我在github上提出了一个问题：https://github.com/tensorflow/tensorflow/issues/14924。这是详细信息。
没关系：
import tensorflow as tf
sess = tf.InteractiveSession()
xx = tf.constant(1, shape=[32,1,4,4,1], dtype=tf.float32)
yy = tf.constant(1, shape=[1,32,1,4,4], dtype=tf.float32)
zz = xx * yy
sess.run([zz])
然而：
x2 = tf.constant(1, shape=[10,32,1,4,4,1])
y2 = tf.constant(1, shape=[10,1,32,1,4,4])
z2 = x2 * y2
sess.run(z2)
给出错误：
UnimplementedError (see above for traceback): Broadcast between [10,32,1,4,4,1] and [10,1,32,1,4,4] is not supported yet. [[Node: mul_1 = Mul[T=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Const_2, Const_3)]]
日志：
---------------------------------------------------------------------------
UnimplementedError Traceback (most recent call last)
<ipython-input-2-eef82717f8d8> in <module>()
2 y2 = tf.constant(1, shape=[10,1,32,1,4,4])
3 z2 = x2 * y2
----> 4 sess.run(z2)
/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
887 try:
888 result = self._run(None, fetches, feed_dict, options_ptr,
--> 889 run_metadata_ptr)
890 if run_metadata:
891 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
1118 if final_fetches or final_targets or (handle and feed_dict_tensor):
1119 results = self._do_run(handle, final_targets, final_fetches,
-> 1120 feed_dict_tensor, options, run_metadata)
1121 else:
1122 results = []
/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1315 if handle is None:
1316 return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1317 options, run_metadata)
1318 else:
1319 return self._do_call(_prun_fn, self._session, handle, feeds, fetches)
/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
1334 except KeyError:
1335 pass
-> 1336 raise type(e)(node_def, op, message)
1337
1338 def _extend_graph(self):
UnimplementedError: Broadcast between [10,32,1,4,4,1] and [10,1,32,1,4,4] is not supported yet.
[[Node: mul_1 = Mul[T=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Const_2, Const_3)]]
Caused by op u'mul_1', defined at:
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/runpy.py", line 174, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/ipykernel/__main__.py", line 3, in <module>
app.launch_new_instance()
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/ipykernel/kernelapp.py", line 474, in start
ioloop.IOLoop.instance().start()
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/tornado/ioloop.py", line 887, in start
handler_func(fd_obj, events)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 276, in dispatcher
return self.dispatch_shell(stream, msg)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
handler(stream, idents, msg)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 390, in execute_request
user_expressions, allow_stdin)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/ipykernel/zmqshell.py", line 501, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2821, in run_ast_nodes
if self.run_code(code, result):
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-2-eef82717f8d8>", line 3, in <module>
z2 = x2 * y2
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 894, in binary_op_wrapper
return func(x, y, name=name)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 1117, in _mul_dispatch
return gen_math_ops._mul(x, y, name=name)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 2726, in _mul
"Mul", x=x, y=y, name=name)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
op_def=op_def)
File "/home/jetadmin/anaconda2/envs/ygtf/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1470, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
UnimplementedError (see above for traceback): Broadcast between [10,32,1,4,4,1] and [10,1,32,1,4,4] is not supported yet.
[[Node: mul_1 = Mul[T=DT_INT32, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Const_2, Const_3)]]
更新：
我认为原因与维度的匹配方式有关，而不是维度的总数或不匹配的数量。因为以下脚本运行正常，其中x3的倒数第二个维度从4更改为1，再添加一个不匹配的位置。
x3 = tf.constant(1, shape=[10,32,1,4,1,1])
y3 = tf.constant(1, shape=[10,1,32,1,4,4])
z3 = x3 * y3
sess.run(z3)
答案 0 :(得分：1)
正如您可能已经观察到的那样，目前Tensorflow已限制尺寸不匹配的数量，它将纠正广播。
为此，我编写了自己的广播功能，将可变数量的张量广播到一个共同的形状。但请注意，如果未定义张量的形状或其形状包含None，则此功能将无效。
def broadcast_tensors(tensors):
shapes = [t.get_shape().as_list() for t in tensors]
max_rank = max([len(s) for s in shapes])
# Rank equalize all the tensors
for index in range(len(shapes)):
shape = shapes[index]
if len(shape) == max_rank:
continue
tensor = tensors[index]
for _ in range(max_rank - len(shape)):
shape.insert(0, 1)
tensor = tf.expand_dims(tensor, axis = 0)
tensors[index] = tensor
# Ensure if broadcasting is possible
from collections import Counter
broadcast_shape = []
for index in range(max_rank):
dimensions = [s[index] for s in shapes]
repeats = Counter(dimensions)
if len(repeats) > 2 or (len(repeats) == 2 and \
1 not in list(repeats.keys())):
raise Exception("Broadcasting not possible")
broadcast_shape.append(max(repeats.keys()))
# Broadcast the tensors
for axis, dimension in enumerate(broadcast_shape):
tensors = [tf.concat([t] * dimension, axis = axis) \
if t.get_shape()[axis] == 1 else t for t in tensors]
return tensors
输出：
x = tf.constant(1, shape = [10, 32, 1, 4, 4, 1])
y = tf.constant(1, shape = [1, 32, 1, 4, 1])
z = tf.constant(1, shape = [32, 4, 1, 1])
x, y, z = broadcast_tensors([x, y, z])
print(x.get_shape(), y.get_shape(), z.get_shape())
# (10, 32, 32, 4, 4, 1) (10, 32, 32, 4, 4, 1) (10, 32, 32, 4, 4, 1)
x = tf.constant(1, shape = [10, 32, 1, 4, 4, 1])
y = tf.constant(1, shape = [1, 32, 3, 4, 2])
z = tf.constant(1, shape = [32, 3, 1, 3])
x, y, z = broadcast_tensors([x, y, z])
# Exception: Broadcasting not possible