线性模型中不支持将字符串转换为浮点数
线性模型中不支持将字符串转换为浮点数
提问于 2016-10-22 07:27:27
我一直在我的线性模型中得到这个错误：
不支持将
转换为浮点型字符串
具体地说，错误在这一行上：
results = m.evaluate(input_fn=lambda: input_fn(df_test), steps=1)如果有帮助，下面是堆栈跟踪：
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here和here的教程。教程代码可以运行，所以这对我的TensorFlow安装来说不是问题。
输入CSV是许多二进制分类列(yes/no)形式的数据。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，我得到了相同的错误。
我该如何解决这个问题？
回答 11
Stack Overflow用户
发布于 2016-12-06 19:58:42
我遇到了完全相同的问题，您需要确保提供给模型的输入数据的格式是正确的。(不仅是功能，还有标签列)
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型format.Something，就像添加
skiprows=1读取csv时：
df_test = pd.read_csv(test_file, names=COLUMNS_TEST, skipinitialspace=True, skiprows=1, engine="python")我建议你检查一下：
df_test.dtypes你应该得到像这样的东西
Feature1 int64
Feature2 int64
Feature3 int64
Feature4 object
Feature5 object
Feature6 float64
dtype: object如果您未获得正确的数据类型，则model.fit将失败
Stack Overflow用户
发布于 2018-01-21 03:13:51
问题是您可能已经指出了类似于真正的类型的特性，但是在您的数据框中仍然是string，或者当在tf.constant中设置时，您没有转换为正确的类型。
确认列的类型。你可以只检查类型(df是你的数据帧)：
df.info()您可以看到所有列和类型，其中一些如下所示：
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 178932 entries, 0 to 178931
Data columns (total 64 columns):
d_prcp 178932 non-null float64
d_stn 178932 non-null int64
ws_lat 178932 non-null float64
ws_lon 178932 non-null float64
d_year 178932 non-null int64
d_temp 178932 non-null float64
...您可以使用下面的函数在tensorflow中将数据转换为正确的类型。(此代码来自repo google/training-data-analyst：link here)
def make_input_fn(df):
def pandas_to_tf(pdcol):
# convert the pandas column values to float
t = tf.constant(pdcol.astype('float32').values)
# take the column which is of shape (N) and make it (N, 1)
return tf.expand_dims(t, -1)
def input_fn():
# create features, columns
features = {k: pandas_to_tf(df[k]) for k in FEATURES}
labels = tf.constant(df[TARGET].values)
return features, labels
return input_fn
def make_feature_cols():
input_columns = [tf.contrib.layers.real_valued_column(k) for k in FEATURES]
return input_columnsStack Overflow用户
发布于 2016-10-22 10:36:16
你不能从字面上把一个字符串转换成一个数字，特别是"y"，"n“到1.0/0.0。
如果你有数字字符串(例如"0")，你可以尝试tf.string_to_number(..)
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/40186722
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