类型错误：传递给 'Pack' Op 的 'values' 的列表中的张量的类型 [string, float32] 并不完全匹配。 来自 CSV 教程的错误

Question

我在 TensorFlow 中获得了自己的训练数据等。 我从 Colab 上的 TensorFlow获得了Load CSV Data 教程，我更改了一些变量的配置以匹配我的数据。 当我运行程序时，我收到了这个错误：

/tensorflow-2.0.0/python3.6/tensorflow_core/python/autograph/impl/api.py in wrapper(*args, **kwargs)
    235       except Exception as e:  # pylint:disable=broad-except
    236         if hasattr(e, 'ag_error_metadata'):
--> 237           raise e.ag_error_metadata.to_exception(e)
    238         else:
    239           raise

TypeError: in converted code:

    <ipython-input-15-ed03747f8311>:2 pack  *
        return tf.stack(list(features.values()), axis=-1), label
    /tensorflow-2.0.0/python3.6/tensorflow_core/python/util/dispatch.py:180 wrapper
        return target(*args, **kwargs)
    /tensorflow-2.0.0/python3.6/tensorflow_core/python/ops/array_ops.py:1165 stack
        return gen_array_ops.pack(values, axis=axis, name=name)
    /tensorflow-2.0.0/python3.6/tensorflow_core/python/ops/gen_array_ops.py:6304 pack
        "Pack", values=values, axis=axis, name=name)
    /tensorflow-2.0.0/python3.6/tensorflow_core/python/framework/op_def_library.py:499 _apply_op_helper
        raise TypeError("%s that don't all match." % prefix)

    TypeError: Tensors in list passed to 'values' of 'Pack' Op have types [string, float32] that don't all match.

该块的整个输出可在 Pastebin 上获得。

教程中运行的块是：

packed_dataset = temp_dataset.map(pack)

for features, labels in packed_dataset.take(1):
  print(features.numpy())
  print()
  print(labels.numpy())

Answer 1

我解决了我的问题。

我不小心忘记了我的第一列不是数字，所以我从列表中删除了它。

# Before Stuff
After Stuff

# SELECT_COLUMNS = ['sid', 'dist', 'microtime']
SELECT_COLUMNS = ['dist', 'microtime']
# DEFAULTS       = ['a_r_d_b_id', 0.0, 0.0]
DEFAULTS       = [0.0, 0.0]
temp_dataset = get_dataset(train_file_path, 
                           select_columns=SELECT_COLUMNS,
                           column_defaults = DEFAULTS)

show_batch(temp_dataset)

example_batch, labels_batch = next(iter(temp_dataset)) 

def pack(features, label):
  return tf.stack(list(features.values()), axis=-1), label

packed_dataset = temp_dataset.map(pack)

for features, labels in packed_dataset.take(1):
  print(features.numpy())
  print()
  print(labels.numpy())