thomelane commented on a change in pull request #15343: Revise Symbol tutorial
URL: https://github.com/apache/incubator-mxnet/pull/15343#discussion_r297301068
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File path: docs/tutorials/basic/symbol.md
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@@ -429,18 +356,93 @@ print({'input':arg, 'output':out})
### Variable Sharing
-To share the contents between several symbols,
-we can bind these symbols with the same array as follows:
+To share the contents between several symbols, we can bind these symbols with
the same array as follows:
```python
a = mx.sym.Variable('a')
b = mx.sym.Variable('b')
b = a + a * a
-data = mx.nd.ones((2,3))*2
+data = mx.nd.ones((2,3)) * 2
ex = b.bind(ctx=mx.cpu(), args={'a':data, 'b':data})
ex.forward()
ex.outputs[0].asnumpy()
```
+### Weight tying
+
+You can use same principle to tie weights of different layers. In the example
below two `FullyConnected` layers share same weights and biases, but process
different data. Find a full example below.
Review comment:
Could you explain a little more about where to look or what's going on? It's
quite a long code block.
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