TensorFlow 1 version |
Ragged Tensors.
This package defines ops for manipulating ragged tensors (tf.RaggedTensor
),
which are tensors with non-uniform shapes. In particular, each RaggedTensor
has one or more ragged dimensions, which are dimensions whose slices may have
different lengths. For example, the inner (column) dimension of
rt=[[3, 1, 4, 1], [], [5, 9, 2], [6], []]
is ragged, since the column slices
(rt[0, :]
, ..., rt[4, :]
) have different lengths. For a more detailed
description of ragged tensors, see the tf.RaggedTensor
class documentation
and the Ragged Tensor Guide.
Additional ops that support RaggedTensor
Arguments that accept RaggedTensor
s are marked in bold.
tf.batch_gather
(params, indices, name=None
)tf.bitwise.bitwise_and
(x, y, name=None
)tf.bitwise.bitwise_or
(x, y, name=None
)tf.bitwise.bitwise_xor
(x, y, name=None
)tf.bitwise.invert
(x, name=None
)tf.bitwise.left_shift
(x, y, name=None
)tf.bitwise.right_shift
(x, y, name=None
)tf.cast
(x, dtype, name=None
)tf.clip_by_value
(t, clip_value_min, clip_value_max, name=None
)tf.concat
(values, axis, name='concat'
)tf.debugging.check_numerics
(tensor, message, name=None
)tf.dtypes.complex
(real, imag, name=None
)tf.dtypes.saturate_cast
(value, dtype, name=None
)tf.dynamic_partition
(data, partitions, num_partitions, name=None
)tf.expand_dims
(input, axis=None
, name=None
, dim=None
)tf.gather_nd
(params, indices, name=None
, batch_dims=0
)tf.gather
(params, indices, validate_indices=None
, name=None
, axis=None
, batch_dims=0
)tf.identity
(input, name=None
)tf.io.decode_base64
(input, name=None
)tf.io.decode_compressed
(bytes, compression_type=''
, name=None
)tf.io.encode_base64
(input, pad=False
, name=None
)tf.math.abs
(x, name=None
)tf.math.acos
(x, name=None
)tf.math.acosh
(x, name=None
)tf.math.add_n
(inputs, name=None
)tf.math.add
(x, y, name=None
)tf.math.angle
(input, name=None
)tf.math.asin
(x, name=None
)tf.math.asinh
(x, name=None
)tf.math.atan2
(y, x, name=None
)tf.math.atan
(x, name=None
)tf.math.atanh
(x, name=None
)tf.math.ceil
(x, name=None
)tf.math.conj
(x, name=None
)tf.math.cos
(x, name=None
)tf.math.cosh
(x, name=None
)tf.math.digamma
(x, name=None
)tf.math.divide_no_nan
(x, y, name=None
)tf.math.divide
(x, y, name=None
)tf.math.equal
(x, y, name=None
)tf.math.erf
(x, name=None
)tf.math.erfc
(x, name=None
)tf.math.erfinv
(x, name=None
)tf.math.exp
(x, name=None
)tf.math.expm1
(x, name=None
)tf.math.floor
(x, name=None
)tf.math.floordiv
(x, y, name=None
)tf.math.floormod
(x, y, name=None
)tf.math.greater_equal
(x, y, name=None
)tf.math.greater
(x, y, name=None
)tf.math.imag
(input, name=None
)tf.math.is_finite
(x, name=None
)tf.math.is_inf
(x, name=None
)tf.math.is_nan
(x, name=None
)tf.math.less_equal
(x, y, name=None
)tf.math.less
(x, y, name=None
)tf.math.lgamma
(x, name=None
)tf.math.log1p
(x, name=None
)tf.math.log_sigmoid
(x, name=None
)tf.math.log
(x, name=None
)tf.math.logical_and
(x, y, name=None
)tf.math.logical_not
(x, name=None
)tf.math.logical_or
(x, y, name=None
)tf.math.logical_xor
(x, y, name='LogicalXor'
)tf.math.maximum
(x, y, name=None
)tf.math.minimum
(x, y, name=None
)tf.math.multiply
(x, y, name=None
)tf.math.ndtri
(x, name=None
)tf.math.negative
(x, name=None
)tf.math.not_equal
(x, y, name=None
)tf.math.pow
(x, y, name=None
)tf.math.real
(input, name=None
)tf.math.reciprocal
(x, name=None
)tf.math.reduce_all
(input_tensor, axis=None
, keepdims=False
, name=None
)tf.math.reduce_any
(input_tensor, axis=None
, keepdims=False
, name=None
)tf.math.reduce_max
(input_tensor, axis=None
, keepdims=False
, name=None
)tf.math.reduce_mean
(input_tensor, axis=None
, keepdims=False
, name=None
)tf.math.reduce_min
(input_tensor, axis=None
, keepdims=False
, name=None
)tf.math.reduce_prod
(input_tensor, axis=None
, keepdims=False
, name=None
)tf.math.reduce_sum
(input_tensor, axis=None
, keepdims=False
, name=None
)tf.math.rint
(x, name=None
)tf.math.round
(x, name=None
)tf.math.rsqrt
(x, name=None
)tf.math.sign
(x, name=None
)tf.math.sin
(x, name=None
)tf.math.sinh
(x, name=None
)tf.math.sqrt
(x, name=None
)tf.math.square
(x, name=None
)tf.math.squared_difference
(x, y, name=None
)tf.math.subtract
(x, y, name=None
)tf.math.tan
(x, name=None
)tf.math.truediv
(x, y, name=None
)tf.math.unsorted_segment_max
(data, segment_ids, num_segments, name=None
)tf.math.unsorted_segment_mean
(data, segment_ids, num_segments, name=None
)tf.math.unsorted_segment_min
(data, segment_ids, num_segments, name=None
)tf.math.unsorted_segment_prod
(data, segment_ids, num_segments, name=None
)tf.math.unsorted_segment_sqrt_n
(data, segment_ids, num_segments, name=None
)tf.math.unsorted_segment_sum
(data, segment_ids, num_segments, name=None
)tf.nn.dropout
(x, keep_prob=None
, noise_shape=None
, seed=None
, name=None
, rate=None
)tf.one_hot
(indices, depth, on_value=None
, off_value=None
, axis=None
, dtype=None
, name=None
)tf.ones_like
(tensor, dtype=None
, name=None
, optimize=True
)tf.print
(*inputs, **kwargs)tf.rank
(input, name=None
)tf.realdiv
(x, y, name=None
)tf.reverse
(tensor, axis, name=None
)tf.size
(input, name=None
, out_type=tf.int32
)tf.squeeze
(input, axis=None
, name=None
, squeeze_dims=None
)tf.stack
(values, axis=0
, name='stack'
)tf.strings.as_string
(input, precision=-1
, scientific=False
, shortest=False
, width=-1
, fill=''
, name=None
)tf.strings.format
(template, inputs, placeholder='{}'
, summarize=3
, name=None
)tf.strings.join
(inputs, separator=''
, name=None
)tf.strings.length
(input, name=None
, unit='BYTE'
)tf.strings.reduce_join
(inputs, axis=None
, keepdims=False
, separator=''
, name=None
)tf.strings.regex_full_match
(input, pattern, name=None
)tf.strings.regex_replace
(input, pattern, rewrite, replace_global=True
, name=None
)tf.strings.strip
(input, name=None
)tf.strings.substr
(input, pos, len, name=None
, unit='BYTE'
)tf.strings.to_hash_bucket_fast
(input, num_buckets, name=None
)tf.strings.to_hash_bucket_strong
(input, num_buckets, key, name=None
)tf.strings.to_hash_bucket
(input, num_buckets, name=None
)tf.strings.to_hash_bucket
(input, num_buckets, name=None
)tf.strings.to_number
(input, out_type=tf.float32
, name=None
)tf.strings.unicode_script
(input, name=None
)tf.tile
(input, multiples, name=None
)tf.truncatediv
(x, y, name=None
)tf.truncatemod
(x, y, name=None
)tf.where
(condition, x=None
, y=None
, name=None
)tf.where
(condition, x=None
, y=None
, name=None
)tf.zeros_like
(tensor, dtype=None
, name=None
, optimize=True
)n
Functions
boolean_mask(...)
: Applies a boolean mask to data
without flattening the mask dimensions.
constant(...)
: Constructs a constant RaggedTensor from a nested Python list.
cross(...)
: Generates feature cross from a list of tensors.
cross_hashed(...)
: Generates hashed feature cross from a list of tensors.
map_flat_values(...)
: Applies op
to the values of one or more RaggedTensors.
range(...)
: Returns a RaggedTensor
containing the specified sequences of numbers.
row_splits_to_segment_ids(...)
: Generates the segmentation corresponding to a RaggedTensor row_splits
.
segment_ids_to_row_splits(...)
: Generates the RaggedTensor row_splits
corresponding to a segmentation.
stack(...)
: Stacks a list of rank-R
tensors into one rank-(R+1)
RaggedTensor
.
stack_dynamic_partitions(...)
: Stacks dynamic partitions of a Tensor or RaggedTensor.