package aialgs.data

Get desktop application:
View/edit binary Protocol Buffers messages

Support for storing binary data for parsing in other ways (such as JPEG/etc). This is an example of another type of value and may not immediately be supported.

Used in: Value

• repeated bytes value = 1

• optional string content_type = 2

  Stores the content type of the data if known. This will allow the possibility of using decoders for common formats in the future.

A sparse or dense rank-R tensor that stores data as doubles (float64).

Used in: Value

• repeated float values = 1

  Each value in the vector. If keys is empty this is treated as a dense vector.

• repeated uint64 keys = 2

  If not empty then the vector is treated as sparse with each key specifying the location of the value in the sparse vector.

• repeated uint64 shape = 3

  Optional shape which will allow the vector to represent a matrix. e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row and keys[i] % 20 will give the column. This also supports n-dimensonal tensors. NB. this must be specified if the tensor is sparse.

A sparse or dense rank-R tensor that stores data as doubles (float64).

Used in: Value

• repeated double values = 1

  Each value in the vector. If keys is empty this is treated as a dense vector.

• repeated uint64 keys = 2

  If not empty then the vector is treated as sparse with each key specifying the location of the value in the sparse vector.

• repeated uint64 shape = 3

  Optional shape which will allow the vector to represent a matrix. e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row and keys[i] % 20 will give the column. This also supports n-dimensonal tensors. NB. this must be specified if the tensor is sparse.

A sparse or dense rank-R tensor that stores data as 32-bit ints (int32).

Used in: Value

• repeated int32 values = 1

  Each value in the vector. If keys is empty this is treated as a dense vector.

• repeated uint64 keys = 2

  If not empty then the vector is treated as sparse with each key specifying the location of the value in the sparse vector.

• repeated uint64 shape = 3

  Optional shape which will allow the vector to represent a matrix. e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row and keys[i] % 20 will give the column. This also supports n-dimensonal tensors. NB. this must be specified if the tensor is sparse.

• map<string, Value> features = 1

  Map from the name of the feature to the value. For vectors and libsvm-like datasets, a single feature with the name `values` should be specified.

• map<string, Value> label = 2

  Optional set of labels for this record. Similar to features field above, the key used for generic scalar / vector labels should ve 'values'

• optional string uid = 3

  Unique identifier for this record in the dataset. Whilst not necessary, this allows better debugging where there are data issues. This is not used by the algorithm directly.

• optional string metadata = 4

  Textual metadata describing the record. This may include JSON-serialized information about the source of the record. This is not used by the algorithm directly.

• optional string configuration = 5

  Optional serialized JSON object that allows per-record hyper-parameters/configuration/other information to be set. The meaning/interpretation of this field is defined by the algorithm author and may not be supported. This is used to pass additional inference configuration when batch inference is used (e.g. types of scores to return).

Used in: Record

• oneof value

  • Float32Tensor float32_tensor = 2

    The numbering assumes the possible use of: - float16, float128 - int8, int16, int32

  • Float64Tensor float64_tensor = 3

  • Int32Tensor int32_tensor = 7

  • Bytes bytes = 9