# Plumbing: A look under the hood of gluon¶

In the previous tutorials, we taught you about linear regression and softmax regression. We explained how these models work in principle, showed you how to implement them from scratch, and presented a compact implementation using gluon. We explained how to do things in gluon but didn’t really explain how they work. We relied on nn.Sequential, syntactically convenient shorthand for nn.Block but didn’t peek under the hood. And while each notebook presented a working, trained model, we didn’t show you how to inspect its parameters, save and load models, etc. In this chapter, we’ll take a break from modeling to explore the gory details of mxnet.gluon.

First, let’s get the preliminaries out of the way.

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from __future__ import print_function
import mxnet as mx
import numpy as np
from mxnet import nd, autograd, gluon
from mxnet.gluon import nn, Block

###########################
#  Specify the context we'll be using
###########################
ctx = mx.cpu()

###########################
###########################
batch_size = 64
def transform(data, label):
return data.astype(np.float32)/255, label.astype(np.float32)
batch_size, shuffle=True)
batch_size, shuffle=False)


## Composing networks with gluon.Block¶

Now you might remember that up until now, we’ve defined neural networks (for example, a multilayer perceptron) like this:

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net1 = gluon.nn.Sequential()
with net1.name_scope():


This is a convenient shorthand that allows us to express a neural network compactly. When we want to build simple networks, this saves us a lot of time. But both (i) to understand how nn.Sequential works, and (ii) to compose more complex architectures, you’ll want to understand gluon.Block.

Let’s take a look at how the same model would be expressed with gluon.Block.

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class MLP(Block):
def __init__(self, **kwargs):
super(MLP, self).__init__(**kwargs)
with self.name_scope():
self.dense0 = nn.Dense(128)
self.dense1 = nn.Dense(64)
self.dense2 = nn.Dense(10)

def forward(self, x):
x = nd.relu(self.dense0(x))
x = nd.relu(self.dense1(x))
return self.dense2(x)


Now that we’ve defined a class for MLPs, we can go ahead and instantiate one:

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net2 = MLP()


And initialize its parameters:

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net2.initialize(ctx=ctx)


At this point we can pass data through the network by calling it like a function, just as we have in the previous tutorials.

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for data, _ in train_data:
data = data.as_in_context(ctx)
break
net2(data[0:1])


## Calling Block as a function¶

Notice that MLP is a class and thus its instantiation, net2, is an object. If you’re a casual Python user, you might be surprised to see that we can call an object as a function. This is a syntactic convenience owing to Python’s __call__ method. Basically, gluon.Block.__call__(x) is defined so that net(data) behaves identically to net.forward(data). Since passing data through models is so fundamental and common, you’ll be glad to save these 8 characters many times per day.

## So what is a Block?¶

In gluon, a Block is a generic component in a neural network. The entire network is a Block, each layer is a Block, and we can even have repeating sequences of layers that form an intermediate Block.

This might sounds confusing, so let’s make it simple. Each neural network has to do the following things: 1. Store parameters 2. Accept inputs 3. Produce outputs (the forward pass) 4. Take derivatives (the backward pass)

This can be said for the network as a whole, but it can also be said of each individual layer. A single fully-connected layer is parameterized by a weight matrix and a bias vector, produces outputs from inputs, and, given the derivative of some objective with respect to its outputs, can calculate the derivative with respect to its inputs.

Fortunately, MXNet can take derivatives automatically. So we only have to define the forward pass (forward(self, x)). Then, using mxnet.autograd, gluon can handle the backward pass. This is quite a powerful interface. For example we could define the forward pass for some component to take multiple inputs and combine them in arbitrary ways. We can even compose the forward() function such that it throws together a different architecture on the fly depending on some conditions that we could specify in Python. As long as the result is an NDArray, we’re in the clear.

## What’s the deal with name_scope()?¶

The next thing you might have noticed is that we added all of our layers inside a with net1.name_scope(): block. This coerces gluon to give each parameter an appropriate name, indicating which model it belongs to, e.g. sequential8_dense2_weight. Keeping these names straight makes our lives much easier once we start writing more complex code where we might be working with multiple models and saving and loading the parameters of each. It helps us to make sure that we associate each weight with the right model.

## Demystifying nn.Sequential¶

So Sequential is basically a way of throwing together a Block on the fly. Let’s revisit the Sequential version of our multilayer perceptron.

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net1 = gluon.nn.Sequential()
with net1.name_scope():


In just 5 lines and 183 characters, we defined a multilayer perceptron with three fully-connected layers, each parametrized by weight matrix and bias term. We also specified the ReLU activation function for the hidden layers.

Sequential itself subclasses Block and maintains a list of _children. Then, every time we call net1.add(...) our net simply registers a new child. We can actually pass in an arbitrary Block, even layers that we write ourselves.

When we call forward on a Sequential, it executes the following code:

def forward(self, x):
for block in self._children:
x = block(x)
return x


Basically, it calls each child on the output of the previous one, returning the final output at the end of the chain.

## Shape inference¶

One of the first things you might notice is that for each layer, we only specified the number of nodes output, we never specified how many input nodes! You might wonder, how does gluon know that the first weight matrix should be $$784 \times 128$$ and not $$42 \times 128$$. In fact it doesn’t. We can see this by accessing the network’s parameters.

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print(net1.collect_params())


Take a look at the shapes of the weight matrices: (128,0), (64, 0), (10, 0). What does it mean to have zero dimension in a matrix? This is gluon’s way of marking that the shape of these matrices is not yet known. The shape will be inferred on the fly once the network is provided with some input.

So when we initialize our parameters, you might wonder, what precisely is happening?

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net1.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)


In this situation, gluon is not actually initializing any parameters! Instead, it’s making a note of which initializer to associate with each parameter, even though its shape is not yet known. The parameters are instantiated and the initializer is called once we provide the network with some input.

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net1(data)
print(net1.collect_params())


This shape inference can be extremely useful at times. For example, when working with convnets, it can be quite a pain to calculate the shape of various hidden layers. It depends on both the kernel size, the number of filters, the stride, and the precise padding scheme used which can vary in subtle ways from library to library.

## Specifying shape manually¶

If we want to specify the shape manually, that’s always an option. We accomplish this by using the in_units argument when adding each layer.

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net2 = gluon.nn.Sequential()
with net2.name_scope():

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net2.collect_params().initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)