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Setup

I found a paper on that has a varient on normal auto-encoders (contractive) which for its gradient uses the following regularization penalty:

$$\left|\left|J_f(x)\right|\right|^2_F = \sum_{ij}{\left( \frac{\partial h_j(x)}{\partial x_i} \right)}^2$$

where $\left|\left|\cdot\right|\right|_F^2$ is the Frobenius norm, $h$ is the hidden units, and $x$ is the input. The paper also gives an alternative form (when a sigmoid is used for $f$) to the equation that looks like:

$$\left|\left|J_f(x)\right|\right|^2_F = \sum_{i=1}^{d_h}(h_i(1-h_i))^2\sum_{j=1}^{d_x}W^2_{ij}$$

Question 1

As per usual, no actual derivation is given to get the second form of the equation in the paper. I attempted to derive it myself, but would greatly appreciate it if someone could check my work and let me know what mistakes I might have made.

$$ a(x) = W^T x + b $$ $$ h(x) = f(a(x)) $$ $$ f(x) = \frac{1}{1+e^{-x}} $$

Then, using the chain rule:

$$ \frac{\partial h(x)}{\partial x} = \frac{\partial h(x)}{\partial a(x)} \frac{\partial a(x)}{\partial x} $$

Using the standard derivation of the sigmoid: $$ \frac{\partial h(x)}{\partial a(x)} = f(a(x))(1 - f(a(x))) = h(1- h) $$

and:

$$ \frac{\partial a(x)}{\partial x} = W $$

Thus, finally:

$$ \frac{\partial h(x)}{\partial x} = h(1-h)W $$

Question 2

The problem I run into is that I'm not entirely sure of how to take the derivative of $\left(\frac{\partial h(x)}{\partial x}\right)^2$ with respect to $W$ in order to be able to get the gradient. If I square my previous result, I should get:

$$ \frac{\partial \left[h(1-h)\right]^2W^2}{\partial W} $$

which I would think would give:

$$ 2\left[h(1-h)\right]^2 W $$

And I'm having difficulty in interpreting this. I am a bit confused about whether h(1-h) is then an dot product. If so, does that just give me a scalar multiplied by W? If not, and it's an element-wise multiplication, then I think the dimensionality would be all wrong.

Or perhaps I did all of this incorrectly. Any help would be greatly appreciated!

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when I interpret your equations correctly, the $W$ is supposed to be a matrix. This means that $a(x)$ is a vector and the then chain rule actually reads: $$\frac{\partial h_i}{\partial x_j} = \sum_k \frac{\partial h_i}{\partial a_k} \frac{\partial a_k}{\partial x_j}.$$

In matrix notation, the second term is $\frac{\partial a}{\partial x} = W^\top$. If I interpret your equations correctly, $f(x)$ is applied to each element of $a$ individually. Therefore, $\frac{\partial h_i}{\partial a_j} = \delta_{ij}h(a_j)(1-h(a_j))$ which means that it is a diagonal matrix with the term $h(a_j)(1-h(a_j))$ as the $j$th entry. Therefore $$\frac{\partial h_i}{\partial x_j} = \sum_k \delta_{ik} h(a_k)(1-h(a_k)) W_{kj} = h(a_i)(1-h(a_i)) W_{ij}.$$ Thus, $$\sum_{ij}\left(\frac{\partial h_i}{\partial x_j}\right)^2 =\sum_{ij} h(a_i)^2(1-h(a_i))^2 W_{ij}^2 = \sum_{i} h(a_i)^2(1-h(a_i))^2 \sum_{i} W_{ij}^2$$

Edit (second derivative): Since I am guessing that you want the derivative of the regularizer w.r.t to $W$, here is what I get (please check it numerically for correctness)

$$\frac{\partial}{\partial W_{kl}}\|J_{f}\|_{F}^{2}=\frac{\partial}{\partial W_{kl}}\sum_{i}h(a_{i})^{2}(1-h(a_{i}))^{2}\sum_{j}W_{ij}^{2}$$ $$=\sum_{i}\left(h(a_{i})^{2}(1-h(a_{i}))^{2}\sum_{j}\delta_{ik}\delta_{jl}2W_{ij}+\left(\sum_{j}W_{ij}^{2}\right)\frac{\partial}{\partial W_{kl}}h(a_{i})^{2}(1-h(a_{i}))^{2}\right)$$ $$=h(a_{k})^{2}(1-h(a_{k}))^{2}2W_{kl}+\sum_{i}\left(\sum_{j}W_{ij}^{2}\right)\left(2h(a_{i})h'(a_{i})\frac{\partial a_{i}}{\partial W_{kl}}\cdot(1-h(a_{i}))^{2}-h(a_{i})^{2}2(1-h(a_{i}))h'(a_{i})\frac{\partial a_{i}}{\partial W_{kl}}\right)$$ $$=2h(a_{k})^{2}(1-h(a_{k}))^{2}W_{kl}+\sum_{i}\left(\sum_{j}W_{ij}^{2}\right)2\left(h(a_{i})^{2}(1-h(a_{i}))^{3}\delta_{ik}x_{l}-h(a_{i})^{3}(1-h(a_{i}))^{2}\delta_{ik}x_{l}\right)$$ $$=2h(a_{k})^{2}(1-h(a_{k}))^{2}W_{kl}+\left(\sum_{j}W_{kj}^{2}\right)2h(a_{k})^{2}(1-h(a_{k}))^{3}x_{l}-\left(\sum_{j}W_{kj}^{2}\right)2h(a_{k})^{3}(1-h(a_{k}))^{2}x_{l}$$ $$=2h(a_{k})^{2}(1-h(a_{k}))^{2}\left(W_{kl}+x_{l}\left(1-2h(a_{k})\right)\left(\sum_{j}W_{kj}^{2}\right)\right)$$

Sory for the mess but I wanted you to be able to follow my calculations in case the numerical derivative turns out to be not correct.

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  • $\begingroup$ I'm sure you're right, but I'm not sure I follow why h_i' should be the sum over h_i/a_k * a_k/x_j due to a(x) being a vector (Which it is) versus the non-summed version I have above. Also, any advice on what the final derivative looks like? Thanks! $\endgroup$ – Ranon Aug 26 '11 at 16:56
  • $\begingroup$ Oh, okay, I'm guessing that's because the number is being taken so you have to sum the activation values. I was originally hoping to understand it without the summation because it's easier to see how to take its derivative.. $\endgroup$ – Ranon Aug 26 '11 at 22:16
  • $\begingroup$ Hi Ranon, the summation is because of the chain rule: Say you have a function g from R to R^n and a function f from R^n to R, then the derivative of f(g(x)) w.r.t. x is sum_i df/dg_i dg_i/dx. I think the confusion might be that you look at your function h as from R to R but the way it is used it is really R^n to R where h is applied to each component of a. That's also the reason why the Jacobian is diagonal. $\endgroup$ – fabee Aug 27 '11 at 15:20
  • $\begingroup$ What do you mean by final derivative? It should be all there, or did I forget anything? $\endgroup$ – fabee Aug 27 '11 at 15:21
  • $\begingroup$ Thanks for the explanation, it's not something I've thought about before. By final derivative I meant the derivative of $\sum_{ij}(\frac{\partial h_i}{\partial x_j})^2$. $\endgroup$ – Ranon Aug 27 '11 at 15:26
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fabee's calculations are correct. You can also express the terms without assuming a sigmoid activation function.

To simplify the notation, let $h'_i = \frac{\partial h_i}{\partial a_i}$.

The term is: $$ \| J_f \|_F^2 = \sum_{i} \left( h'_i \right)^2 \sum_j W_{i,j}^2 $$

The gradient is: $$ \frac{\partial }{\partial W_{k,l}} \| J_f \|_F^2 = 2 (h'_k)^2 W_{kl} + \left(\sum_j W_{kj}^2\right) \frac{\partial (h'_k)^2}{\partial a_k} x_l $$

You can then use any activation functions. Let's see some examples.

Sigmoid

$ h_k = \text{sig}(a_k) $

$ (h'_k)^2 = h_k^2 (1 - h_k)^2 $

$ \frac{\partial (h'_k)^2}{\partial a_k} = 2 h_k^2 (1 -h_k)^2 (1 - 2 h_k)$

You can verify that you get the same term as fabee's.

SoftPlus

$ h_k = \ln (1 + \exp(a_k))$

$ (h'_k)^2 = \text{sig}(a_k)^2 $

$ \frac{\partial (h'_k)^2}{\partial a_k} = 2 \text{sig}(a_k)^2 - 2 \text{sig}(a_k)^3$

Tanh

$ h_k = \tanh(a_k) $

$(h'_k)^2 = (1 - h_k^2)^2$

$ \frac{\partial (h'_k)^2}{\partial a_k} = -4 h_k (1 - h_k^2)^2 $

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