Create a list of partitioned variables according to the given
Compat aliases for migration
See Migration guide for more details.
tf.create_partitioned_variables( shape, slicing, initializer, dtype=tf.dtypes.float32, trainable=True, collections=None, name=None, reuse=None )
Currently only one dimension of the full variable can be sliced, and the full variable can be reconstructed by the concatenation of the returned list along that dimension.
| ||List of integers. The shape of the full variable.|
| || List of integers. How to partition the variable. Must be of the same length as |
For convenience, The requested number of partitions does not have to divide the corresponding dimension evenly. If it does not, the shapes of the partitions are incremented by 1 starting from partition 0 until all slack is absorbed. The adjustment rules may change in the future, but as you can save/restore these variables with different slicing specifications this should not be a problem.
| || A |
| || Type of the variables. Ignored if |
| || If True also add all the variables to the graph collection |
| || List of graph collections keys to add the variables to. Defaults to |
| || Optional name for the full variable. Defaults to |
| || Boolean or |
|A list of Variables corresponding to the slicing.|
| ||If any of the arguments is malformed.|
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