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Combines a batch of feature ids and values into a single SparseTensor
. (deprecated)
tf.compat.v1.sparse_merge(
sp_ids, sp_values, vocab_size, name=None, already_sorted=False
)
The most common use case for this function occurs when feature ids and
their corresponding values are stored in Example
protos on disk.
parse_example
will return a batch of ids and a batch of values, and this
function joins them into a single logical SparseTensor
for use in
functions such as sparse_tensor_dense_matmul
, sparse_to_dense
, etc.
The SparseTensor
returned by this function has the following properties:
indices
is equivalent tosp_ids.indices
with the last dimension discarded and replaced withsp_ids.values
.values
is simplysp_values.values
.- If
sp_ids.dense_shape = [D0, D1, ..., Dn, K]
, thenoutput.shape = [D0, D1, ..., Dn, vocab_size]
.
For example, consider the following feature vectors:
vector1 = [-3, 0, 0, 0, 0, 0]
vector2 = [ 0, 1, 0, 4, 1, 0]
vector3 = [ 5, 0, 0, 9, 0, 0]
These might be stored sparsely in the following Example protos by storing only the feature ids (column number if the vectors are treated as a matrix) of the non-zero elements and the corresponding values:
examples = [Example(features={
"ids": Feature(int64_list=Int64List(value=[0])),
"values": Feature(float_list=FloatList(value=[-3]))}),
Example(features={
"ids": Feature(int64_list=Int64List(value=[1, 4, 3])),
"values": Feature(float_list=FloatList(value=[1, 1, 4]))}),
Example(features={
"ids": Feature(int64_list=Int64List(value=[0, 3])),
"values": Feature(float_list=FloatList(value=[5, 9]))})]
The result of calling parse_example on these examples will produce a
dictionary with entries for "ids" and "values". Passing those two objects
to this function along with vocab_size=6, will produce a SparseTensor
that
sparsely represents all three instances. Namely, the indices
property will
contain the coordinates of the non-zero entries in the feature matrix (the
first dimension is the row number in the matrix, i.e., the index within the
batch, and the second dimension is the column number, i.e., the feature id);
values
will contain the actual values. shape
will be the shape of the
original matrix, i.e., (3, 6). For our example above, the output will be
equal to:
SparseTensor(indices=[[0, 0], [1, 1], [1, 3], [1, 4], [2, 0], [2, 3]],
values=[-3, 1, 4, 1, 5, 9],
dense_shape=[3, 6])
This method generalizes to higher-dimensions by simply providing a list for
both the sp_ids as well as the vocab_size.
In this case the resulting SparseTensor
has the following properties:
indices
is equivalent tosp_ids[0].indices
with the last dimension discarded and concatenated withsp_ids[0].values, sp_ids[1].values, ...
.values
is simplysp_values.values
.- If
sp_ids.dense_shape = [D0, D1, ..., Dn, K]
, thenoutput.shape = [D0, D1, ..., Dn] + vocab_size
.
Returns | |
---|---|
A SparseTensor compactly representing a batch of feature ids and values,
useful for passing to functions that expect such a SparseTensor .
|