我在tensorflow中使用高级API有一个玩具示例：tf.estimator，tf.data和tf.feature_column。我想使用tf.keras.estimator.model_to_estimator将罐装估算器与keras模型交换。我可以从keras模型生成一个估算器，但后来我得到一个关于输入的名称和形状的错误。我认为keras模型的输入形状是错误的，因为input_fn传递了所有数据，而不是特征列。换句话说，我不确定如何将特征列连接到keras模型
以下是有效代码的相关部分：
...
col1 = categorical_column_with_vocabulary_list('col1', [1, 2, 3])
col1_ind = C.indicator_column(col1)
col2 = numeric_column('col2')
...
estimator = E.DNNClassifier(
feature_columns=[col1_ind, col2],
hidden_units=[10])
...
def input_fn(features, labels, batch_size):
dataset = D.Dataset.from_tensor_slices((dict(features),
labels))
dataset = dataset.shuffle(1000).repeat().batch(batch_size)
return dataset
...
train_and_evaluate(estimator, train_spec, eval_spec)
如果我尝试将DNNClassifier换成以下内容，我会遇到问题：
model = tf.keras.models.Sequential()
model.add(L.Dense(10, activation='relu', input_dim=9))
....
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
estimator = model_to_estimator(keras_model=model)
在这种情况下，我收到以下错误消息：
INFO:tensorflow:Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after 600 secs (eval_spec.throttle_secs) or training is finished.
INFO:tensorflow:Calling model_fn.
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-82-0242f6f379fc> in <module>()
----> 1 E.train_and_evaluate(estimator, train_spec, eval_spec)
~/.local/lib/python3.5/site-packages/tensorflow/python/estimator/training.py in train_and_evaluate(estimator, train_spec, eval_spec)
437 '(with task id 0). Given task id {}'.format(config.task_id))
438
--> 439 executor.run()
440
441
~/.local/lib/python3.5/site-packages/tensorflow/python/estimator/training.py in run(self)
516 config.task_type != run_config_lib.TaskType.EVALUATOR):
517 logging.info('Running training and evaluation locally (non-distributed).')
--> 518 self.run_local()
519 return
520
~/.local/lib/python3.5/site-packages/tensorflow/python/estimator/training.py in run_local(self)
648 input_fn=self._train_spec.input_fn,
649 max_steps=self._train_spec.max_steps,
--> 650 hooks=train_hooks)
651
652 # Final export signal: For any eval result with global_step >= train
~/.local/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
353
354 saving_listeners = _check_listeners_type(saving_listeners)
--> 355 loss = self._train_model(input_fn, hooks, saving_listeners)
356 logging.info('Loss for final step: %s.', loss)
357 return self
~/.local/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
822 worker_hooks.extend(input_hooks)
823 estimator_spec = self._call_model_fn(
--> 824 features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
825
826 if self._warm_start_settings:
~/.local/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py in _call_model_fn(self, features, labels, mode, config)
803
804 logging.info('Calling model_fn.')
--> 805 model_fn_results = self._model_fn(features=features, **kwargs)
806 logging.info('Done calling model_fn.')
807
~/.local/lib/python3.5/site-packages/tensorflow/python/keras/_impl/keras/estimator.py in model_fn(features, labels, mode)
317 """model_fn for keras Estimator."""
318 model = _clone_and_build_model(mode, keras_model, custom_objects, features,
--> 319 labels)
320 # Get inputs to EstimatorSpec
321 predictions = dict(zip(model.output_names, model.outputs))
~/.local/lib/python3.5/site-packages/tensorflow/python/keras/_impl/keras/estimator.py in _clone_and_build_model(mode, keras_model, custom_objects, features, labels)
251 input_tensors = _create_ordered_io(keras_model,
252 estimator_io=features,
--> 253 is_input=True)
254 # Get list of outputs.
255 if labels is None:
~/.local/lib/python3.5/site-packages/tensorflow/python/keras/_impl/keras/estimator.py in _create_ordered_io(keras_model, estimator_io, is_input)
94 'It needs to match one '
95 'of the following: %s' % ('input' if is_input else 'output', key,
---> 96 ', '.join(keras_io_names)))
97 tensors = [_cast_tensor_to_floatx(estimator_io[io_name])
98 for io_name in keras_io_names]
ValueError: Cannot find input with name "col1" in Keras Model. It needs to match one of the following: dense_1_input
要将feature_columns与通过model_to_estimator(keras_model=model)创建的估算器连接起来，必须使feature_column的名称与模型的输入图层的名称相匹配。
例如，您的input_fn()可能如下所示：
def input_fn(features, labels, batch_size):
dataset = D.Dataset.from_tensor_slices((dict(features), labels))
dataset = dataset.shuffle(1000).repeat().batch(batch_size)
iterator = dataset.make_initializable_iterator()
tf.add_to_collection(
tf.GraphKeys.TABLE_INITIALIZERS, iterator.initializer)
features, labels = iterator.get_next()
return {"dense_1_input": features}, labels
因此，无论输入图层的名称是什么，keras模型都需要添加_input的该名称的要素列：
model = tf.keras.models.Sequential()
model.add(L.Dense(10, activation='relu', input_dim=9, name="MY_NAME"))
def input_fn(features, labels, batch_size):
...
return {"MY_NAME_input": features}, labels
一些示例代码：
from tensorflow.python.feature_column import feature_column_v2 as fc
feature_layer = fc.FeatureLayer(your_feature_columns)
model = tf.keras.Sequential([
feature_layer,
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(64, activation=tf.nn.relu),
tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)
])