Python Coding challenge - Day 401| What is the output of the following Python Code?

 

Code Explanation:

Step 1: Import PyTorch

import torch

This imports the PyTorch library, which is used for deep learning, tensor computations, and GPU acceleration.

torch provides various tensor operations similar to NumPy but optimized for deep learning.

Step 2: Create a Tensor

x = torch.tensor([1.0, 2.0, 3.0])

This creates a 1D tensor with floating-point values [1.0, 2.0, 3.0].

torch.tensor([...]) is used to initialize a tensor.

The values are explicitly written as floating-point (1.0, 2.0, 3.0) because PyTorch defaults to float32 if no data type is specified.

Step 3: Apply ReLU Activation

y = torch.relu(x - 2)

First, the expression x - 2 is computed:

x - 2  # Element-wise subtraction

This means:

[1.0, 2.0, 3.0] - 2

= [-1.0, 0.0, 1.0]

Next, torch.relu() is applied:

torch.relu() is the Rectified Linear Unit (ReLU) activation function, which is widely used in neural networks.

It is defined as:

𝑅

𝑒

eLU(x)=max(0,x)

Applying ReLU to [-1.0, 0.0, 1.0]:

lua

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ReLU(-1.0) = max(0, -1.0) = 0.0

ReLU(0.0) = max(0, 0.0) = 0.0

ReLU(1.0) = max(0, 1.0) = 1.0

Final result for y:

y = [0.0, 0.0, 1.0]

Step 4: Print Output

print(y)

This prints the final tensor y after applying ReLU:

tensor([0., 0., 1.])

This means:

-1.0 became 0.0

0.0 remained 0.0

1.0 remained 1.0

Final Output

tensor([0., 0., 1.])