Table Of Contents
Table Of Contents


Symbol.infer_shape(*args, **kwargs)[source]

Infers the shapes of all arguments and all outputs given the known shapes of some arguments.

This function takes the known shapes of some arguments in either positional way or keyword argument way as input. It returns a tuple of None values if there is not enough information to deduce the missing shapes.


>>> a = mx.sym.var('a')
>>> b = mx.sym.var('b')
>>> c = a + b
>>> arg_shapes, out_shapes, aux_shapes = c.infer_shape(a=(3,3))
>>> arg_shapes
[(3L, 3L), (3L, 3L)]
>>> out_shapes
[(3L, 3L)]
>>> aux_shapes
>>> c.infer_shape(a=(0,3)) # 0s in shape means unknown dimensions. So, returns None.
(None, None, None)

Inconsistencies in the known shapes will cause an error to be raised. See the following example:

>>> data = mx.sym.Variable('data')
>>> out = mx.sym.FullyConnected(data=data, name='fc1', num_hidden=1000)
>>> out = mx.sym.Activation(data=out, act_type='relu')
>>> out = mx.sym.FullyConnected(data=out, name='fc2', num_hidden=10)
>>> weight_shape= (1, 100)
>>> data_shape = (100, 100)
>>> out.infer_shape(data=data_shape, fc1_weight=weight_shape)
Error in operator fc1: Shape inconsistent, Provided=(1,100), inferred shape=(1000,100)
  • *args – Shape of arguments in a positional way. Unknown shape can be marked as None.
  • **kwargs – Keyword arguments of the known shapes.

  • arg_shapes (list of tuple or None) – List of argument shapes. The order is same as the order of list_arguments().
  • out_shapes (list of tuple or None) – List of output shapes. The order is same as the order of list_outputs().
  • aux_shapes (list of tuple or None) – List of auxiliary state shapes. The order is same as the order of list_auxiliary_states().