Python 使用tensorflow检查点进行推理

我正在将字符（

x_train

）输入到的示例13中定义的RNN模型。下面是对应于模型定义、输入预处理和训练的代码

def char_rnn_model(features, target):
    """Character level recurrent neural network model to predict classes."""
    target = tf.one_hot(target, 15, 1, 0)
    #byte_list = tf.one_hot(features, 256, 1, 0)
    byte_list = tf.cast(tf.one_hot(features, 256, 1, 0), dtype=tf.float32)
    byte_list = tf.unstack(byte_list, axis=1)

    cell = tf.contrib.rnn.GRUCell(HIDDEN_SIZE)
    _, encoding = tf.contrib.rnn.static_rnn(cell, byte_list, dtype=tf.float32)


    logits = tf.contrib.layers.fully_connected(encoding, 15, activation_fn=None)
    #loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
    loss = tf.contrib.losses.softmax_cross_entropy(logits=logits, onehot_labels=target)

    train_op = tf.contrib.layers.optimize_loss(
      loss,
      tf.contrib.framework.get_global_step(),
      optimizer='Adam',
      learning_rate=0.001)

    return ({
      'class': tf.argmax(logits, 1),
      'prob': tf.nn.softmax(logits)
    }, loss, train_op)

# pre-process 
char_processor = learn.preprocessing.ByteProcessor(MAX_DOCUMENT_LENGTH)
x_train = np.array(list(char_processor.fit_transform(x_train)))
x_test = np.array(list(char_processor.transform(x_test)))

# train
model_dir = "model"
classifier = learn.Estimator(model_fn=char_rnn_model,model_dir=model_dir)
count=0
n_epoch = 20
while count<n_epoch:
        print("\nEPOCH " + str(count))
        classifier.fit(x_train, y_train, steps=1000,batch_size=10)
        y_predicted = [
              p['class'] for p in classifier.predict(
              x_test, as_iterable=True,batch_size=10)
        ]
        score = metrics.accuracy_score(y_test, y_predicted)
        print('Accuracy: {0:f}'.format(score))
        count+=1

print(metrics.classification_report(y_test, predicted))

其中

meta_file

是指向model.ckpt-？？？？？？？？？meta文件之一的路径

我想将经过训练的模型应用于新的角色序列。所以我打了：

new_input = ["Some Sequence of character"]
new_input_processed = np.array(list(char_processor.transform(new_input)))
output = sess.run(new_input_processed)

但我得到了以下错误：

   ---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-13-982f2b9b18b3> in <module>()
----> 1 output = sess.run(new_input_processed)

/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    898     try:
    899       result = self._run(None, fetches, feed_dict, options_ptr,
--> 900                          run_metadata_ptr)
    901       if run_metadata:
    902         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1118     # Create a fetch handler to take care of the structure of fetches.
   1119     fetch_handler = _FetchHandler(
-> 1120         self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)
   1121 
   1122     # Run request and get response.

/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in __init__(self, graph, fetches, feeds, feed_handles)
    425     """
    426     with graph.as_default():
--> 427       self._fetch_mapper = _FetchMapper.for_fetch(fetches)
    428     self._fetches = []
    429     self._targets = []

/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in for_fetch(fetch)
    251         if isinstance(fetch, tensor_type):
    252           fetches, contraction_fn = fetch_fn(fetch)
--> 253           return _ElementFetchMapper(fetches, contraction_fn)
    254     # Did not find anything.
    255     raise TypeError('Fetch argument %r has invalid type %r' % (fetch,

/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in __init__(self, fetches, contraction_fn)
    284         raise TypeError('Fetch argument %r has invalid type %r, '
    285                         'must be a string or Tensor. (%s)' %
--> 286                         (fetch, type(fetch), str(e)))
    287       except ValueError as e:
    288         raise ValueError('Fetch argument %r cannot be interpreted as a '

TypeError: Fetch argument array([[ 83, 111, 109, 101,  32,  83, 101, 113, 117, 101, 110,  99, 101,
         32, 111, 102,  32,  99, 104,  97, 114,  97,  99, 116, 101, 114,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0]], dtype=uint8) has invalid type <type 'numpy.ndarray'>, must be a string or Tensor. (Can not convert a ndarray into a Tensor or Operation.)

---------------------------------------------------------------------------
TypeError回溯（最近一次调用上次）
在（）
---->1输出=sess.run（新输入已处理）
/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run（self、fetches、feed_dict、options、run_元数据）
898试试：
899结果=self.\u运行（无、取数、输入、选项、，
-->900运行（元数据（ptr）
901如果运行\u元数据：
902 proto_data=tf_session.tf_GetBuffer（run_metadata_ptr）
/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in\u run（self、handle、fetches、feed\u dict、options、run\u元数据）
1118#创建一个获取处理程序来处理获取的结构。
1119 fetch\u handler=\u FetchHandler(
->1120 self.\u图形、获取、馈送\u dict\u张量、馈送\u句柄=馈送\u句柄）
1121
1122#运行请求并获取响应。
/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in_u__________（self、graph、fetches、feed、feed_句柄）
425     """
426与graph.as_default（）一样：
-->427 self.\u fetch\u mapper=\u FetchMapper.for\u fetch（fetches）
428 self._fetches=[]
429自我目标=[]
/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in for_fetch（fetch）
251如果存在（提取，张量类型）：
252取数，收缩=fetch（取数）
-->253返回元素fetchmapper（获取、收缩）
254#什么也没找到。
255 raise TypeError（'提取参数%r具有无效的类型%r'（提取，
/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in_u_初始化（self、fetches、construction）
284 raise TypeError（'获取参数%r的类型%r无效，'
285'必须是字符串或张量。（%s）'
-->286（fetch，type（fetch），str（e）））
287除e值错误外：
288 raise VALUERROR（'无法将提取参数%r解释为'
TypeError:获取参数数组（[[83,111,109,101,32,83,101,113,117,101,110,99,101，
32, 111, 102,  32,  99, 104,  97, 114,  97,  99, 116, 101, 114,
0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
0，0，0，0，0，0，0，0]]，dtype=uint8）的类型无效，必须是字符串或张量。（无法将数据数组转换为张量或操作。）

我使用的是Tensorflow 1.8.0和python 2.7.14

=================== 编辑 ===================

可能应该使用（）函数

export\u saved model

，但我不理解它的所有参数

   ---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-13-982f2b9b18b3> in <module>()
----> 1 output = sess.run(new_input_processed)

/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    898     try:
    899       result = self._run(None, fetches, feed_dict, options_ptr,
--> 900                          run_metadata_ptr)
    901       if run_metadata:
    902         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1118     # Create a fetch handler to take care of the structure of fetches.
   1119     fetch_handler = _FetchHandler(
-> 1120         self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)
   1121 
   1122     # Run request and get response.

/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in __init__(self, graph, fetches, feeds, feed_handles)
    425     """
    426     with graph.as_default():
--> 427       self._fetch_mapper = _FetchMapper.for_fetch(fetches)
    428     self._fetches = []
    429     self._targets = []

/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in for_fetch(fetch)
    251         if isinstance(fetch, tensor_type):
    252           fetches, contraction_fn = fetch_fn(fetch)
--> 253           return _ElementFetchMapper(fetches, contraction_fn)
    254     # Did not find anything.
    255     raise TypeError('Fetch argument %r has invalid type %r' % (fetch,

/home/user/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in __init__(self, fetches, contraction_fn)
    284         raise TypeError('Fetch argument %r has invalid type %r, '
    285                         'must be a string or Tensor. (%s)' %
--> 286                         (fetch, type(fetch), str(e)))
    287       except ValueError as e:
    288         raise ValueError('Fetch argument %r cannot be interpreted as a '

TypeError: Fetch argument array([[ 83, 111, 109, 101,  32,  83, 101, 113, 117, 101, 110,  99, 101,
         32, 111, 102,  32,  99, 104,  97, 114,  97,  99, 116, 101, 114,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0]], dtype=uint8) has invalid type <type 'numpy.ndarray'>, must be a string or Tensor. (Can not convert a ndarray into a Tensor or Operation.)