thomelane commented on a change in pull request #15343: Revise Symbol tutorial URL: https://github.com/apache/incubator-mxnet/pull/15343#discussion_r297299933
########## File path: docs/tutorials/basic/symbol.md ########## @@ -359,33 +307,21 @@ ex_gpu.forward() ex_gpu.outputs[0].asnumpy() ``` -We can also use `eval` method to evaluate the symbol. It combines calls to `bind` -and `forward` methods. +We can also use [eval](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Symbol.eval) method to evaluate the symbol. It combines calls to `bind` and `forward` methods. ```python ex = c.eval(ctx = mx.cpu(), a = mx.nd.ones([2,3]), b = mx.nd.ones([2,3])) print('number of outputs = %d\nthe first output = \n%s' % ( len(ex), ex[0].asnumpy())) ``` -For neural nets, a more commonly used pattern is ```simple_bind```, which -creates all of the argument arrays for you. Then you can call ```forward```, -and ```backward``` (if the gradient is needed) to get the gradient. +For neural nets, a more commonly used pattern is [simple_bind](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.Symbol.simple_bind), which creates all of the argument arrays for you. Then you can call `forward`, and [backward](https://mxnet.incubator.apache.org/api/python/executor/executor.html#mxnet.executor.Executor.backward) to get gradients if needed. ### Load and Save -Logically symbols correspond to ndarrays. They both represent a tensor. They both -are inputs/outputs of operators. We can either serialize a `Symbol` object by -using `pickle`, or by using `save` and `load` methods directly as we discussed in -[NDArray tutorial](http://mxnet.io/tutorials/basic/ndarray.html#serialize-from-to-distributed-filesystems). +Logically symbols correspond to NDArrays. They both represent a tensor. They both are inputs/outputs of operators. We can either serialize a `Symbol` object by using `pickle`, or by using `save` and `load` methods directly as it is explained in [NDArray tutorial](http://mxnet.io/tutorials/basic/ndarray.html#serialize-from-to-distributed-filesystems). Review comment: explained in [NDArray tutorial] -> explained in [this NDArray tutorial] ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
