TensorFlow中如何确定张量的形状实例

2020-06-23 16:00 来源:易采站长站 作者:王冬梅 点击: 评论:

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原标题:TensorFlow中如何确定张量的形状实例

我们可以使用tf.shape()获取某张量的形状张量。

import tensorflow as tf
x = tf.reshape(tf.range(1000), [10, 10, 10])
sess = tf.Session()
sess.run(tf.shape(x))
 
Out[1]: array([10, 10, 10])

我们可以使用tf.shape()在计算图中确定改变张量的形状。

high = tf.shape(x)[0] // 2
width = tf.shape(x)[1] * 2
x_reshape = tf.reshape(x, [high, width, -1])
sess.run(tf.shape(x_reshape))
 
Out: array([ 5, 20, 10])

我们可以使用tf.shape_n()在计算图中得到若干个张量的形状。

y = tf.reshape(tf.range(504), [7,8,9])
sess.run(tf.shape_n([x, y]))
 
Out: [array([10, 10, 10]), array([7, 8, 9])]

我们可以使用tf.size()获取张量的元素个数。

sess.run([tf.size(x), tf.size(y)])

Out: [1000, 504]

tensor.get_shape()或者tensor.shape是无法在计算图中用于确定张量的形状。

In [20]: x.get_shape()
Out[20]: TensorShape([Dimension(10), Dimension(10), Dimension(10)])
 
In [21]: x.get_shape()[0]
Out[21]: Dimension(10)
 
In [22]: type(x.get_shape()[0])
Out[22]: tensorflow.python.framework.tensor_shape.Dimension
 
In [23]: x.get_shape()
Out[23]: TensorShape([Dimension(10), Dimension(10), Dimension(10)])
 
In [24]: sess.run(x.get_shape())
---------------------------------------------------------------------------
TypeError     Traceback (most recent call last)
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in __init__(self, fetches, contraction_fn)
 299  self._unique_fetches.append(ops.get_default_graph().as_graph_element(
--> 300  fetch, allow_tensor=True, allow_operation=True))
 301 except TypeError as e:
 
~\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in as_graph_element(self, obj, allow_tensor, allow_operation)
 3477 with self._lock:
-> 3478 return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
 3479
 
~\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in _as_graph_element_locked(self, obj, allow_tensor, allow_operation)
 3566 raise TypeError("Can not convert a %s into a %s." % (type(obj).__name__,
-> 3567        types_str))
 3568
 
TypeError: Can not convert a TensorShapeV1 into a Tensor or Operation.
 
During handling of the above exception, another exception occurred:
 
TypeError     Traceback (most recent call last)
<ipython-input-24-de007c69e003> in <module>
----> 1 sess.run(x.get_shape())
 
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
 927 try:
 928 result = self._run(None, fetches, feed_dict, options_ptr,
--> 929    run_metadata_ptr)
 930 if run_metadata:
 931  proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
 
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
 1135 # Create a fetch handler to take care of the structure of fetches.
 1136 fetch_handler = _FetchHandler(
-> 1137  self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)
 1138
 1139 # Run request and get response.
 
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in __init__(self, graph, fetches, feeds, feed_handles)
 469 """
 470 with graph.as_default():
--> 471 self._fetch_mapper = _FetchMapper.for_fetch(fetches)
 472 self._fetches = []
 473 self._targets = []
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in for_fetch(fetch)
 269  if isinstance(fetch, tensor_type):
 270  fetches, contraction_fn = fetch_fn(fetch)
--> 271  return _ElementFetchMapper(fetches, contraction_fn)
 272 # Did not find anything.
 273 raise TypeError('Fetch argument %r has invalid type %r' % (fetch,
~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in __init__(self, fetches, contraction_fn)
 302  raise TypeError('Fetch argument %r has invalid type %r, '
 303    'must be a string or Tensor. (%s)' %
--> 304    (fetch, type(fetch), str(e)))
 305 except ValueError as e:
 306  raise ValueError('Fetch argument %r cannot be interpreted as a '
TypeError: Fetch argument TensorShape([Dimension(10), Dimension(10), Dimension(10)]) has invalid type <class 'tensorflow.python.framework.tensor_shape.TensorShapeV1'>, must be a string or Tensor. (Can not convert a TensorShapeV1 into a Tensor or Operation.)

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