线性模型不支持将字符串转换为浮点数 [英] Cast string to float is not supported in Linear Model

问题描述

我的线性模型中不断出现此错误:

I keep getting this error in my linear model:

不支持将字符串转换为浮点

Cast string to float is not supported

具体来说，错误在这一行:

Specifically, the error is on this line:

results = m.evaluate(input_fn=lambda: input_fn(df_test), steps=1)

如果有帮助，这里是堆栈跟踪:

If it helps, here's the stack trace:

 File "tensorflowtest.py", line 164, in <module>
    m.fit(input_fn=lambda: input_fn(df_train), steps=int(100))
  File "/home/computer/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/linear.py", line 475, in fit
    max_steps=max_steps)
  File "/home/computer/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 333, in fit
    max_steps=max_steps)
  File "/home/computer/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 662, in _train_model
    train_op, loss_op = self._get_train_ops(features, targets)
  File "/home/computer/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 963, in _get_train_ops
    _, loss, train_op = self._call_model_fn(features, targets, ModeKeys.TRAIN)
  File "/home/computer/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 944, in _call_model_fn
    return self._model_fn(features, targets, mode=mode, params=self.params)
  File "/home/computer/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/linear.py", line 220, in _linear_classifier_model_fn
    loss = loss_fn(logits, targets)
  File "/home/computer/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/linear.py", line 141, in _log_loss_with_two_classes
    logits, math_ops.to_float(target))
  File "/home/computer/.local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 661, in to_float
    return cast(x, dtypes.float32, name=name)
  File "/home/computer/.local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 616, in cast
    return gen_math_ops.cast(x, base_type, name=name)
  File "/home/computer/.local/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 419, in cast
    result = _op_def_lib.apply_op("Cast", x=x, DstT=DstT, name=name)
  File "/home/computer/.local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 749, in apply_op
    op_def=op_def)
  File "/home/computer/.local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2380, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/home/computer/.local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1298, in __init__
    self._traceback = _extract_stack()

UnimplementedError (see above for traceback): Cast string to float is not supported
         [[Node: ToFloat = Cast[DstT=DT_FLOAT, SrcT=DT_STRING, _device="/job:localhost/replica:0/task:0/cpu:0"](Reshape_1)]]

该模型改编自此处 和 此处.教程代码确实可以运行，所以我的 TensorFlow 安装没有问题.

The model is an adaptation of the tutorial from here and here. The tutorial code does run, so it's not a problem with my TensorFlow installation.

输入的 CSV 是许多二进制分类列形式的数据 (yes/no).最初，我将每列中的数据表示为 0 和 1，但是当我将其更改为 ys 和 ns 时，我得到了同样的错误.

The input CSV is data in the form of many binary categorical columns (yes/no). Initially, I represented the data in each column as 0's and 1's, but I get the same error when I change it to ys and ns.

我该如何解决这个问题?

How do I fix this?

推荐答案

我遇到了完全相同的问题，您需要确保为模型提供的输入数据格式正确.(不仅是特征，还有标签列)

I had the exact same problem, you need to make sure that the input data you are feeding the model is in the right format. ( not just the features but also the label column)

我的问题是我没有跳过数据文件中的第一行，所以我试图将标题转换为浮点格式.就像添加一样简单

My problem was that i was not skipping the first row in the data file, so i was trying to convert the titles to float format.Something as simple as adding

skiprows=1

读取 csv 时:

df_test = pd.read_csv(test_file, names=COLUMNS_TEST, skipinitialspace=True, skiprows=1, engine="python")

我建议您检查:

df_test.dtypes

你应该得到类似的东西

Feature1      int64
Feature2      int64
Feature3      int64
Feature4      object
Feature5      object
Feature6      float64
dtype: object

如果您没有获得正确的 dtype，那么 model.fit 将会失败

If you are not getting the correct dtype then the model.fit is going to fail

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