Python tensorflow.python.ops.string_ops.string_to_hash_bucket_fast() Examples

The following are 15 code examples of tensorflow.python.ops.string_ops.string_to_hash_bucket_fast(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.ops.string_ops , or try the search function .
Example #1
Source File: feature_column.py    From lambda-packs with MIT License 6 votes vote down vote up 
def _transform_feature(self, inputs):
    input_tensor = _to_sparse_input(inputs.get(self.key))
    if not isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
      raise ValueError('SparseColumn input must be a SparseTensor.')

    _assert_string_or_int(
        input_tensor.dtype,
        prefix='column_name: {} input_tensor'.format(self.key))

    if self.dtype.is_integer != input_tensor.dtype.is_integer:
      raise ValueError(
          'Column dtype and SparseTensors dtype must be compatible. '
          'key: {}, column dtype: {}, tensor dtype: {}'.format(
              self.key, self.dtype, input_tensor.dtype))

    if self.dtype == dtypes.string:
      sparse_values = input_tensor.values
    else:
      sparse_values = string_ops.as_string(input_tensor.values)

    sparse_id_values = string_ops.string_to_hash_bucket_fast(
        sparse_values, self.hash_bucket_size, name='lookup')
    return sparse_tensor_lib.SparseTensor(
        input_tensor.indices, sparse_id_values, input_tensor.dense_shape)
Example #2
Source File: hashes.py    From lambda-packs with MIT License 6 votes vote down vote up 
def _apply_transform(self, input_tensors, **kwargs):
    """Applies the transformation to the `transform_input`.

    Args:
      input_tensors: a list of Tensors representing the input to
        the Transform.
      **kwargs: additional keyword arguments, unused here.

    Returns:
        A namedtuple of Tensors representing the transformed output.
    """
    result = string_ops.string_to_hash_bucket_fast(input_tensors[0],
                                                   self._num_buckets,
                                                   name=None)
    # pylint: disable=not-callable
    return self.return_type(result)
Example #3
Source File: hashes.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up 
def _apply_transform(self, input_tensors, **kwargs):
    """Applies the transformation to the `transform_input`.

    Args:
      input_tensors: a list of Tensors representing the input to
        the Transform.
      **kwargs: additional keyword arguments, unused here.

    Returns:
        A namedtuple of Tensors representing the transformed output.
    """
    result = string_ops.string_to_hash_bucket_fast(input_tensors[0],
                                                   self._num_buckets,
                                                   name=None)
    # pylint: disable=not-callable
    return self.return_type(result)
Example #4
Source File: hashes.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
def _apply_transform(self, input_tensors, **kwargs):
    """Applies the transformation to the `transform_input`.

    Args:
      input_tensors: a list of Tensors representing the input to
        the Transform.
      **kwargs: additional keyword arguments, unused here.

    Returns:
        A namedtuple of Tensors representing the transformed output.
    """
    result = string_ops.string_to_hash_bucket_fast(input_tensors[0],
                                                   self._num_buckets,
                                                   name=None)
    # pylint: disable=not-callable
    return self.return_type(result)
Example #5
Source File: feature_column.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 6 votes vote down vote up 
def _transform_feature(self, inputs):
    input_tensor = _to_sparse_input(inputs.get(self.key))
    if not isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
      raise ValueError('SparseColumn input must be a SparseTensor.')

    _assert_string_or_int(
        input_tensor.dtype,
        prefix='column_name: {} input_tensor'.format(self.key))

    if self.dtype.is_integer != input_tensor.dtype.is_integer:
      raise ValueError(
          'Column dtype and SparseTensors dtype must be compatible. '
          'key: {}, column dtype: {}, tensor dtype: {}'.format(
              self.key, self.dtype, input_tensor.dtype))

    if self.dtype == dtypes.string:
      sparse_values = input_tensor.values
    else:
      sparse_values = string_ops.as_string(input_tensor.values)

    sparse_id_values = string_ops.string_to_hash_bucket_fast(
        sparse_values, self.hash_bucket_size, name='lookup')
    return sparse_tensor_lib.SparseTensor(
        input_tensor.indices, sparse_id_values, input_tensor.dense_shape)
Example #6
Source File: hashes.py    From keras-lambda with MIT License 6 votes vote down vote up 
def _apply_transform(self, input_tensors, **kwargs):
    """Applies the transformation to the `transform_input`.

    Args:
      input_tensors: a list of Tensors representing the input to
        the Transform.
      **kwargs: additional keyword arguments, unused here.

    Returns:
        A namedtuple of Tensors representing the transformed output.
    """
    result = string_ops.string_to_hash_bucket_fast(input_tensors[0],
                                                   self._num_buckets,
                                                   name=None)
    # pylint: disable=not-callable
    return self.return_type(result)
Example #7
Source File: lookup_ops.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _get_string_to_hash_bucket_fn(self, hasher_spec):
    """Returns the string_to_hash_bucket op to use based on `hasher_spec`."""
    if not isinstance(hasher_spec, HasherSpec):
      raise TypeError("hasher_spec must be of type HasherSpec %s" % hasher_spec)
    if hasher_spec.hasher == "fasthash":
      return string_ops.string_to_hash_bucket_fast
    if hasher_spec.hasher == "legacy":
      return string_ops.string_to_hash_bucket
    if hasher_spec.hasher == "stronghash":
      return functools.partial(
          string_ops.string_to_hash_bucket_strong, key=hasher_spec.key)
    raise ValueError("Unknown hasher %s" % hasher_spec.hasher)
Example #8
Source File: feature_column.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _do_transform(self, input_tensor):
    if self.dtype.is_integer:
      sparse_values = string_ops.as_string(input_tensor.values)
    else:
      sparse_values = input_tensor.values

    sparse_id_values = string_ops.string_to_hash_bucket_fast(
        sparse_values, self.bucket_size, name="lookup")
    return sparse_tensor_py.SparseTensor(input_tensor.indices, sparse_id_values,
                                         input_tensor.dense_shape)
Example #9
Source File: lookup_ops.py    From lambda-packs with MIT License 5 votes vote down vote up 
def _get_string_to_hash_bucket_fn(self, hasher_spec):
    """Returns the string_to_hash_bucket op to use based on `hasher_spec`."""
    if not isinstance(hasher_spec, HasherSpec):
      raise TypeError("hasher_spec must be of type HasherSpec %s" % hasher_spec)
    if hasher_spec.hasher == "fasthash":
      return string_ops.string_to_hash_bucket_fast
    if hasher_spec.hasher == "legacy":
      return string_ops.string_to_hash_bucket
    if hasher_spec.hasher == "stronghash":
      return functools.partial(
          string_ops.string_to_hash_bucket_strong, key=hasher_spec.key)
    raise ValueError("Unknown hasher %s" % hasher_spec.hasher)
Example #10
Source File: feature_column.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def insert_transformed_feature(self, columns_to_tensors):
    """Handles sparse column to id conversion."""
    input_tensor = self._get_input_sparse_tensor(columns_to_tensors)

    if self.dtype.is_integer:
      sparse_values = string_ops.as_string(input_tensor.values)
    else:
      sparse_values = input_tensor.values

    sparse_id_values = string_ops.string_to_hash_bucket_fast(
        sparse_values, self.bucket_size, name="lookup")
    columns_to_tensors[self] = sparse_tensor_py.SparseTensor(
        input_tensor.indices, sparse_id_values, input_tensor.dense_shape)
Example #11
Source File: lookup_ops.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
def _get_string_to_hash_bucket_fn(self, hasher_spec):
    """Returns the string_to_hash_bucket op to use based on `hasher_spec`."""
    if not isinstance(hasher_spec, HasherSpec):
      raise TypeError("hasher_spec must be of type HasherSpec %s" % hasher_spec)
    if hasher_spec.hasher == "fasthash":
      return string_ops.string_to_hash_bucket_fast
    if hasher_spec.hasher == "legacy":
      return string_ops.string_to_hash_bucket
    if hasher_spec.hasher == "stronghash":
      return functools.partial(
          string_ops.string_to_hash_bucket_strong, key=hasher_spec.key)
    raise ValueError("Unknown hasher %s" % hasher_spec.hasher)
Example #12
Source File: feature_column.py    From deep_image_model with Apache License 2.0 5 votes vote down vote up 
def insert_transformed_feature(self, columns_to_tensors):
    """Handles sparse column to id conversion."""
    sparse_tensor = columns_to_tensors[self.name]
    if self.dtype.is_integer:
      sparse_values = string_ops.as_string(sparse_tensor.values)
    else:
      sparse_values = sparse_tensor.values

    sparse_id_values = string_ops.string_to_hash_bucket_fast(
        sparse_values, self.bucket_size, name="lookup")
    columns_to_tensors[self] = sparse_tensor_py.SparseTensor(
        sparse_tensor.indices, sparse_id_values, sparse_tensor.shape)
Example #13
Source File: lookup_ops.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 5 votes vote down vote up 
def _get_string_to_hash_bucket_fn(self, hasher_spec):
    """Returns the string_to_hash_bucket op to use based on `hasher_spec`."""
    if not isinstance(hasher_spec, HasherSpec):
      raise TypeError("hasher_spec must be of type HasherSpec %s" % hasher_spec)
    if hasher_spec.hasher == "fasthash":
      return string_ops.string_to_hash_bucket_fast
    if hasher_spec.hasher == "legacy":
      return string_ops.string_to_hash_bucket
    if hasher_spec.hasher == "stronghash":
      return functools.partial(
          string_ops.string_to_hash_bucket_strong, key=hasher_spec.key)
    raise ValueError("Unknown hasher %s" % hasher_spec.hasher)
Example #14
Source File: feature_column.py    From keras-lambda with MIT License 5 votes vote down vote up 
def insert_transformed_feature(self, columns_to_tensors):
    """Handles sparse column to id conversion."""
    input_tensor = self._get_input_sparse_tensor(columns_to_tensors)

    if self.dtype.is_integer:
      sparse_values = string_ops.as_string(input_tensor.values)
    else:
      sparse_values = input_tensor.values

    sparse_id_values = string_ops.string_to_hash_bucket_fast(
        sparse_values, self.bucket_size, name="lookup")
    columns_to_tensors[self] = sparse_tensor_py.SparseTensor(
        input_tensor.indices, sparse_id_values, input_tensor.dense_shape)
Example #15
Source File: lookup_ops.py    From keras-lambda with MIT License 5 votes vote down vote up 
def _get_string_to_hash_bucket_fn(self, hasher_spec):
    """Returns the string_to_hash_bucket op to use based on `hasher_spec`."""
    if not isinstance(hasher_spec, HasherSpec):
      raise TypeError("hasher_spec must be of type HasherSpec %s" % hasher_spec)
    if hasher_spec.hasher == "fasthash":
      return string_ops.string_to_hash_bucket_fast
    if hasher_spec.hasher == "legacy":
      return string_ops.string_to_hash_bucket
    if hasher_spec.hasher == "stronghash":
      return functools.partial(
          string_ops.string_to_hash_bucket_strong, key=hasher_spec.key)
    raise ValueError("Unknown hasher %s" % hasher_spec.hasher)