tf.raw_ops.RaggedCross
Stay organized with collections
Save and categorize content based on your preferences.
Generates a feature cross from a list of tensors, and returns it as a RaggedTensor.
tf.raw_ops.RaggedCross(
ragged_values,
ragged_row_splits,
sparse_indices,
sparse_values,
sparse_shape,
dense_inputs,
input_order,
hashed_output,
num_buckets,
hash_key,
out_values_type,
out_row_splits_type,
name=None
)
See tf.ragged.cross for more details.
Args:
ragged_values: A list of Tensor objects with types from: int64, string.
The values tensor for each RaggedTensor input.
ragged_row_splits: A list of Tensor objects with types from: int32, int64.
The row_splits tensor for each RaggedTensor input.
sparse_indices: A list of Tensor objects with type int64.
The indices tensor for each SparseTensor input.
sparse_values: A list of Tensor objects with types from: int64, string.
The values tensor for each SparseTensor input.
sparse_shape: A list with the same length as sparse_indices of Tensor objects with type int64.
The dense_shape tensor for each SparseTensor input.
dense_inputs: A list of Tensor objects with types from: int64, string.
The tf.Tensor inputs.
input_order: A string.
String specifying the tensor type for each input. The ith character in
this string specifies the type of the ith input, and is one of: 'R' (ragged),
'D' (dense), or 'S' (sparse). This attr is used to ensure that the crossed
values are combined in the order of the inputs from the call to tf.ragged.cross.
hashed_output: A bool.
num_buckets: An int that is >= 0.
hash_key: An int.
out_values_type: A tf.DType from: tf.int64, tf.string.
out_row_splits_type: A tf.DType from: tf.int32, tf.int64.
name: A name for the operation (optional).
Returns:
A tuple of Tensor objects (output_values, output_row_splits).
output_values: A `Tensor` of type `out_values_type`.
output_row_splits: A `Tensor` of type `out_row_splits_type`.
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.raw_ops.RaggedCross\n\n\u003cbr /\u003e\n\nGenerates a feature cross from a list of tensors, and returns it as a RaggedTensor.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.RaggedCross`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/RaggedCross)\n\n\u003cbr /\u003e\n\n tf.raw_ops.RaggedCross(\n ragged_values,\n ragged_row_splits,\n sparse_indices,\n sparse_values,\n sparse_shape,\n dense_inputs,\n input_order,\n hashed_output,\n num_buckets,\n hash_key,\n out_values_type,\n out_row_splits_type,\n name=None\n )\n\nSee [`tf.ragged.cross`](../../tf/ragged/cross) for more details.\n\nArgs:\nragged_values: A list of `Tensor` objects with types from: `int64`, `string`.\nThe values tensor for each RaggedTensor input.\nragged_row_splits: A list of `Tensor` objects with types from: `int32`, `int64`.\nThe row_splits tensor for each RaggedTensor input.\nsparse_indices: A list of `Tensor` objects with type `int64`.\nThe indices tensor for each SparseTensor input.\nsparse_values: A list of `Tensor` objects with types from: `int64`, `string`.\nThe values tensor for each SparseTensor input.\nsparse_shape: A list with the same length as `sparse_indices` of `Tensor` objects with type `int64`.\nThe dense_shape tensor for each SparseTensor input.\ndense_inputs: A list of `Tensor` objects with types from: `int64`, `string`.\nThe tf.Tensor inputs.\ninput_order: A `string`.\nString specifying the tensor type for each input. The `i`th character in\nthis string specifies the type of the `i`th input, and is one of: 'R' (ragged),\n'D' (dense), or 'S' (sparse). This attr is used to ensure that the crossed\nvalues are combined in the order of the inputs from the call to tf.ragged.cross.\nhashed_output: A `bool`.\nnum_buckets: An `int` that is `\u003e= 0`.\nhash_key: An `int`.\nout_values_type: A [`tf.DType`](../../tf/dtypes/DType) from: `tf.int64, tf.string`.\nout_row_splits_type: A [`tf.DType`](../../tf/dtypes/DType) from: `tf.int32, tf.int64`.\nname: A name for the operation (optional).\n\nReturns:\nA tuple of `Tensor` objects (output_values, output_row_splits). \n\n output_values: A `Tensor` of type `out_values_type`.\n output_row_splits: A `Tensor` of type `out_row_splits_type`."]]
