[GitHub] [incubator-mxnet] MonicaGu removed a comment on issue #17684: The output of the ReLU layer in MXNET is different from that in tensorflow and cntk

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MonicaGu removed a comment on issue #17684: The output of the ReLU layer in MXNET is different from that in tensorflow and cntk
URL: https://github.com/apache/incubator-mxnet/issues/17684#issuecomment-603136144
 
 
   I ran your code and reproduced the difference in ReLU layer. However, when I use the mxnet output of `conv1_bn` as the input of ReLU, TensorFlow and mxnet backends have the same output. This is my code:
   ```
   import pickle
   import numpy as np
   import tensorflow as tf
   import mxnet as mx
   
   with open("output/{}_{}.pkl".format("mxnet",imagename[:-4]), "rb+") as f:
   	backend_output_dict = pickle.load(f)
   count = 0
   for each in backend_output_dict:
   	count += 1
   	if count == 3:
   		input = each
   		break
   mxoutput = mx.nd.relu(mx.nd.array(input))
   tfoutput = tf.nn.relu(input)
   print(delta(mxoutput.asnumpy(), tfoutput))
   ```
   The output of running the code is:
   ```
   [0.]
   [0.]
   ```
   
   So I suppose there might be a problem in BatchNorm and the slight difference in `conv1_bn` might lead to a big difference in `conv1_relu`.

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