bhavinthaker commented on a change in pull request #7921: Add three sparse tutorials URL: https://github.com/apache/incubator-mxnet/pull/7921#discussion_r140676905
########## File path: docs/tutorials/sparse/train.md ########## @@ -0,0 +1,256 @@ + +# Train a Linear Regression Model with Sparse Symbols +In previous tutorials, we introduced `CSRNDArray` and `RowSparseNDArray`, +the basic data structures for manipulating sparse data. +MXNet also provides `Sparse Symbol` API, which enables symbolic expressions that handle sparse arrays. +In this tutorial, we first focus on how to compose a symbolic graph with sparse operators, +then train a linear regression model using sparse symbols with the Module API. + +## Prerequisites + +To complete this tutorial, we need: + +- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html). + +- [Jupyter Notebook](http://jupyter.org/index.html) and [Python Requests](http://docs.python-requests.org/en/master/) packages. +``` +pip install jupyter requests +``` + +- Basic knowledge of Symbol in MXNet. See the detailed tutorial for Symbol in [Symbol - Neural network graphs and auto-differentiation](https://mxnet.incubator.apache.org/tutorials/basic/symbol.html). + +- Basic knowledge of CSRNDArray in MXNet. See the detailed tutorial for CSRNDArray in [TODO(haibin) Add Link Here](http://ec2-54-187-32-207.us-west-2.compute.amazonaws.com/tutorials/sparse/csr.html). + +- Basic knowledge of RowSparseNDArray in MXNet. See the detailed tutorial for RowSparseNDArray in [TODO(haibin) Add Link Here](http://ec2-54-187-32-207.us-west-2.compute.amazonaws.com/tutorials/sparse/rowsparse.html). + +## Variables + +Variables are placeholder for arrays. We can use them to hold sparse arrays, too. + +### Variable Storage Types + +The `stype` attribute of a variable is used to indicate the storage type of the array. +By default, the `stype` of a variable is "default" which indicates the default dense storage format. +We can specify the `stype` of a variable as "csr" or "row_sparse" to hold sparse arrays. + + +```python +import mxnet as mx +# Create a variable to hold an NDArray +a = mx.sym.Variable('a') +# Create a variable to hold a CSRNDArray +b = mx.sym.Variable('b', stype='csr') +# Create a variable to hold a RowSparseNDArray +c = mx.sym.Variable('c', stype='row_sparse') +(a, b, c) +``` + +### Bind with Sparse Arrays + +The sparse symbols constructed above declare storage types of the arrays to hold. +To evaluate them, we need to feed the free variables with sparse data. + +You can instantiate an executor from a sparse symbol by using the `simple_bind` method, +which allocate zeros to all free variables according to their storage types. +The executor provides `forward` method for evaluation and an attribute +`outputs` to get all the results. Later, we will show the use of the `backward` method and other methods computing the gradients and updating parameters. A simple example first: + + +```python +shape = (2,2) +# Instantiate an executor from sparse symbols +b_exec = b.simple_bind(ctx=mx.cpu(), b=shape) +c_exec = c.simple_bind(ctx=mx.cpu(), c=shape) +b_exec.forward() +c_exec.forward() +# Sparse arrays of zeros are bound to b and c +print(b_exec.outputs, c_exec.outputs) +``` + +You can update the array held by the variable by accessing executor's `arg_dict` and assigning new values. + + +```python +var_exec.arg_dict['b'][:] = mx.nd.ones(shape).tostype('csr') Review comment: FYI: NameError: name 'var_exec' is not defined ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on 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
