# However What’s Backpropagation, Actually? (Half 1) | by Matthew Chak | Feb, 2024

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Regardless of performing some work and analysis within the AI ecosystem for a while, I didn’t actually cease to consider backpropagation and gradient updates inside neural networks till just lately. This text seeks to rectify that and can hopefully present an intensive but easy-to-follow dive into the subject by implementing a easy (but considerably highly effective) neural community framework from scratch.

Essentially, a neural community is only a mathematical perform from our enter house to our desired output house. In reality, we are able to successfully “unwrap” any neural community right into a perform. Contemplate, as an illustration, the next easy neural community with two layers and one enter:

We are able to now assemble an equal perform by going forwards layer by layer, ranging from the enter. Let’s comply with our closing perform layer by layer:

- On the enter, we begin with the id perform
*pred(x) = x* - On the first linear layer, we get
*pred(x) = w*₁*x + b*₁ - The ReLU nets us
*pred(x) = max(0, w*₁*x + b*₁) - On the closing layer, we get
*pred(x) = w*₂*(max(0, w*₁*x + b*₁)) +*b*₂

With extra difficult nets, these capabilities in fact get unwieldy, however the level is that we are able to assemble such representations of neural networks.

We are able to go one step additional although — capabilities of this type should not extraordinarily handy for computation, however we are able to parse them right into a extra helpful type, particularly a syntax tree. For our easy internet, the tree would appear like this:

On this tree type, our leaves are parameters, constants, and inputs, and the opposite nodes are **elementary operations** whose arguments are their kids. After all, these elementary operations don’t need to be binary — the sigmoid operation, as an illustration, is unary (and so is ReLU if we don’t symbolize it as a max of 0 and x), and we are able to select to help multiplication and addition of multiple enter.

By pondering of our community as a tree of those elementary operations, we are able to now do loads of issues very simply with recursion, which can type the idea of each our backpropagation and ahead propagation algorithms. In code, we are able to outline a recursive neural community class that appears like this:

`from dataclasses import dataclass, area`

from typing import Listing@dataclass

class NeuralNetNode:

"""A node in our neural community tree"""

kids: Listing['NeuralNetNode'] = area(default_factory=checklist)

def op(self, x: Listing[float]) -> float:

"""The operation that this node performs"""

elevate NotImplementedError

def ahead(self) -> float:

"""Consider this node on the given enter"""

return self.op([child.forward() for child in self.children])

# That is only for comfort

def __call__(self) -> Listing[float]:

return self.ahead()

def __repr__(self):

return f'{self.__class__.__name__}({self.kids})'

Suppose now that we’ve a differentiable loss perform for our neural community, say MSE. Recall that MSE (for one pattern) is outlined as follows:

We now want to replace our parameters (the inexperienced circles in our tree illustration) given the worth of our loss. To do that, we’d like the by-product of our loss perform with respect to every parameter. Calculating this immediately from the loss is extraordinarily tough although — in spite of everything, our MSE is calculated by way of the worth predicted by our neural internet, which might be a very difficult perform.

That is the place very helpful piece of arithmetic — the chain rule — comes into play. As a substitute of being pressured to compute our extremely complicated derivatives from the get-go, we are able to as a substitute compute a collection of easier derivatives.

It seems that the chain rule meshes very properly with our recursive tree construction. The concept mainly works as follows: assuming that we’ve easy sufficient elementary operations, every elementary operation is aware of its by-product with respect to all of its arguments. Given the by-product from the dad or mum operation, we are able to thus compute the by-product of every baby operation with respect to the loss perform via easy multiplication. For a easy linear regression mannequin utilizing MSE, we are able to diagram it as follows:

After all, a few of our nodes don’t do something with their derivatives — particularly, solely our leaf nodes care. However now every node can get the by-product of its output with respect to the loss perform via this recursive course of. We are able to thus add the next strategies to our NeuralNetNode class:

`def grad(self) -> Listing[float]:`

"""The gradient of this node with respect to its inputs"""

elevate NotImplementedErrordef backward(self, derivative_from_parent: float):

"""Propagate the by-product from the dad or mum to the youngsters"""

self.on_backward(derivative_from_parent)

deriv_wrt_children = self.grad()

for baby, derivative_wrt_child in zip(self.kids, deriv_wrt_children):

baby.backward(derivative_from_parent * derivative_wrt_child)

def on_backward(self, derivative_from_parent: float):

"""Hook for subclasses to override. Issues like updating parameters"""

move

**Train 1: **Strive creating one in all these bushes for a easy linear regression mannequin and carry out the recursive gradient updates by hand for a few steps.

*Word: For simplicity’s sake, we require our nodes to have just one dad or mum (or none in any respect). If every node is allowed to have a number of dad and mom, our backwards() algorithm turns into considerably extra difficult as every baby must sum the by-product of its dad and mom to compute its personal. We are able to do that iteratively with a topological type (e.g. see **here**) or nonetheless recursively, i.e. with reverse accumulation (although on this case we would want to do a second move to really replace the entire parameters). This isn’t terribly tough, so I’ll depart it as an train to the reader (and can speak about it extra partly 2, keep tuned).*

## Constructing Fashions

The remainder of our code actually simply entails implementing parameters, inputs, and operations, and naturally operating our coaching. Parameters and inputs are pretty easy constructs:

`import random`@dataclass

class Enter(NeuralNetNode):

"""A leaf node that represents an enter to the community"""

worth: float=0.0

def op(self, x):

return self.worth

def grad(self) -> Listing[float]:

return [1.0]

def __repr__(self):

return f'{self.__class__.__name__}({self.worth})'

@dataclass

class Parameter(NeuralNetNode):

"""A leaf node that represents a parameter to the community"""

worth: float=area(default_factory=lambda: random.uniform(-1, 1))

learning_rate: float=0.01

def op(self, x):

return self.worth

def grad(self):

return [1.0]

def on_backward(self, derivative_from_parent: float):

self.worth -= derivative_from_parent * self.learning_rate

def __repr__(self):

return f'{self.__class__.__name__}({self.worth})'

Operations are barely extra difficult, although not an excessive amount of so — we simply must calculate their gradients correctly. Under are implementations of some helpful operations:

`import math`@dataclass

class Operation(NeuralNetNode):

"""A node that performs an operation on its inputs"""

move

@dataclass

class Add(Operation):

"""A node that provides its inputs"""

def op(self, x):

return sum(x)

def grad(self):

return [1.0] * len(self.kids)

@dataclass

class Multiply(Operation):

"""A node that multiplies its inputs"""

def op(self, x):

return math.prod(x)

def grad(self):

grads = []

for i in vary(len(self.kids)):

cur_grad = 1

for j in vary(len(self.kids)):

if i == j:

proceed

cur_grad *= self.kids[j].ahead()

grads.append(cur_grad)

return grads

@dataclass

class ReLU(Operation):

"""

A node that applies the ReLU perform to its enter.

Word that this could solely have one baby.

"""

def op(self, x):

return max(0, x[0])

def grad(self):

return [1.0 if self.children[0].ahead() > 0 else 0.0]

@dataclass

class Sigmoid(Operation):

"""

A node that applies the sigmoid perform to its enter.

Word that this could solely have one baby.

"""

def op(self, x):

return 1 / (1 + math.exp(-x[0]))

def grad(self):

return [self.forward() * (1 - self.forward())]

The operation superclass right here is just not helpful but, although we are going to want it to extra simply discover our mannequin’s inputs later.

Discover how typically the gradients of the capabilities require the values from their kids, therefore we require calling the kid’s ahead() methodology. We’ll contact upon this extra in somewhat bit.

Defining a neural community in our framework is a bit verbose however is similar to developing a tree. Right here, as an illustration, is code for a easy linear classifier in our framework:

`linear_classifier = Add([`

Multiply([

Parameter(),

Input()

]),

Parameter()

])

## Utilizing Our Fashions

To run a prediction with our mannequin, we’ve to first populate the inputs in our tree after which name ahead() on the dad or mum. To populate the inputs although, we first want to seek out them, therefore we add the next methodology to our **Operation** class (we don’t add this to our NeuralNetNode class because the Enter kind isn’t outlined there but):

`def find_input_nodes(self) -> Listing[Input]:`

"""Discover the entire enter nodes within the subtree rooted at this node"""

input_nodes = []

for baby in self.kids:

if isinstance(baby, Enter):

input_nodes.append(baby)

elif isinstance(baby, Operation):

input_nodes.prolong(baby.find_input_nodes())

return input_nodes

We are able to now add the predict() methodology to the Operation class:

`def predict(self, inputs: Listing[float]) -> float:`

"""Consider the community on the given inputs"""

input_nodes = self.find_input_nodes()

assert len(input_nodes) == len(inputs)

for input_node, worth in zip(input_nodes, inputs):

input_node.worth = worth

return self.ahead()

**Train 2**: The present manner we carried out predict() is considerably inefficient since we have to traverse the tree to seek out all of the inputs each time we run predict(). Write a compile() methodology that caches the operation’s inputs when it’s run.

Coaching our fashions is now very easy:

`from typing import Callable, Tuple`def train_model(

mannequin: Operation,

loss_fn: Callable[[float, float], float],

loss_grad_fn: Callable[[float, float], float],

information: Listing[Tuple[List[float], float]],

epochs: int=1000,

print_every: int=100

):

"""Practice the given mannequin on the given information"""

for epoch in vary(epochs):

total_loss = 0.0

for x, y in information:

prediction = mannequin.predict(x)

total_loss += loss_fn(y, prediction)

mannequin.backward(loss_grad_fn(y, prediction))

if epoch % print_every == 0:

print(f'Epoch {epoch}: loss={total_loss/len(information)}')

Right here, as an illustration, is how we’d practice a linear Fahrenheit to Celsius classifier utilizing our framework:

`def mse_loss(y_true: float, y_pred: float) -> float:`

return (y_true - y_pred) ** 2def mse_loss_grad(y_true: float, y_pred: float) -> float:

return -2 * (y_true - y_pred)

def fahrenheit_to_celsius(x: float) -> float:

return (x - 32) * 5 / 9

def generate_f_to_c_data() -> Listing[List[float]]:

information = []

for _ in vary(1000):

f = random.uniform(-1, 1)

information.append([[f], fahrenheit_to_celsius(f)])

return information

linear_classifier = Add([

Multiply([

Parameter(),

Input()

]),

Parameter()

])

train_model(linear_classifier, mse_loss, mse_loss_grad, generate_f_to_c_data())

After operating this, we get

`print(linear_classifier)`

print(linear_classifier.predict([32]))>> Add(kids=[Multiply(children=[Parameter(0.5555555555555556), Input(0.8930639016107234)]), Parameter(-17.777777777777782)])

>> -1.7763568394002505e-14

Which appropriately corresponds to a linear classifier with weight 0.56, bias -17.78 (which is the Fahrenheit to Celsius formulation)

We are able to, in fact, additionally practice way more complicated fashions, e.g. right here is one for predicting if some extent (x, y) is above or beneath the road y = x:

`def bce_loss(y_true: float, y_pred: float, eps: float=0.00000001) -> float:`

y_pred = min(max(y_pred, eps), 1 - eps)

return -y_true * math.log(y_pred) - (1 - y_true) * math.log(1 - y_pred)def bce_loss_grad(y_true: float, y_pred: float, eps: float=0.00000001) -> float:

y_pred = min(max(y_pred, eps), 1 - eps)

return (y_pred - y_true) / (y_pred * (1 - y_pred))

def generate_binary_data():

information = []

for _ in vary(1000):

x = random.uniform(-1, 1)

y = random.uniform(-1, 1)

information.append([(x, y), 1 if y > x else 0])

return information

model_binary = Sigmoid(

[

Add(

[

Multiply(

[

Parameter(),

ReLU(

[

Add(

[

Multiply(

[

Parameter(),

Input()

]

),

Multiply(

[

Parameter(),

Input()

]

),

Parameter()

]

)

]

)

]

),

Parameter()

]

)

]

)

train_model(model_binary, bce_loss, bce_loss_grad, generate_binary_data())

Then we fairly get

`print(model_binary.predict([1, 0]))`

print(model_binary.predict([0, 1]))

print(model_binary.predict([0, 1000]))

print(model_binary.predict([-5, 3]))

print(model_binary.predict([0, 0]))>> 3.7310797619230176e-66

>> 0.9997781079343139

>> 0.9997781079343139

>> 0.9997781079343139

>> 0.23791579184662365

Although this has affordable runtime, it’s considerably slower than we’d count on. It is because we’ve to name ahead() and re-calculate the mannequin inputs *lots* within the name to backwards(). As such, have the next train:

**Train 3**: Add caching to our community. That’s, on the decision to ahead(), the mannequin ought to return the cached worth from the earlier name to ahead() *if and provided that the inputs haven’t modified because the final name*. Be sure that you run ahead() once more if the inputs have modified.

And that’s about it! We now have a working neural community framework through which we are able to practice simply loads of fascinating fashions (although not networks with nodes that feed into a number of different nodes. This isn’t too tough so as to add — see the notice within the dialogue of the chain rule), although granted it’s a bit verbose. In the event you’d prefer to make it higher, attempt a number of the following:

**Train 4: **When you concentrate on it, extra “complicated” nodes in our community (e.g. Linear layers) are actually simply “macros” in a way — that’s, if we had a neural internet tree that regarded, say, as follows:

what you might be actually doing is that this:

In different phrases, *Linear(inp) *is absolutely only a macro for a tree containing *|inp| + 1 *parameters, the primary of that are weights in multiplication and the final of which is a bias. Every time we see *Linear(inp)*, we are able to thus substitute it for an equal tree composed solely of elementary operations.

For this train, your job is thus to implement the **Macro** class. The category ought to be an **Operation** that recursively replaces itself with elementary operations

*Word: this step might be accomplished at any time when, although it’s probably best so as to add a compile() methodology to the Operation class that it’s important to name earlier than coaching (or add it to your current methodology from Train 2). We are able to, in fact, additionally implement extra complicated nodes in different (maybe extra environment friendly) methods, however that is nonetheless a very good train.*

**Train 5: **Although we don’t actually ever want inner nodes to provide something apart from one quantity as their output, it’s typically good for the foundation of our tree (that’s, our output layer) to provide one thing else (e.g. a listing of numbers within the case of a Softmax). Implement the **Output **class and permit it to provide a Listof[float] as a substitute of only a float. As a bonus, attempt implementing the SoftMax output.

*Word: there are just a few methods of doing this. You can also make Output prolong Operation, after which modify the NeuralNetNode class’ op() methodology to return a Listing[float] as a substitute of only a float. Alternatively, you can create a brand new Node superclass that each Output and Operation prolong. That is probably simpler.*

*Word additional that though these outputs can produce lists, they are going to nonetheless solely get one by-product again from the loss perform — the loss perform will simply occur to take a listing of floats as a substitute of a float (e.g. the Categorical Cross Entropy loss)*

**Train 6: **Keep in mind how earlier within the article we stated that neural nets are simply mathematical capabilities comprised of elementary operations? Add the *funcify()* methodology to the NeuralNetNode class that turns it into such a perform written in human-readable notation (add parentheses as you please). For instance, the neural internet *Add([Parameter(0.1), Parameter(0.2)])* ought to collapse to “0.1 + 0.2” (or “(0.1 + 0.2)”).

*Word: For this to work, inputs ought to be named. In the event you did train 2, title your inputs within the compile() perform. If not, you’ll have to determine a technique to title your inputs — writing a compile() perform remains to be probably the simplest manner.*

**Train 7: **Modify our framework to permit nodes to have a number of dad and mom. I’ll resolve this partly 2.

That’s all for now! In the event you’d like to take a look at the code, you may take a look at this google colab that has every little thing (aside from options to each train however #6, although I could add these partly 2).

Contact me at mchak@calpoly.edu for any inquiries.

*Except in any other case specified, all photos are by the creator.*

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