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] // 2width = 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\ensorflow\\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\ensorflow\\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\ensorflow\\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) in ----> 1 sess.run(x.get_shape())

~\\Anaconda3\\lib\\site-packages\ensorflow\\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\ensorflow\\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\ensorflow\\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\ensorflow\\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\ensorflow\\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 , must be a string or Tensor. (Can not convert a TensorShapeV1 into a Tensor or Op

我们可以使⽤tf.rank()来确定张量的秩。tf.rank()会返回⼀个代表张量秩的张量，可直接在计算图中使⽤。

In [25]: tf.rank(x)

Out[25]:

In [26]: sess.run(tf.rank(x))Out[26]: 3

补充知识：tensorflow循环改变tensor的值使⽤tf.concat()实现4维tensor的循环赋值

alist=[[[[1,1,1],[2,2,2],[3,3,3]],[[4,4,4],[5,5,5],[6,6,6]]],[[[7,7,7],[8,8,8],[9,9,9]],[[10,10,10],[11,11,11],[12,12,12]]]] #2,2,3,3-n,c,h,w

kenel=(np.asarray(alist)*2).tolist()print(kenel)

inputs=tf.constant(alist,dtype=tf.float32)kenel=tf.constant(kenel,dtype=tf.float32)inputs=tf.transpose(inputs,[0,2,3,1]) #n,h,w,ckenel=tf.transpose(kenel,[0,2,3,1]) #n,h,w,cuints=inputs.get_shape()h=int(uints[1])w=int(uints[2])encoder_output=[]

for b in range(int(uints[0])): encoder_output_c=[]

for c in range(int(uints[-1])):

one_channel_in = inputs[b, :, :, c]

one_channel_in = tf.reshape(one_channel_in, [1, h, w, 1]) one_channel_kernel = kenel[b, :, :, c]

one_channel_kernel = tf.reshape(one_channel_kernel, [h, w, 1, 1])

encoder_output_cc = tf.nn.conv2d(input=one_channel_in, filter=one_channel_kernel, strides=[1, 1, 1, 1], padding=\"SAME\") if c==0:

encoder_output_c=encoder_output_cc else:

encoder_output_c=tf.concat([encoder_output_c,encoder_output_cc],axis=3) if b==0:

encoder_output=encoder_output_c else:

encoder_output = tf.concat([encoder_output, encoder_output_c], axis=0)with tf.Session() as sess:

print(sess.run(tf.transpose(encoder_output,[0,3,1,2]))) print(encoder_output.get_shape())

输出：

[[[[ 32. 48. 32.] [ 56. 84. 56.] [ 32. 48. 32.]] [[ 200. 300. 200.] [ 308. 462. 308.] [ 200. 300. 200.]]]

[[[ 512. 768. 512.] [ 776. 11. 776.] [ 512. 768. 512.]] [[ 968. 1452. 968.] [1460. 2190. 1460.] [ 968. 1452. 968.]]]](2, 3, 3, 2)

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