jmerkow edited a comment on issue #14421: Updating mxnet from 1.0.0, networks give different outputs URL: https://github.com/apache/incubator-mxnet/issues/14421#issuecomment-494122473 Here is a small example that you can use to reproduce the issue: Code: ``` import mxnet as mx class Batch(object): def __init__(self, data): self.data = data def get_batch_shape(self): return tuple(self.data[0].shape) data = mx.sym.Variable('data') gpool = mx.sym.Pooling(data, name='gpooling', global_pool=True, pad=(1,1), pool_type='avg', kernel=(8,8)) mod = mx.mod.Module(gpool, context=mx.gpu(0), label_names=[]) data = Batch([mx.ndarray.ones((1, 3, 5, 5))]) mod.bind(for_training=True, force_rebind=True, data_shapes=[('data', data.get_batch_shape())],) mod.init_params() mod.forward(data) print(mod.get_outputs()[0].asnumpy().squeeze().tolist()) ``` MX 1.0.0 output: > [0.6399999856948853, 0.6399999856948853, 0.6399999856948853] MX 1.4.0 output: > [1.0, 1.0, 1.0] This appears to be a bug with #9730...
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