Neg#

  • Domain: ai.onnx

  • Since version: 13

Neg takes one input data (Tensor ) and produces one output data (Tensor ) where each element flipped sign, y = -x, is applied to the tensor elementwise.

Inputs

  • X (T): Input tensor

Outputs

  • Y (T): Output tensor

Type Constraints

  • T: Constrain input and output types to signed numeric tensors. Allowed types: tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8).

Examples#

test_cc_neg

Node:
  Neg(x) -> (y)

Inputs:
  x: shape=(2, 3), dtype=float32
    [[-1.  ,  0.  ,  1.5 ],
     [-2.25,  3.5 , -4.75]]

Outputs:
  y: shape=(2, 3), dtype=float32
    [[ 1.  , -0.  , -1.5 ],
     [ 2.25, -3.5 ,  4.75]]

test_cc_neg_bfloat16

Node:
  Neg(x) -> (y)

Inputs:
  x: shape=(2, 3), dtype=bfloat16
    [[-1, 0, 1.5],
     [-2.25, 3.5, -4.75]]

Outputs:
  y: shape=(2, 3), dtype=bfloat16
    [[1, -0, -1.5],
     [2.25, -3.5, 4.75]]

test_cc_neg_double

Node:
  Neg(x) -> (y)

Inputs:
  x: shape=(2, 3), dtype=float64
    [[-1.  ,  0.  ,  1.5 ],
     [-2.25,  3.5 , -4.75]]

Outputs:
  y: shape=(2, 3), dtype=float64
    [[ 1.  , -0.  , -1.5 ],
     [ 2.25, -3.5 ,  4.75]]

test_cc_neg_float16

Node:
  Neg(x) -> (y)

Inputs:
  x: shape=(2, 3), dtype=float16
    [[-1.  ,  0.  ,  1.5 ],
     [-2.25,  3.5 , -4.75]]

Outputs:
  y: shape=(2, 3), dtype=float16
    [[ 1.  , -0.  , -1.5 ],
     [ 2.25, -3.5 ,  4.75]]

test_cc_neg_int16

Node:
  Neg(x) -> (y)

Inputs:
  x: shape=(2, 3), dtype=int16
    [[   -1,     0,     2],
     [-1000,     3,    -5]]

Outputs:
  y: shape=(2, 3), dtype=int16
    [[   1,    0,   -2],
     [1000,   -3,    5]]

test_cc_neg_int32

Node:
  Neg(x) -> (y)

Inputs:
  x: shape=(2, 3), dtype=int32
    [[     -1,       0,       2],
     [-100000,       3,      -5]]

Outputs:
  y: shape=(2, 3), dtype=int32
    [[     1,      0,     -2],
     [100000,     -3,      5]]

test_cc_neg_int64

Node:
  Neg(x) -> (y)

Inputs:
  x: shape=(2, 3), dtype=int64
    [[            -1,              0,              2],
     [-1000000000000,              3,             -5]]

Outputs:
  y: shape=(2, 3), dtype=int64
    [[            1,             0,            -2],
     [1000000000000,            -3,             5]]

test_cc_neg_int8

Node:
  Neg(x) -> (y)

Inputs:
  x: shape=(2, 3), dtype=int8
    [[  -1,    0,    2],
     [-127,    3,   -5]]

Outputs:
  y: shape=(2, 3), dtype=int8
    [[  1,   0,  -2],
     [127,  -3,   5]]

test_cc_shape_inference_if_symbolic_shapes

Node:
  Neg(I2) -> (Y2)

Inputs:
  I2: shape=(), dtype=bool
    True
  input_1: shape=(3, 4), dtype=float32
    [[1.       , 1.1      , 1.2      , 1.3      ],
     [1.4      , 1.5      , 1.6      , 1.7      ],
     [1.8      , 1.9000001, 2.       , 2.1      ]]
  input_2: shape=(5, 4), dtype=float32
    [[-1.        , -0.9       , -0.8       , -0.7       ],
     [-0.6       , -0.5       , -0.39999998, -0.3       ],
     [-0.19999999, -0.09999996,  0.        ,  0.10000002],
     [ 0.20000005,  0.30000007,  0.39999998,  0.5       ],
     [ 0.6       ,  0.70000005,  0.8000001 ,  0.9       ]]
  input_3: shape=(5,), dtype=bool
    [ True, False,  True, False,  True]
  input_4: shape=(3,), dtype=int64
    [1, 2, 3]
  input_5: shape=(5,), dtype=int64
    [-1, -2, -3, -4, -5]

Outputs:
  Y2: shape=(3, 4), dtype=float32
    [[1.       , 1.1      , 1.2      , 1.3      ],
     [1.4      , 1.5      , 1.6      , 1.7      ],
     [1.8      , 1.9000001, 2.       , 2.1      ]]
  output_1: shape=(3,), dtype=int64
    [-1, -2, -3]

test_neg

Node:
  Neg(x) -> (y)

Inputs:
  x: shape=(3, 4, 5), dtype=float32
    [[[ 1.4243481 , -0.61890423, -0.5907667 ,  1.4329695 ,  0.5837956 ],
      [-1.3854368 ,  1.2791865 ,  0.32735094,  0.6038593 ,  0.24222691],
      [-0.89236253, -1.1303798 , -0.09180629, -0.12591071, -1.2615176 ],
      [-0.55638343, -0.747256  , -0.59118223, -0.9279915 , -0.73401135]],

     [[ 1.1054384 , -0.69560546, -2.1534553 ,  0.11396561, -0.8268097 ],
      [ 1.2137318 , -0.22223468,  0.32949635, -0.21049212,  1.3518724 ],
      [ 0.01262847,  0.6841954 , -1.2623075 ,  0.20052178, -1.1255072 ],
      [-0.6395738 ,  1.5355366 , -1.1466801 , -0.42676184, -0.74427605]],

     [[-1.5989435 ,  2.3646672 , -1.0641551 ,  0.90345967, -0.24993172],
      [-1.784248  , -0.47239977,  0.09873669, -0.36464727,  0.6651279 ],
      [-1.01641   , -0.39525023,  0.45574856, -0.3439513 , -0.5487247 ],
      [ 0.06280329, -0.14411083, -1.1603392 ,  0.49200374, -0.16951095]]]

Outputs:
  y: shape=(3, 4, 5), dtype=float32
    [[[-1.4243481 ,  0.61890423,  0.5907667 , -1.4329695 , -0.5837956 ],
      [ 1.3854368 , -1.2791865 , -0.32735094, -0.6038593 , -0.24222691],
      [ 0.89236253,  1.1303798 ,  0.09180629,  0.12591071,  1.2615176 ],
      [ 0.55638343,  0.747256  ,  0.59118223,  0.9279915 ,  0.73401135]],

     [[-1.1054384 ,  0.69560546,  2.1534553 , -0.11396561,  0.8268097 ],
      [-1.2137318 ,  0.22223468, -0.32949635,  0.21049212, -1.3518724 ],
      [-0.01262847, -0.6841954 ,  1.2623075 , -0.20052178,  1.1255072 ],
      [ 0.6395738 , -1.5355366 ,  1.1466801 ,  0.42676184,  0.74427605]],

     [[ 1.5989435 , -2.3646672 ,  1.0641551 , -0.90345967,  0.24993172],
      [ 1.784248  ,  0.47239977, -0.09873669,  0.36464727, -0.6651279 ],
      [ 1.01641   ,  0.39525023, -0.45574856,  0.3439513 ,  0.5487247 ],
      [-0.06280329,  0.14411083,  1.1603392 , -0.49200374,  0.16951095]]]

test_neg_example

Node:
  Neg(x) -> (y)

Inputs:
  x: shape=(2,), dtype=float32
    [-4.,  2.]

Outputs:
  y: shape=(2,), dtype=float32
    [ 4., -2.]

Differences with previous version (6)#

SchemaDiff: Neg (domain 'ai.onnx')

  • old version: 6

  • new version: 13

  • breaking: no

Type constraints:

  • changed ‘T’: added types: [‘tensor(bfloat16)’]

Version History#