TypeError:传递给“ Pack” Op的“ values”的列表中的张量具有不完全匹配的类型[string,float32]。 CSV教程中的错误

时间:2019-12-08 18:57:09

标签: python-3.x pandas numpy tensorflow tf.keras

我在TensorFlow中获得了自己的训练数据等。我得到了Load CSV Data tutorial from TensorFlow on Colab,并更改了一些变量的配置以匹配我的数据。运行程序时,出现此错误:

/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.

该块的整个输出可用here on Pastebin

本教程中运行的代码块是:

packed_dataset = temp_dataset.map(pack)

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

1 个答案:

答案 0 :(得分:0)

我解决了我的问题。

我意外地忘记了我的第一列不是不是数字,所以我将其从列表中删除了。

# 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())