Tanh#
Domain:
ai.onnxSince version: 13
Calculates the hyperbolic tangent of the given input tensor element-wise.
Inputs
input (T): Input tensor
Outputs
output (T): The hyperbolic tangent values of the input tensor computed element-wise
Type Constraints
T: Constrain input and output types to float tensors. Allowed types: tensor(bfloat16), tensor(double), tensor(float), tensor(float16).
Examples#
test_cc_tanh
Node:
Tanh(input) -> (output)
Inputs:
input: shape=(2, 3), dtype=float32
[[-4., -1., 0.],
[ 1., 2., 4.]]
Outputs:
output: shape=(2, 3), dtype=float32
[[-0.9993293, -0.7615942, 0. ],
[ 0.7615942, 0.9640276, 0.9993293]]
test_cc_tanh_bfloat16
Node:
Tanh(x) -> (y)
Inputs:
x: shape=(2, 3), dtype=bfloat16
[[-2, -1, 0],
[0.5, 1, 2]]
Outputs:
y: shape=(2, 3), dtype=bfloat16
[[-0.964844, -0.761719, 0],
[0.462891, 0.761719, 0.964844]]
test_cc_tanh_double
Node:
Tanh(x) -> (y)
Inputs:
x: shape=(2, 3), dtype=float64
[[-2. , -1. , 0. ],
[ 0.5, 1. , 2. ]]
Outputs:
y: shape=(2, 3), dtype=float64
[[-0.96402758, -0.76159416, 0. ],
[ 0.46211716, 0.76159416, 0.96402758]]
test_cc_tanh_float16
Node:
Tanh(x) -> (y)
Inputs:
x: shape=(2, 3), dtype=float16
[[-2. , -1. , 0. ],
[ 0.5, 1. , 2. ]]
Outputs:
y: shape=(2, 3), dtype=float16
[[-0.964 , -0.7617, 0. ],
[ 0.4622, 0.7617, 0.964 ]]
test_tanh
Node:
Tanh(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
[[[ 0.8905025 , -0.5503647 , -0.5304468 , 0.89227355, 0.52541864],
[-0.88216287, 0.85626805, 0.31613848, 0.53979003, 0.23759806],
[-0.7125586 , -0.81114924, -0.09154923, -0.12524953, -0.851482 ],
[-0.5052892 , -0.63350904, -0.5307454 , -0.7296561 , -0.6255134 ]],
[[ 0.802444 , -0.601571 , -0.9734081 , 0.11347475, -0.6787592 ],
[ 0.8377954 , -0.21864694, 0.31806815, -0.2074375 , 0.87449455],
[ 0.0126278 , 0.5942401 , -0.851699 , 0.19787674, -0.809476 ],
[-0.5646093 , 0.9113673 , -0.8166513 , -0.40261155, -0.63172174]],
[[-0.9215094 , 0.98248994, -0.78724897, 0.7179783 , -0.24485448],
[-0.9451502 , -0.44013628, 0.09841708, -0.3493009 , 0.58176583],
[-0.76840025, -0.3758776 , 0.42661285, -0.3310004 , -0.4995638 ],
[ 0.06272084, -0.14312144, -0.8211504 , 0.45580533, -0.16790582]]]
test_tanh_example
Node:
Tanh(x) -> (y)
Inputs:
x: shape=(3,), dtype=float32
[-1., 0., 1.]
Outputs:
y: shape=(3,), dtype=float32
[-0.7615942, 0. , 0.7615942]
Differences with previous version (6)#
SchemaDiff: Tanh (domain 'ai.onnx')
old version: 6
new version: 13
breaking: no
Type constraints:
changed ‘T’: added types: [‘tensor(bfloat16)’]