Question

我对tensorflow非常陌生，并试图学习如何保存和加载以前训练过的模型。我使用Estimator创建了一个简单模型并对其进行了训练。

classifier = tf.estimator.Estimator(model_fn=bag_of_words_model)

# Train
train_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"words": x_train},       # x_train is 2D numpy array of shape (26, 5)
    y=y_train,                  # y_train is 1D panda series of length 26 
    batch_size=1000,
    num_epochs=None,
    shuffle=True)

classifier.train(input_fn=train_input_fn, steps=300)

然后我尝试保存模型：

def serving_input_receiver_fn():
    serialized_tf_example = tf.placeholder(dtype=tf.int64, shape=(None, 5), name='words')
    receiver_tensors = {"predictor_inputs": serialized_tf_example}
    features = {"words": tf.placeholder(tf.int64, shape=(None, 5))}
    return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)

full_model_dir = classifier.export_savedmodel(export_dir_base="E:/models/", serving_input_receiver_fn=serving_input_receiver_fn)

我实际上已经从this similar question复制了serving_input_receiver_fn。我不完全了解该函数中发生了什么。但这会将我的模型存储在E:/models/<some time stamp>中。我现在尝试加载此保存的模型：

from tensorflow.contrib import predictor

classifier = predictor.from_saved_model("E:\\models\\<some time stamp>")

完美加载模型。此后，我对如何使用此classifier对象获取有关新数据的预测感到惊讶。我已经按照指南here来实现它，但没有做到:(。这就是我所做的：

predictions = classifier({'predictor_inputs': x_test})["output"]  # x_test is 2D numpy array same like x_train in the training part

我得到如下错误：

2019-01-10 12:43:38.603506: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
INFO:tensorflow:Restoring parameters from E:\models\1547101005\variables\variables
Traceback (most recent call last):
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\client\session.py", line 1334, in _do_call
    return fn(*args)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\client\session.py", line 1319, in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\client\session.py", line 1407, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype int64 and shape [?,5]
     [[{{node Placeholder}} = Placeholder[dtype=DT_INT64, shape=[?,5], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "E:/ml_classif/tensorflow_bow_with_prob/load_model.py", line 85, in <module>
    predictions = classifier({'predictor_inputs': x_test})["output"]
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\contrib\predictor\predictor.py", line 77, in __call__
    return self._session.run(fetches=self.fetch_tensors, feed_dict=feed_dict)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\client\session.py", line 929, in run
    run_metadata_ptr)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\client\session.py", line 1152, in _run
    feed_dict_tensor, options, run_metadata)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\client\session.py", line 1328, in _do_run
    run_metadata)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\client\session.py", line 1348, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype int64 and shape [?,5]
     [[node Placeholder (defined at E:\ml_classif\venv\lib\site-packages\tensorflow\contrib\predictor\saved_model_predictor.py:153)  = Placeholder[dtype=DT_INT64, shape=[?,5], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

Caused by op 'Placeholder', defined at:
  File "E:/ml_classif/tensorflow_bow_with_prob/load_model.py", line 82, in <module>
    classifier = predictor.from_saved_model("E:\\models\\1547101005")
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\contrib\predictor\predictor_factories.py", line 153, in from_saved_model
    config=config)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\contrib\predictor\saved_model_predictor.py", line 153, in __init__
    loader.load(self._session, tags.split(','), export_dir)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\saved_model\loader_impl.py", line 197, in load
    return loader.load(sess, tags, import_scope, **saver_kwargs)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\saved_model\loader_impl.py", line 350, in load
    **saver_kwargs)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\saved_model\loader_impl.py", line 278, in load_graph
    meta_graph_def, import_scope=import_scope, **saver_kwargs)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\training\saver.py", line 1696, in _import_meta_graph_with_return_elements
    **kwargs))
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\framework\meta_graph.py", line 806, in import_scoped_meta_graph_with_return_elements
    return_elements=return_elements)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\util\deprecation.py", line 488, in new_func
    return func(*args, **kwargs)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\framework\importer.py", line 442, in import_graph_def
    _ProcessNewOps(graph)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\framework\importer.py", line 234, in _ProcessNewOps
    for new_op in graph._add_new_tf_operations(compute_devices=False):  # pylint: disable=protected-access
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\framework\ops.py", line 3440, in _add_new_tf_operations
    for c_op in c_api_util.new_tf_operations(self)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\framework\ops.py", line 3440, in <listcomp>
    for c_op in c_api_util.new_tf_operations(self)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\framework\ops.py", line 3299, in _create_op_from_tf_operation
    ret = Operation(c_op, self)
  File "E:\ml_classif\venv\lib\site-packages\tensorflow\python\framework\ops.py", line 1770, in __init__
    self._traceback = tf_stack.extract_stack()

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype int64 and shape [?,5]
     [[node Placeholder (defined at E:\ml_classif\venv\lib\site-packages\tensorflow\contrib\predictor\saved_model_predictor.py:153)  = Placeholder[dtype=DT_INT64, shape=[?,5], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

它说我必须将价值提供给占位符（我认为serving_input_receiver_fn中定义的占位符）。我不知道如何在不使用张量流的Session对象的情况下做到这一点。

如有需要，请随时询问更多信息。

Answer 1

在对serving_input_receiver_fn有点模糊的理解之后，我发现features一定不能是placeholder，因为它会创建2个占位符（1个占位serialized_tf_example，另一个占位符features）。我修改了函数，如下所示（更改仅针对features变量）：

def serving_input_receiver_fn():
    serialized_tf_example = tf.placeholder(dtype=tf.int64, shape=(None, 5), name='words')
    receiver_tensors = {"predictor_inputs": serialized_tf_example}
    features = {"words": tf.tile(serialized_tf_example, multiples=[1, 1])}  # Changed this
    return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)

当我尝试预测加载模型的输出时，现在没有错误。有用！唯一的问题是输出不正确（为此我发布了一个新问题:)）。

张量流模型的预测失败

1 个答案: