Я новичок в машинном обучении и в настоящее время следую учебному пособию в блокноте jupyter. https://www.tensorflow.org/tutorials/estimator/linear
Однако я продолжал получать эту ошибку и не мог предсказать точность. Я попытался найти решение для Google, и они сказали, что это может быть устаревшая версия TF. Тем не менее, мой TF в настоящее время имеет версию '2.0.0-alpha0', и я использую python 3.7.4.
Спасибо за ваше время!
CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',
'embark_town', 'alone']
NUMERIC_COLUMNS = ['age', 'fare']
feature_columns = []
for feature_name in CATEGORICAL_COLUMNS:
vocabulary = dftrain[feature_name].unique() # gets a list of all unique values from given feature column
feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))
for feature_name in NUMERIC_COLUMNS:
feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.float32))
# The cryptic lines of code inside the append() create an object that our model can use to map string values like "male" and "female" to integers.
# This allows us to avoid manually having to encode our dataframes.
# https://www.tensorflow.org/api_docs/python/tf/feature_column/categorical_column_with_vocabulary_list?version=stable
def make_input_fn(data_df, label_df, num_epochs=10, 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_columns)
# We create a linear estimtor by passing the feature columns we created earlier
linear_est.train(train_input_fn) # train
result = linear_est.evaluate(eval_input_fn) # get model metrics/stats by testing on tetsing data
clear_output() # clears consoke output
print(result['accuracy']) # the result variable is simply a dict of stats about our model
INFO:tensorflow:Calling model_fn.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-27-1a944b4b2878> 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
3
4 clear_output() # clears consoke output
5 print(result['accuracy']) # the result variable is simply a dict of stats about our model
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
352
353 saving_listeners = _check_listeners_type(saving_listeners)
--> 354 loss = self._train_model(input_fn, hooks, saving_listeners)
355 logging.info('Loss for final step: %s.', loss)
356 return self
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
1181 return self._train_model_distributed(input_fn, hooks, saving_listeners)
1182 else:
-> 1183 return self._train_model_default(input_fn, hooks, saving_listeners)
1184
1185 def _train_model_default(self, input_fn, hooks, saving_listeners):
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
1211 worker_hooks.extend(input_hooks)
1212 estimator_spec = self._call_model_fn(
-> 1213 features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
1214 global_step_tensor = training_util.get_global_step(g)
1215 return self._train_with_estimator_spec(estimator_spec, worker_hooks,
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py in _call_model_fn(self, features, labels, mode, config)
1169
1170 logging.info('Calling model_fn.')
-> 1171 model_fn_results = self._model_fn(features=features, **kwargs)
1172 logging.info('Done calling model_fn.')
1173
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\linear.py in _model_fn(features, labels, mode, config)
337 partitioner=partitioner,
338 config=config,
--> 339 sparse_combiner=sparse_combiner)
340
341 super(LinearClassifier, self).__init__(
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\canned\linear.py in _linear_model_fn(features, labels, mode, head, feature_columns, optimizer, partitioner, config, sparse_combiner)
141 if not isinstance(features, dict):
142 raise ValueError('features should be a dictionary of `Tensor`s. '
--> 143 'Given type: {}'.format(type(features)))
144
145 optimizer = optimizers.get_optimizer_instance(
ValueError: features should be a dictionary of `Tensor`s. Given type: <class 'tensorflow.python.data.ops.dataset_ops.RepeatDataset'>