spec, "dtype", None)
    132         normalized_components.append(
--> 133             ops.convert_to_tensor(t, name="component_%d" % i, dtype=dtype))
    134 return nest.pack_sequence_as(pack_as, normalized_components)

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/tensorflow/python/profiler/trace.py:183, in trace_wrapper.<locals>.inner_wrapper.<locals>.wrapped(*args, **kwargs)
    181   with Trace(trace_name, **trace_kwargs):
    182     return func(*args, **kwargs)
--> 183 return func(*args, **kwargs)

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:1443, in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
   1441 # TODO(b/142518781): Fix all call-sites and remove redundant arg
   1442 preferred_dtype = preferred_dtype or dtype_hint
-> 1443 return tensor_conversion_registry.convert(
   1444     value, dtype, name, as_ref, preferred_dtype, accepted_result_types
   1445 )

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/tensorflow/python/framework/tensor_conversion_registry.py:234, in convert(value, dtype, name, as_ref, preferred_dtype, accepted_result_types)
    225       raise RuntimeError(
    226           _add_error_prefix(
    227               f"Conversion function {conversion_func!r} for type "
   (...)
    230               f"actual = {ret.dtype.base_dtype.name}",
    231               name=name))
    233 if ret is None:
--> 234   ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
    236 if ret is NotImplemented:
    237   continue

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py:324, in _constant_tensor_conversion_function(v, dtype, name, as_ref)
    321 def _constant_tensor_conversion_function(v, dtype=None, name=None,
    322                                          as_ref=False):
    323   _ = as_ref
--> 324   return constant(v, dtype=dtype, name=name)

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py:263, in constant(value, dtype, shape, name)
    166 @tf_export("constant", v1=[])
    167 def constant(value, dtype=None, shape=None, name="Const"):
    168   """Creates a constant tensor from a tensor-like object.
    169 
    170   Note: All eager `tf.Tensor` values are immutable (in contrast to
   (...)
    261     ValueError: if called on a symbolic tensor.
    262   """
--> 263   return _constant_impl(value, dtype, shape, name, verify_shape=False,
    264                         allow_broadcast=True)

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py:275, in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
    273     with trace.Trace("tf.constant"):
    274       return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
--> 275   return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
    277 const_tensor = ops._create_graph_constant(  # pylint: disable=protected-access
    278     value, dtype, shape, name, verify_shape, allow_broadcast
    279 )
    280 return const_tensor

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py:285, in _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
    283 def _constant_eager_impl(ctx, value, dtype, shape, verify_shape):
    284   """Creates a constant on the current device."""
--> 285   t = convert_to_eager_tensor(value, ctx, dtype)
    286   if shape is None:
    287     return t

File ~/anaconda3/envs/mlp/lib/python3.8/site-packages/tensorflow/python/framework/constant_op.py:98, in convert_to_eager_tensor(value, ctx, dtype)
     96     dtype = dtypes.as_dtype(dtype).as_datatype_enum
     97 ctx.ensure_initialized()
---> 98 return ops.EagerTensor(value, ctx.device_name, dtype)

ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float).

我使用配备 M1 Max 处理器的 MacBook Pro。我不知道这是否会产生错误。

如何解决这个问题？

Answer 1

mulvar_train.to_numpy().astype(np.float32),  
targets=mulvar_train["rail"][seq_length:].astype(np.float32),