Python TensorFlow：AttributeError：“张量”对象没有“形状”属性
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TensorFlow: AttributeError: 'Tensor' object has no attribute 'shape'
提问by Omar Shehab
I have the following code which uses TensorFlow. After I reshape a list, it says
我有以下使用 TensorFlow 的代码。在我重塑列表后，它说
AttributeError: 'Tensor' object has no attribute 'shape'
AttributeError: 'Tensor' 对象没有属性 'shape'
when I try to print its shape.
当我尝试打印它的形状时。
# Get the shape of the training data.
print "train_data.shape: " + str(train_data.shape)
train_data = tf.reshape(train_data, [400, 1])
print "train_data.shape: " + str(train_data.shape)
train_size,num_features = train_data.shape
Output:
输出：
train_data.shape: (400,) Traceback (most recent call last): File "", line 1, in File "/home/shehab/Downloads/tools/python/pycharm-edu-2.0.4/helpers/pydev/pydev_import_hook.py", line 21, in do_import module = self._system_import(name, *args, **kwargs) File "/home/shehab/Dropbox/py-projects/try-tf/logistic_regression.py", line 77, in print "train_data.shape: " + str(train_data.shape) AttributeError: 'Tensor' object has no attribute 'shape'
train_data.shape: (400,) 回溯（最近一次调用）：文件“”，第 1 行，文件“/home/shehab/Downloads/tools/python/pycharm-edu-2.0.4/helpers/pydev/pydev_import_hook .py”，第 21 行，在 do_import 模块 = self._system_import(name, *args, **kwargs) 文件“/home/shehab/Dropbox/py-projects/try-tf/logistic_regression.py”，第 77 行，在打印“train_data.shape:” + str(train_data.shape) AttributeError: 'Tensor' 对象没有属性 'shape'
Could anyone please tell me what I am missing?
谁能告诉我我错过了什么？
回答by mrry
UPDATE:Since TensorFlow 1.0, tf.Tensornow has a tf.Tensor.shapeproperty, which returns the same value as tf.Tensor.get_shape().
更新：从 TensorFlow 1.0 开始，tf.Tensor现在有一个tf.Tensor.shape属性，它返回与tf.Tensor.get_shape().
Indeed, in versions prior to TensorFlow 1.0 tf.Tensordoesn't have a .shapeproperty. You should use the Tensor.get_shape()method instead:
实际上，在 TensorFlow 1.0 之前的版本tf.Tensor中没有.shape属性。您应该改用该Tensor.get_shape()方法：
train_data = tf.reshape(train_data, [400, 1])
print "train_data.shape: " + str(train_data.get_shape())
Note that in general you might not be able to get the actual shape of the result of a TensorFlow operation. In some cases, the shape will be a computed value that depends on running the computation to find its value; and it may even vary from one run to the next (e.g. the shape of tf.unique()). In that case, the result of get_shape()for some dimensions may be None(or "?").
请注意，通常您可能无法获得 TensorFlow 操作结果的实际形状。在某些情况下，形状将是一个计算值，它依赖于运行计算来找到它的值；它甚至可能会因一次运行而异（例如 的形状tf.unique()）。在这种情况下，get_shape()某些维度的结果可能是None（或"?"）。
回答by Sahil Unagar
import tensorflow as tf
and replace train_data.shapewith tf.Session.run(tf.rank(train_data))
并替换train_data.shape为tf.Session.run(tf.rank(train_data))
回答by rosefun
Use tf.shape(tensor), or tf.get_shape(tensor).
使用tf.shape(tensor), 或tf.get_shape(tensor)。
