TensorFlow 1 version
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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 RaggedTensors 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.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.