I’m working on a vision model and it requires using variance function.

## Inconsistency between PyTorch and TensorFlow variance function outputs

For some reasons I am testing the TensorFlow and PyTorch’s variance functions and I find that the results for same input values are different. And it sounds weird because variance is a parameterless function, hence, there are no values to set that might affect the output value.

```
# TensorFlow Example
import tensorflow as tf
x = tf.constant([[1., 2.], [3., 4.]])
print(tf.math.reduce_variance(x))
print(tf.math.reduce_variance(x, 0))
print(tf.math.reduce_variance(x, 1))
# Outputs
# tf.Tensor(1.25, shape=(), dtype=float32)
# tf.Tensor([1. 1.], shape=(2,), dtype=float32)
# tf.Tensor([0.25 0.25], shape=(2,), dtype=float32)
```

```
# PyTorch Example
import torch as t
x = t.tensor([[1., 2.], [3., 4.]])
print(t.var(x))
print(t.var(x, 0))
print(t.var(x, 1))
# Outputs
# tensor(1.6667)
# tensor([2., 2.])
# tensor([0.5000, 0.5000])
```

##
Implementation help with PyTorch’s `var()`

using `sum()`

function

Since my model is trained in PyTorch I want to know how to write the `var()`

function using `sum()`

function. The implementation I tried is as follows but the result doesn’t match with that of PyTorch’s official implementation:

```
import torch as t
_ = t.manual_seed(0)
def var(x, dim):
x_mean = t.mean(x)
sq_devs = t.square(x - x_mean)
return t.mean(sq_devs, dim=dim)
x = t.randn(5, 4, 3)
x_var1 = t.var(x, dim=0)
x_var2 = var(x, dim=0)
print("x:", x)
print("x_var1:", x_var1)
print("x_var2:", x_var2)
```

Output

```
x: tensor([[[-1.1258, -1.1524, -0.2506],
[-0.4339, 0.8487, 0.6920],
[-0.3160, -2.1152, 0.3223],
[-1.2633, 0.3500, 0.3081]],
[[ 0.1198, 1.2377, 1.1168],
[-0.2473, -1.3527, -1.6959],
[ 0.5667, 0.7935, 0.5988],
[-1.5551, -0.3414, 1.8530]],
[[ 0.7502, -0.5855, -0.1734],
[ 0.1835, 1.3894, 1.5863],
[ 0.9463, -0.8437, -0.6136],
[ 0.0316, -0.4927, 0.2484]],
[[ 0.4397, 0.1124, 0.6408],
[ 0.4412, -0.1023, 0.7924],
[-0.2897, 0.0525, 0.5943],
[ 1.5419, 0.5073, -0.5910]],
[[-0.5692, 0.9200, 1.1108],
[ 1.2899, -1.4959, -0.1938],
[ 0.4455, 1.3253, -1.6293],
[-0.5497, -0.4798, -0.4997]]])
x_var1: tensor([[0.5831, 1.0012, 0.4474],
[0.4593, 1.6567, 1.5645],
[0.3082, 1.8627, 0.9353],
[1.5127, 0.2319, 0.9604]])
x_var2: tensor([[0.4835, 0.8038, 0.5476],
[0.4048, 1.3637, 1.2851],
[0.2938, 1.5346, 0.7877],
[1.3801, 0.2065, 0.8126]])
```

I’d really appreciate anyone willing to help me here.

Thanks in advance,

Rahul Bhalley