给出值错误。我已经尝试了一切。 这是错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-105-5a2413254bdd> in <module>()
----> 1 linear_est.train(train_input_fn) # train
2 # result = linear_est.evaluate(eval_input_fn) # get model metrics/stats by testing on tetsing data
8 frames
/usr/local/lib/python3.6/dist-packages/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
ValueError: in converted code:
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/feature_column/feature_column_v2.py:706 call
return self.layer(features)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/base_layer.py:748 __call__
self._maybe_build(inputs)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/base_layer.py:2116 _maybe_build
self.build(input_shapes)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/feature_column/feature_column_v2.py:505 build
trainable=self.trainable)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/feature_column/feature_column_v2.py:308 create_variable
getter=variable_scope.get_variable)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/base_layer.py:446 add_weight
caching_device=caching_device)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/tracking/base.py:744 _add_variable_with_custom_getter
**kwargs_for_getter)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/variable_scope.py:1572 get_variable
aggregation=aggregation)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/variable_scope.py:1315 get_variable
aggregation=aggregation)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/variable_scope.py:568 get_variable
aggregation=aggregation)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/variable_scope.py:520 _true_getter
aggregation=aggregation)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/variable_scope.py:938 _get_single_variable
aggregation=aggregation)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/variables.py:258 __call__
return cls._variable_v1_call(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/variables.py:219 _variable_v1_call
shape=shape)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/variables.py:197 <lambda>
previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/variable_scope.py:2596 default_variable_creator
shape=shape)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/variables.py:262 __call__
return super(VariableMetaclass, cls).__call__(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1411 __init__
distribute_strategy=distribute_strategy)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1520 _init_from_args
if init_from_fn else [initial_value]) as name:
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py:6249 __enter__
return self._name_scope.__enter__()
/usr/lib/python3.6/contextlib.py:81 __enter__
return next(self.gen)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/ops.py:4024 name_scope
raise ValueError("'%s' is not a valid scope name" % name)
ValueError: 'linear/linear_model/Adult Mortality/weights' is not a valid scope name
为什么会出现错误？ 在我的代码中，我基本上制作了一个线性回归模型，该模型使用的是名为data.csv的有关预期寿命的文件。我不了解此错误的原因。任何帮助将是非常可观的。
我的代码：
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import clear_output
from six.moves import urllib
from sklearn.model_selection import train_test_split
import tensorflow.compat.v2.feature_column as fc
import tensorflow as tf
df = pd.read_csv('data.csv')
dftrain, dfeval = train_test_split(df, test_size = 0.2, random_state = 0)
y_train = dftrain.pop('Status')
y_eval = dfeval.pop('Status')
print(y_eval.shape)
dftrain.head()
dfeval.shape
CATEGORICAL_COLUMNS = ['Country']
NUMERIC_COLUMNS = ['Year', 'Life expectancy', 'Adult Mortality', 'infant deaths',
'Alcohol', 'percentage expenditure', 'Hepatitis B', 'Measles',
'BMI', 'under-five deaths', 'Polio', 'Total expenditure',
'Diphtheria', 'HIV/AIDS', 'GDP', 'Population', 'thinness 1-19 years',
'thinness 5-9 years', 'Income composition of resources', 'Schooling']
feature_columnss = []
for feature_name in CATEGORICAL_COLUMNS:
vocabulary = dftrain[feature_name].unique() # gets a list of all unique values from given feature column
feature_columnss.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))
for feature_name in NUMERIC_COLUMNS:
feature_columnss.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))
print(feature_columnss)
def make_input_fn(data_df, label_df, num_epochs=15, shuffle=True, batch_size=32):
def input_function(): # inner function, this will be returned
ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df)) # create tf.data.Dataset object with data and its label
if shuffle:
ds = ds.shuffle(1000) # randomize order of data
ds = ds.batch(batch_size).repeat(num_epochs) # split dataset into batches of 32 and repeat process for number of epochs
return ds # return a batch of the dataset
return input_function # return a function object for use
train_input_fn = make_input_fn(dftrain, y_train) # here we will call the input_function that was returned to us to get a dataset object we can feed to the model
eval_input_fn = make_input_fn(dfeval, y_eval, num_epochs=1, shuffle=False)
linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columnss)
linear_est.train(train_input_fn) # train
result = linear_est.evaluate(eval_input_fn) # get model metrics/stats by testing on tetsing data