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Note: Functions taking Tensor arguments can also take anything accepted by tf.convert_to_tensor.
Tensorflow supports a SparseTensor representation for data that is sparse in multiple dimensions. Contrast this representation with IndexedSlices, which is efficient for representing tensors that are sparse in their first dimension, and dense along all other dimensions.
Represents a sparse tensor.
Tensorflow represents a sparse tensor as three separate dense tensors: indices, values, and dense_shape. In Python, the three tensors are collected into a SparseTensor class for ease of use. If you have separate indices, values, and dense_shape tensors, wrap them in a SparseTensor object before passing to the Ops below.
Concretely, the sparse tensor SparseTensor(values, indices, dense_shape) is
indices: A 2-D int64 tensor of shape [N, ndims]. values: A 1-D tensor of any type and shape [N]. dense_shape: A 1-D int64 tensor of shape [ndims].where N and ndims are the number of values, and number of dimensions in the SparseTensorrespectively.
The corresponding dense tensor satisfies
dense.shape = dense_shape dense[tuple(indices[i])] = values[i]By convention, indices should be sorted in row-major order (or equivalently lexigraphic order on the tuples indices[i]). This is not enforced when SparseTensor objects are constructed, but most Ops assume correct ordering. If the ordering is wrong, it can be fixed by calling sparse_reorder on the misordered SparseTensor.
Example: The sparse tensor
SparseTensor(values=[1, 2], indices=[[0, 0], [1, 2]], shape=[3, 4])represents the dense tensor
[[1, 0, 0, 0] [0, 0, 2, 0] [0, 0, 0, 0]]Creates a SparseTensor.
A SparseTensor
The indices of non-zero values in the represented dense tensor.
A 2-D Tensor of int64 with shape [N, ndims], where N is the number of non-zero values in the tensor, and ndims is the rank.
The non-zero values in the represented dense tensor.
A 1-D Tensor of any data type.
The DType of elements in this tensor.
A 1-D Tensor of int64 representing the shape of the dense tensor.
The Graph that contains the index, value, and shape tensors.
SparseTensorValue(indices, values, shape)
Alias for field number 0
Alias for field number 2
Alias for field number 1
Converts a sparse representation into a dense tensor.
Builds an array dense with shape output_shape such that
# If sparse_indices is scalar dense[i] = (i == sparse_indices ? sparse_values : default_value) # If sparse_indices is a vector, then for each i dense[sparse_indices[i]] = sparse_values[i] # If sparse_indices is an n by d matrix, then for each i in [0, n) dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]All other values in dense are set to default_value. If sparse_values is a scalar, all sparse indices are set to this single value.
A Tensor. Has the same type as sparse_values. Dense output tensor of shape output_shape.
Converts a SparseTensor into a dense tensor.
This op is a convenience wrapper around sparse_to_dense for SparseTensors.
For example, if sp_input has shape [3, 5] and non-empty string values:
[0, 1]: a [0, 3]: b [2, 0]: cand default_value is x, then the output will be a dense [3, 5] string tensor with values:
[[x a x b x] [x x x x x] [c x x x x]]A dense tensor with shape sp_input.shape and values specified by the non-empty values in sp_input. Indices not in sp_input are assigned default_value.
Converts a SparseTensor of ids into a dense bool indicator tensor.
The last dimension of sp_input is discarded and replaced with the values of sp_input. If sp_input.shape = [D0, D1, ..., Dn, K], then output.shape = [D0, D1, ..., Dn, vocab_size], where
output[d_0, d_1, ..., d_n, sp_input[d_0, d_1, ..., d_n, k]] = Trueand False elsewhere in output.
For example, if sp_input.shape = [2, 3, 4] with non-empty values:
[0, 0, 0]: 0 [0, 1, 0]: 10 [1, 0, 3]: 103 [1, 1, 2]: 112 [1, 1, 3]: 113 [1, 2, 1]: 121and vocab_size = 200, then the output will be a [2, 3, 200] dense bool tensor with False everywhere except at positions
(0, 0, 0), (0, 1, 10), (1, 0, 103), (1, 1, 112), (1, 1, 113), (1, 2, 121).This op is useful for converting SparseTensors into dense formats for compatibility with ops that expect dense tensors.
The input SparseTensor must be in row-major order.
A dense bool indicator tensor representing the indices with specified value.
Concatenates a list of SparseTensor along the specified dimension.
Concatenation is with respect to the dense versions of each sparse input. It is assumed that each inputs is a SparseTensor whose elements are ordered along increasing dimension number.
All inputs' shapes must match, except for the concat dimension. The indices, values, and shapeslists must have the same length.
The output shape is identical to the inputs', except along the concat dimension, where it is the sum of the inputs' sizes along that dimension.
The output elements will be resorted to preserve the sort order along increasing dimension number.
This op runs in O(M log M) time, where M is the total number of non-empty values across all inputs. This is due to the need for an internal sort in order to concatenate efficiently across an arbitrary dimension.
For example, if concat_dim = 1 and the inputs are
sp_inputs[0]: shape = [2, 3] [0, 2]: "a" [1, 0]: "b" [1, 1]: "c" sp_inputs[1]: shape = [2, 4] [0, 1]: "d" [0, 2]: "e"then the output will be
shape = [2, 7] [0, 2]: "a" [0, 4]: "d" [0, 5]: "e" [1, 0]: "b" [1, 1]: "c"Graphically this is equivalent to doing
[ a] concat [ d e ] = [ a d e ] [b c ] [ ] [b c ]A SparseTensor with the concatenated output.
Reorders a SparseTensor into the canonical, row-major ordering.
Note that by convention, all sparse ops preserve the canonical ordering along increasing dimension number. The only time ordering can be violated is during manual manipulation of the indices and values to add entries.
Reordering does not affect the shape of the SparseTensor.
For example, if sp_input has shape [4, 5] and indices / values:
[0, 3]: b [0, 1]: a [3, 1]: d [2, 0]: cthen the output will be a SparseTensor of shape [4, 5] and indices / values:
[0, 1]: a [0, 3]: b [2, 0]: c [3, 1]: dA SparseTensor with the same shape and non-empty values, but in canonical ordering.
Retains specified non-empty values within a SparseTensor.
For example, if sp_input has shape [4, 5] and 4 non-empty string values:
[0, 1]: a [0, 3]: b [2, 0]: c [3, 1]: dand to_retain = [True, False, False, True], then the output will be a SparseTensor of shape [4, 5]with 2 non-empty values:
[0, 1]: a [3, 1]: dA SparseTensor with the same shape as the input and M non-empty elements corresponding to the true positions in to_retain.
Fills empty rows in the input 2-D SparseTensor with a default value.
This op adds entries with the specified default_value at index [row, 0] for any row in the input that does not already have a value.
For example, suppose sp_input has shape [5, 6] and non-empty values:
[0, 1]: a [0, 3]: b [2, 0]: c [3, 1]: dRows 1 and 4 are empty, so the output will be of shape [5, 6] with values:
[0, 1]: a [0, 3]: b [1, 0]: default_value [2, 0]: c [3, 1]: d [4, 0]: default_valueNote that the input may have empty columns at the end, with no effect on this op.
The output SparseTensor will be in row-major order and will have the same shape as the input.
This op also returns an indicator vector such that
empty_row_indicator[i] = True iff row i was an empty row.