Python / Python Deep Learning and Neural Networks Interview Questions

Why does weight initialization matter in neural networks, and what is the difference between Xavier and He initialization?

If weights are initialized too small, activations and gradients shrink layer by layer — a form of vanishing gradient from the start. If too large, they explode. The goal of principled initialisation is to keep the variance of activations and gradients roughly constant across all layers at the start of training.

Xavier (Glorot) initialisation draws weights from a distribution with variance 2/(fan_in + fan_out). It was derived assuming linear activations (or tanh in the original paper) by requiring that the variance of the layer's output equals the variance of its input. He (Kaiming) initialisation uses variance 2/fan_in, derived for ReLU activations specifically — since ReLU zeroes out half the input on average, the variance of the output is halved, so doubling the initial weight variance compensates for this. Using Xavier with ReLU causes variance to shrink by roughly half per layer, eventually vanishing.

import torch
import torch.nn as nn

# Default PyTorch Linear layers use Kaiming Uniform initialisation
layer = nn.Linear(256, 128)
print(layer.weight.std())  # approximately sqrt(2/256) â 0.088

# Explicit initialisation
def init_weights(m):
    if isinstance(m, nn.Linear):
        # Xavier: good for sigmoid/tanh activations
        nn.init.xavier_uniform_(m.weight)
        # He/Kaiming: good for ReLU activations (default in PyTorch)
        # nn.init.kaiming_uniform_(m.weight, nonlinearity='relu')
        nn.init.zeros_(m.bias)

model = nn.Sequential(
    nn.Linear(784, 256), nn.ReLU(),
    nn.Linear(256, 128), nn.ReLU(),
    nn.Linear(128, 10)
)
model.apply(init_weights)  # apply init_weights to every sub-module

# Verifying activation variance stays stable across layers:
x = torch.randn(100, 784)
for layer in model:
    x = layer(x)
    print(f'{layer.__class__.__name__}: std={x.std():.3f}')
# With He init + ReLU: std should remain near 1.0 throughout

Take quiz

Why does Xavier initialization use variance 2/(fan_in + fan_out) rather than a constant?It is computationally cheaper than computing the exact variance

✗ Try again.

It is derived analytically to keep the variance of a layer's output equal to the variance of its input under linear activations — preventing activations from growing or shrinking across layers

✓ Correct! Well done.

It prevents the bias terms from being initialized to zero

✗ Try again.

It is based on the number of training examples rather than layer dimensions

✗ Try again.

Why should He initialization be used with ReLU activations instead of Xavier?He initialization is newer and always better than Xavier

✗ Try again.

ReLU zeros out half the inputs on average, halving the output variance per layer; He initialization doubles the initial weight variance (using 2/fan_in) to compensate for this zeroing effect

✓ Correct! Well done.

Xavier initialization produces negative weights that ReLU cannot process

✗ Try again.

He initialization prevents the dying ReLU problem by using larger weights

✗ Try again.

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Python / Python Deep Learning and Neural Networks Interview Questions

Why does weight initialization matter in neural networks, and what is the difference between Xavier and He initialization?

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