Assignment Operators

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What Are Assignment Operators?

In Python, assignment operators are used to assign values to variables. The most basic one is =, but there are many others that combine arithmetic with assignment, such as +=, -=, etc.

Step-by-Step Breakdown with Real Examples

1. Basic Assignment =

x = 5
print(x)

Note:

• This assigns the value 5 to variable x.

• In machine learning, you might initialize weights like this: weight = 0.01.

2. Add and Assign +=

x = 5
x += 3  # same as x = x + 3
print(x)  # Output: 8

Use Case in ML: Updating the learning rate or loss:

loss = 0.0
loss += 0.25  # adding batch loss to total loss

3. Subtract and Assign -=

x = 10
x -= 4  # same as x = x - 4
print(x)  # Output: 6

Use Case in ML: Gradient Descent weight update:

weight = 0.5
gradient = 0.1
learning_rate = 0.01
weight -= learning_rate * gradient

4. Multiply and Assign *=

x = 4
x *= 3  # same as x = x * 3
print(x)  # Output: 12

Use Case in ML: Scaling weights or input:

input = 0.7
scale = 2
input *= scale  # Normalize or scale input

5. Divide and Assign /=

x = 20
x /= 4  # same as x = x / 4
print(x)  # Output: 5.0

Use Case in ML: Averaging losses:

total_loss = 1.2
batches = 4
avg_loss = total_loss
avg_loss /= batches

6. Modulus and Assign %= (Remainder)

x = 10
x %= 3  # same as x = x % 3
print(x)  # Output: 1

Use Case in ML or loops: Logging every N steps:

if step % 100 == 0:
    print("Logging at step", step)

7. Exponent and Assign **=

x = 2
x **= 3  # same as x = x ** 3
print(x)  # Output: 8

Use Case in ML: Exponential learning rate decay or power-related transformations:

lr = 0.1
decay_factor = 0.9
lr **= 2  # simulate decay

8. Floor Divide and Assign //=

x = 9
x //= 2  # same as x = x // 2
print(x)  # Output: 4

Use Case in ML: Chunking data into batches:

data_size = 1000
batch_size = 128
num_batches = data_size
num_batches //= batch_size

Summary Table

Operator	Description	Example (x = 5)	Result (x)
=	Assign	x = 5	5
+=	Add and assign	x += 3	8
-=	Subtract and assign	x -= 2	6
*=	Multiply and assign	x *= 4	20
/=	Divide and assign	x /= 5	4.0
%=	Modulus and assign	x %= 3	1
**=	Exponent and assign	x **= 2	25
//=	Floor divide and assign	x //= 2	2

Practical Machine Learning Example

Let’s simulate a basic training loop to see assignment operators in action:

# Simulating training loop
epochs = 5
learning_rate = 0.1
weight = 0.5
gradient = 0.2

for epoch in range(epochs):
    weight -= learning_rate * gradient  # Gradient Descent
    print(f"Epoch {epoch+1}: weight = {weight}")

Output:

Epoch 1: weight = 0.48
Epoch 2: weight = 0.45999999999999996
Epoch 3: weight = 0.43999999999999995
Epoch 4: weight = 0.41999999999999993
Epoch 5: weight = 0.3999999999999999

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