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Create a list of partitioned variables according to the given
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.
shape: List of integers. The shape of the full variable.
slicing: List of integers. How to partition the variable. Must be of the same length as
shape. Each value indicate how many slices to create in the corresponding dimension. Presently only one of the values can be more than 1; that is, the variable can only be sliced along one dimension.
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.
shapeor a variable initializer function. If a function, it will be called once for each slice, passing the shape and data type of the slice as parameters. The function must return a tensor with the same shape as the slice.
dtype: Type of the variables. Ignored if
trainable: If True also add all the variables to the graph collection
collections: List of graph collections keys to add the variables to. Defaults to
name: Optional name for the full variable. Defaults to
"PartitionedVariable"and gets uniquified automatically.
reuse: Boolean or
Trueand name is set, it would reuse previously created variables. if
Falseit will create new variables. if
None, it would inherit the parent scope reuse.
A list of Variables corresponding to the slicing.
ValueError: If any of the arguments is malformed.