[GitHub] [incubator-mxnet] RogerChern commented on issue #12369: batchnorm from scratch with autograd gives very different gradient from mx.nd.BatchNorm

[GitBox <gi...@apache.org>]

RogerChern commented on issue #12369: batchnorm from scratch with autograd gives very different gradient from mx.nd.BatchNorm
URL: https://github.com/apache/incubator-mxnet/issues/12369#issuecomment-501554476
 
 
   Cool, I now get the correct result with the following snippet.
   
   ```python
   import mxnet as mx
   
   
   def batch_norm_nd(x, gamma, beta, eps=1e-5):
       mean = mx.nd.mean(x, axis=(0, 2, 3), keepdims=True)
       var = mx.nd.mean((x - mean) ** 2, axis=(0, 2, 3), keepdims=True)
       x_hat = (x - mean) / mx.nd.sqrt(var + eps)
   
       return x_hat * gamma + beta
   
   if __name__ == "__main__":
       x1 = mx.nd.random_normal(0.3, 2, shape=(2, 16, 32, 32))
       x2 = x1.copy()
       gamma = mx.nd.ones(shape=(1, 16, 1, 1))
       beta = mx.nd.zeros(shape=(1, 16, 1, 1))
       mmean = mx.nd.zeros(shape=(1, 16, 1, 1))
       mvar = mx.nd.ones(shape=(1, 16, 1, 1))
       x1.attach_grad()
       x2.attach_grad()
       gamma.attach_grad()
       beta.attach_grad()
   
       grad = mx.nd.random_normal(0, 1, shape=(2, 16, 32, 32))
       with mx.autograd.record(train_mode=True):
           y1 = batch_norm_nd(x1, gamma, beta)
       y1.backward(grad)
   
       with mx.autograd.record(train_mode=True):
           y2 = mx.nd.BatchNorm(x2, gamma, beta, mmean, mvar, fix_gamma=False, use_global_stats=False, eps=1e-5)
       y2.backward(grad)
   
       print("--------------------autograd grad scale----------------------")
       print(x1.grad[0, 1])
       print("\n\n")
   
       print("--------------------forward native/autograd----------------------")
       print((y2 / y1)[0, 1])
       print("\n\n")
   
       print("--------------------backward native/autograd----------------------")
       print((x2.grad / x1.grad)[0, 1])
   ```

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