# tf.ragged.map_flat_values

Applies op to the flat_values of one or more RaggedTensors.

### Used in the notebooks

Used in the guide

Replaces any RaggedTensor in args or kwargs with its flat_values tensor (which collapses all ragged dimensions), and then calls op. Returns a RaggedTensor that is constructed from the input RaggedTensors' nested_row_splits and the value returned by the op.

If the input arguments contain multiple RaggedTensors, then they must have identical nested_row_splits.

This operation is generally used to apply elementwise operations to each value in a RaggedTensor.

#### Examples:

rt = tf.ragged.constant([[1, 2, 3], [], [4, 5], [6]])
tf.ragged.map_flat_values(tf.ones_like, rt)
<tf.RaggedTensor [[1, 1, 1], [], [1, 1], [1]]>
tf.ragged.map_flat_values(tf.multiply, rt, rt)
<tf.RaggedTensor [[1, 4, 9], [], [16, 25], [36]]>
<tf.RaggedTensor [[6, 7, 8], [], [9, 10], [11]]>

Example with a non-elementwise operation (note that map_flat_values and map_fn return different results):

rt = tf.ragged.constant([[1.0, 3.0], [], [3.0, 6.0, 3.0]])
def normalized(x):
return x / tf.reduce_sum(x)
tf.ragged.map_flat_values(normalized, rt)
<tf.RaggedTensor [[0.0625, 0.1875], [], [0.1875, 0.375, 0.1875]]>
tf.map_fn(normalized, rt)
<tf.RaggedTensor [[0.25, 0.75], [], [0.25, 0.5, 0.25]]>

op The operation that should be applied to the RaggedTensor flat_values. op is typically an element-wise operation (such as math_ops.add), but any operation that preserves the size of the outermost dimension can be used. I.e., shape[0] of the value returned by op must match shape[0] of the RaggedTensors' flat_values tensors.
*args Arguments for op.
**kwargs Keyword arguments for op.

A RaggedTensor whose ragged_rank matches the ragged_rank of all input RaggedTensors.

ValueError If args contains no RaggedTensors, or if the nested_splits of the input RaggedTensors are not identical.

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