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