Vanishing — sigmoid is the culprit

Numerical Stability and Initialization

Why initialization matters

Why was deep learning hard before 2012? Nobody could train deep networks reliably — gradients either died at zero or blew up to infinity.

Three ingredients fixed it:

  1. Non-saturating activations — ReLU and friends.
  2. Careful weight initialization — Xavier, Kaiming.
  3. Symmetry breaking — random init, not zero init.

This deck makes the failure modes concrete.

The chain rule turns the gradient into a product

For an L-layer network with hidden states \mathbf{h}^{(1)}, \mathbf{h}^{(2)}, \ldots, the gradient of the loss with respect to a weight in layer \ell is

\frac{\partial \mathcal{L}}{\partial \mathbf{W}^{(\ell)}} = \underbrace{\frac{\partial \mathcal{L}}{\partial \mathbf{h}^{(L)}}}_{\text{loss}} \cdot \underbrace{\frac{\partial \mathbf{h}^{(L)}}{\partial \mathbf{h}^{(L-1)}}}_{\mathbf{M}_L} \cdots \underbrace{\frac{\partial \mathbf{h}^{(\ell+1)}}{\partial \mathbf{h}^{(\ell)}}}_{\mathbf{M}_{\ell+1}} \cdot \frac{\partial \mathbf{h}^{(\ell)}}{\partial \mathbf{W}^{(\ell)}}.

It’s a product of L - \ell Jacobian matrices. Two ways this product can misbehave:

  • All Jacobians have spectral radius < 1 → product shrinks geometrically → vanishing gradient.
  • All Jacobians have spectral radius > 1 → product grows geometrically → exploding gradient.

Setup

%matplotlib inline
from d2l import mxnet as d2l
from mxnet import autograd, np, npx
npx.set_np()
x = np.arange(-8.0, 8.0, 0.1)
x.attach_grad()
with autograd.record():
    y = npx.sigmoid(x)
y.backward()

d2l.plot(x, [y, x.grad], legend=['sigmoid', 'gradient'], figsize=(4.5, 2.5))

The sigmoid’s derivative peaks at \sigma'(0) = 0.25 and collapses to zero at the tails. In a 10-layer stack 0.25^{10} \approx 10^{-6} — gradients at layer 1 are a millionth of those near the output. ReLU fixes this: derivative is exactly 1 wherever the unit is active.

Exploding — random-matrix products

Multiply 100 random 4\times4 Gaussian matrices and watch the entries:

M = np.random.normal(size=(4, 4))
print('a single matrix', M)
for i in range(100):
    M = np.dot(M, np.random.normal(size=(4, 4)))
print('after multiplying 100 matrices', M)

Random Gaussian matrices have spectral radius > 1, so the product diverges. Same effect on gradients in a deep net with poorly scaled weights — loss goes to NaN in a few hundred steps.

Crash modes you’ll actually see

  • Loss spikes mid-training — exploding gradient on a bad batch.
  • Loss is NaN from step 1 — exploding init.
  • Loss won’t go down — vanishing gradient (or learning rate too small).

The fix: keep variance constant through depth

Forward pass through a linear layer with n_{\text{in}} inputs:

o_i = \sum_{j=1}^{n_{\text{in}}} w_{ij}\, x_j.

If w_{ij} \sim \mathcal{N}(0, \sigma^2) and inputs are i.i.d. with variance \gamma^2:

\mathbb{E}[o_i] = 0,\quad \mathrm{Var}[o_i] = n_{\text{in}}\, \sigma^2\, \gamma^2.

For variance to be preserved layer-to-layer (\mathrm{Var}[o] = \gamma^2):

\boxed{\sigma^2 = \frac{1}{n_{\text{in}}}}.

Same argument for the backward pass gives \sigma^2 = 1/n_{\text{out}}. Can’t satisfy both — so Xavier averages them.

Xavier and Kaiming

Xavier / Glorot (2010):

\sigma^2 = \frac{2}{n_{\text{in}} + n_{\text{out}}}.

Preserves variance both forward and backward. Designed for \tanh / sigmoid.

Kaiming / He (2015):

\sigma^2 = \frac{2}{n_{\text{in}}}.

Same idea, but compensates for ReLU halving the post-activation variance. Default for modern CNNs and Transformers.

Both ship as defaults in every framework. Bias starts at 0.

Symmetry breaking

Set every weight to the same constant c:

  • Every hidden unit in a layer computes the same thing.
  • Every gradient is the same.
  • After every update, the weights are still the same.
  • An h-unit layer behaves like a 1-unit layer forever.

Initialize randomly — even tiny noise breaks the permutation symmetry between hidden units. (SGD alone doesn’t.)

Modern building blocks

Init alone gets you “trains a 10-layer net without NaN”. Modern best practice for hundreds of layers stacks more on top:

  • BatchNorm / LayerNorm — re-normalize activations to unit variance during training, removing the burden from init.
  • Residual connections — h^{(\ell+1)} = h^{(\ell)} + f(h^{(\ell)}) gives the gradient a direct path back, so the multiplicative shrinkage doesn’t compound.
  • GroupNorm / RMSNorm — variants robust to small batch sizes / sequence models.
  • Mixed-precision training — keep master weights in fp32 to avoid underflow even when activations are fp16.

The chapter on Modern CNNs revisits these.

Recap

  • Gradients in a deep net are products of per-layer Jacobians — they vanish or explode without care.
  • Vanishing: saturated activations (sigmoid/tanh) + small weights → no gradient at the bottom layers.
  • Exploding: large weights → NaN.
  • Two complementary fixes: non-saturating activations (ReLU) and variance-preserving init (Xavier / Kaiming).
  • Random init breaks symmetry between units; zero init collapses every hidden layer to a single neuron.
  • BatchNorm + residuals + careful init together get you to 100+ layers reliably.