Python 属性错误：'；张量'；对象没有属性'；形状'；

堆栈跟踪

Traceback (most recent call last):
  File "main.py", line 6, in <module>
    connection.start_socket(8089, callback=handler.message_processor)
  File "/mnt/d/workspace/SketchRecognitionWithTensorFlow/src/main/python/connection/python_socket_server.py", line 13, in start_socket
    process_message(connection, callback=callback)
  File "/mnt/d/workspace/SketchRecognitionWithTensorFlow/src/main/python/connection/python_socket_server.py", line 38, in process_message
    result = callback(general_proto)
  File "/mnt/d/workspace/SketchRecognitionWithTensorFlow/src/main/python/recognition/proto_handler.py", line 39, in message_processor
    return train_shape(general_proto.template)
  File "/mnt/d/workspace/SketchRecognitionWithTensorFlow/src/main/python/recognition/proto_handler.py", line 23, in train_shape
    rec.add_training_data(recognition_template.interpretation.label, recognition_template.shape)
  File "/mnt/d/workspace/SketchRecognitionWithTensorFlow/src/main/python/recognition/recognition_manager.py", line 98, in add_training_data
    self.recognizers[label].train(label, points)
  File "/mnt/d/workspace/SketchRecognitionWithTensorFlow/src/main/python/recognition/simple/recognizer.py", line 78, in train
    self.classifier.fit(x=reshaped_tensor, y=target, steps=1)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 173, in fit
    input_fn, feed_fn = _get_input_fn(x, y, batch_size)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 67, in _get_input_fn
    x, y, n_classes=None, batch_size=batch_size)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/io/data_feeder.py", line 117, in setup_train_data_feeder
    X, y, n_classes, batch_size, shuffle=shuffle, epochs=epochs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/learn/python/learn/io/data_feeder.py", line 239, in __init__
    self.X.shape, None if self.y is None else self.y.shape, n_classes,
AttributeError: 'Tensor' object has no attribute 'shape'

标签是一个字符串 点列表是x，y值数组的列表 例：

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我不知道为什么张量没有形状属性，或者如何解决这个问题。非常感谢您的帮助。

对于没有行号的代码段，堆栈跟踪没有多大帮助。但我可以告诉你们，当我看到那个错误的时候，是因为我在numpy数组上使用了tf.reforme，而不是真正的张量流张量。当我试图确定形状时，结果对象抛出了该错误。希望这能有所帮助。

从提供的代码中很难看出，特别是因为我无法确定错误发生在哪里。但无论如何，我以前遇到过一个相关的问题：

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在我看来，一些代码试图处理tf张量，就像它是np数组一样。张量没有

shape

属性，因为它们的形状存储为更复杂的对象。如果你想从np中获取信息，你必须调用

my\u tensor.get\u shape（）.as\u list（）

。但是，由于您的代码（您发布的）中的非用户试图访问

张量上的任何
形状
属性，我不确定如何使用这些信息来解决您的问题。
因为没有可接受的答案，而且我自己来自谷歌：

归功于mrry，其内容如下：

由于TensorFlow 1.0，
tf.Tensor
现在有一个属性，它返回与相同的值。
那么解决方案是什么呢？？？@YanKingYin我看不出问题所在-你被困在哪里了？如果这个答案对你没有帮助，就打开另一个问题
def create_classifier(self):
        hiddenLayers = [self.num_points, self.num_points * 2, 10]
        self.classifier = tf.contrib.learn.DNNClassifier(hidden_units=hiddenLayers)

[[1,2],[2,3],[3,4],...]


def train(self, label, point_list):
    points = self.resample(point_list, self.num_points)
    utils.strip_ids_from_points(points)
    value_class = 1 if label == self.label else 0
    target = tf.reshape(tf.constant(value_class), [1])
    print 'training classifier to recognize value as: [' + str(value_class) + '] label is ' + label + ' class is ' + self.label
    point_tensor = tf.convert_to_tensor(points, dtype=tf.float32)
    reshaped_tensor = tf.reshape(point_tensor, [1, self.num_points * 2])
    print reshaped_tensor
    print target
    self.classifier.fit(x=reshaped_tensor, y=target, steps=1)