aaronmarkham commented on a change in pull request #10511: add naming tutorial URL: https://github.com/apache/incubator-mxnet/pull/10511#discussion_r181165057
########## File path: docs/tutorials/gluon/naming.md ########## @@ -0,0 +1,236 @@ + +# Naming of Gluon Parameter and Blocks + +In gluon, each Parameter or Block has a name (and prefix). Parameter names are specified by users and Block names can be either specified by users or automatically created. + +In this tutorial we talk about the best practices on naming. First, let's import MXNet and Gluon: + + +```python +from __future__ import print_function +import mxnet as mx +from mxnet import gluon +``` + +## Naming Blocks + +When creating a block, you can assign a prefix to it: + + +```python +mydense = gluon.nn.Dense(100, prefix='mydense_') +print(mydense.prefix) +``` + + mydense_ + + +When no prefix is given, Gluon will automatically generate one: + + +```python +dense0 = gluon.nn.Dense(100) +print(dense0.prefix) +``` + + dense0_ + + +When you create more Blocks of the same kind, they will be named differently to avoid collision: + + +```python +dense1 = gluon.nn.Dense(100) +print(dense1.prefix) +``` + + dense1_ + + +## Naming Parameters + +Parameters within a Block will be named by prepending the prefix of the Block to the name of the Parameter: + + +```python +print(dense0.collect_params()) +``` + + dense0_ ( + Parameter dense0_weight (shape=(100, 0), dtype=<type 'numpy.float32'>) + Parameter dense0_bias (shape=(100,), dtype=<type 'numpy.float32'>) + ) + + +## Name scopes + +To manage the names of nested Blocks, each Block has a `name_scope` attached to it. All Blocks created within a name scope will have its parent Block's prefix prepended to its name. + +Let's demonstrate this by first define a simple neural net: + + +```python +class Model(gluon.Block): + def __init__(self, **kwargs): + super(Model, self).__init__(**kwargs) + with self.name_scope(): + self.dense0 = gluon.nn.Dense(20) + self.dense1 = gluon.nn.Dense(20) + self.mydense = gluon.nn.Dense(20, prefix='mydense_') + + def forward(self, x): + x = mx.nd.relu(self.dense0(x)) + x = mx.nd.relu(self.dense1(x)) + return mx.nd.relu(self.mydense(x)) +``` + +Now let's instantiate our neural net. + +- Note that `model0.dense0` is named as `model0_dense0_` instead of `dense0_`. + +- Also note that although we specified `mydense_` as prefix for `model.mydense`, its parent's prefix is automatically prepended to generate the prefix `model0_mydense_`. + + +```python +model0 = Model() +model0.initialize() +model0(mx.nd.zeros((1, 20))) +print(model0.prefix, model0.dense0.prefix, model0.dense1.prefix, model0.mydense.prefix) +``` + + model0_ model0_dense0_ model0_dense1_ model0_mydense_ + + +If we instantiate `Model` again, it will be given a different name like shown before for `Dense`. + +- Note that `model1.dense0` is still named as `dense0_` instead of `dense2_`, following dense layers in previously created `model0`. This is because each instance of model's name scope is independent of each other. + + +```python +model1 = Model() +print(model1.prefix, model1.dense0.prefix, model1.dense1.prefix, model1.mydense.prefix) +``` + + model1_ model1_dense0_ model1_dense1_ model1_mydense_ + + +**It is recommended that you manually specify prefix for the top level Block (i.e. `model = Model(prefix='mymodel_')`) to avoid potential confusions in naming** Review comment: And why not show that code sample if it's so important? ---------------------------------------------------------------- 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: us...@infra.apache.org With regards, Apache Git Services
