I have been studying Andrew Ng's Coursera course about Deep Learning. In that he mentions that we calculate the activation functions during the forward pass, and the derivatives $\dfrac{dL}{dz} \ $, $\dfrac{dL}{dw} \ $, and $\dfrac{dL}{db} \ $ during the backward pass.

Consider a single neural node Single neural node

Here, $x_{1} \ ,\ x_{2} \ ,\ ...\ ,\ x_{n}$ are our inputs to the node

$b$ is the bias

$w_{1} ,\ w_{2} ,\ ...\ ,\ w_{n}$ are the weights associated with the inputs

$\hat{y}$ or $a$ is our activation function where $a\ =\ \sigma ( z) \ =\frac{1}{1\ +\ e^{-z}} \ $

$z\ =\ w_{1} x_{1} +w_{2} x_{2} +\dots +w_{n} x_{n} +\ b$

The term $L\ ( a,y) \ =\ -\{\ y\log a\ +\ ( 1-y)\log( 1-a) \ \}$ is our loss function

The way Andrew calculates the derivatives is as follows:

$ \begin{gathered} \dfrac{dL}{dw} \ =\ \dfrac{dL}{dz} \cdot \dfrac{dz}{dw}\\ \\ \dfrac{dL}{dz} \ =\ \dfrac{dL}{da} \cdot \dfrac{da}{dz}\\ \end{gathered}$

We back-propagate to calculate

$\dfrac{dL}{da}\\$ (1-step back)

$\dfrac{da}{dz}\\$ and $\dfrac{dL}{dz} \ =\ \dfrac{dL}{da} \cdot \dfrac{da}{dz}\\$ (2-step back)

$\dfrac{dz}{dw}\\$ and $\dfrac{dL}{dw} \ =\ \dfrac{dL}{dz} \cdot \dfrac{dz}{dw}\\$ (3-step back)

However, my question is, why can't we calculate these derivatives during the forward pass itself? Like so,

$\dfrac{dL}{dw} \ =\ \dfrac{dL}{da} \cdot \dfrac{da}{dz} \cdot \dfrac{dz}{dw}\\$

Step 1: Calculate $z\\$ as well as $\dfrac{dz}{dw}\\$

Step 2: Calculate $a\\$ as well as $\dfrac{da}{dz}\\$ and $\dfrac{da}{dw} \ =\ \dfrac{da}{dz} \cdot \dfrac{dz}{dw}\\$

Step 3: Calculate $L\\$ as well as $\dfrac{dL}{da}\\$ and $\dfrac{dL}{dw} \ =\ \dfrac{dL}{da} \cdot \dfrac{da}{dw}\\$

Is there any reason why we take the back-prop approach, and not calculate both sets of functions together during forward prop?

EDIT: For a network with $n$ layers, we can calculate the weight derivative for a layer $j$, as:

$ \begin{gathered} \dfrac{dL}{dw^{[ j]}} =\dfrac{dL}{da^{[ n]}} \cdot \dfrac{da^{[ n]}}{dz^{[ n]}} \cdot \dfrac{dz^{[ n]}}{da^{[ n-1]}} \dots \dfrac{dz^{[ j+1]}}{da^{[ j]}} \cdot \underbrace{\dfrac{da^{[ j]}}{dz^{[ j]}} \cdot \dfrac{dz^{[ j]}}{dw^{[ j]}}}_{combine\ the\ two}\\ \\ =\dfrac{dL}{da^{[ n]}} \cdot \dfrac{da^{[ n]}}{dz^{[ n]}} \cdot \dfrac{dz^{[ n]}}{da^{[ n-1]}} \dots \underbrace{\dfrac{dz^{[ j+1]}}{da^{[ j]}} \cdot \dfrac{da^{[ j]}}{dw^{[ j]}}}_{combine\ the\ two}\\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \vdots \\ \\ =\underbrace{\dfrac{dL}{da^{[ n]}} \cdot \dfrac{da^{[ n]}}{dw^{[ j]}}}\\ \end{gathered}$

As can be seen, any layer's derivative can be calculated without the need to backtrack

Edit 2: Let's take a 2 layer network

enter image description here

$a^{[ 0]} =\left[\begin{smallmatrix} x_{1}\\ x_{2}\\ \vdots \\ x_{n} \end{smallmatrix}\right]$

$a^{[ 1]} =\left[\begin{smallmatrix} a^{[ 1]}_{1}\\ a^{[ 1]}_{2}\\ \vdots \\ a^{[ 1]}_{m} \end{smallmatrix}\right]$

$w^{[ 1]} =\left[\begin{smallmatrix} n\ weights\ for\ layer\ 1\ node\ 1\\ n\ weights\ for\ layer\ 1\ node\ 2\\ \vdots \\ n\ weights\ for\ layer\ 1\ node\ m \end{smallmatrix}\right] =\left[\begin{smallmatrix} w^{[ 1]}_{1}\\ w^{[ 1]}_{2}\\ \vdots \\ w^{[ 1]}_{m} \end{smallmatrix}\right] =\left[\begin{smallmatrix} w^{[ 1]}_{1( 1)} & w^{[ 1]}_{1( 2)} & \dots & w^{[ 1]}_{1( n)}\\ w^{[ 1]}_{2( 1)} & w^{[ 1]}_{2( 2)} & \dots & w^{[ 1]}_{2( n)}\\ \vdots & \vdots & \dots & \vdots \\ w^{[ 1]}_{m( 1)} & w^{[ 1]}_{m( 2)} & \dots & w^{[ 1]}_{m( n)} \end{smallmatrix}\right]$

Step 1: For the first layer: $z^{[ 1]} \ =\ w^{[ 1] T} a^{[ 0]} =\left[\begin{smallmatrix} z^{[ 1]}_{1}\\ z^{[ 1]}_{2}\\ \vdots \\ z^{[ 1]}_{m} \end{smallmatrix}\right]$

$a^{[ 1]} =\sigma \left( z^{[ 1]}\right) =\left[\begin{smallmatrix} \sigma \left( z^{[ 1]}_{1}\right)\\ \sigma \left( z^{[ 1]}_{2}\right)\\ \vdots \\ \sigma \left( z^{[ 1]}_{m}\right) \end{smallmatrix}\right]$

$\dfrac{\partial a^{[ 1]}}{\partial w^{[ 1]}} \ =\left[\begin{smallmatrix} \dfrac{\partial a}{\partial w} \ for\ layer\ 1\ node\ 1\ \\ \dfrac{\partial a}{\partial w} \ for\ layer\ 1\ node\ 2\\ \vdots \\ \dfrac{\partial a}{\partial w} \ for\ layer\ 1\ node\ m \end{smallmatrix}\right] =\left[\begin{smallmatrix} \dfrac{\partial a^{[ 1]}_{1}}{\partial w^{[ 1]}_{1}}\\ \dfrac{\partial a^{[ 1]}_{2}}{\partial w^{[ 1]}_{2}}\\ \vdots \\ \dfrac{\partial a^{[ 1]}_{m}}{\partial w^{[ 1]}_{m}} \end{smallmatrix}\right]$

$=\left[\begin{smallmatrix} \dfrac{\partial a^{[ 1]}_{1}}{\partial w^{[ 1]}_{1( 1)}} & \dfrac{\partial a^{[ 1]}_{1}}{\partial w^{[ 1]}_{1( 2)}} & \dots & \dfrac{\partial a^{[ 1]}_{1}}{\partial w^{[ 1]}_{1( 2)}}\\ \dfrac{\partial a^{[ 1]}_{2}}{\partial w^{[ 1]}_{2( 1)}} & \dfrac{\partial a^{[ 1]}_{2}}{\partial w^{[ 1]}_{2( 2)}} & \dots & \dfrac{\partial a^{[ 1]}_{2}}{\partial w^{[ 1]}_{2( n)}}\\ \vdots & \vdots & \dots & \vdots \\ \dfrac{\partial a^{[ 1]}_{m}}{\partial w^{[ 1]}_{m( 1)}} & \dfrac{\partial a^{[ 1]}_{m}}{\partial w^{[ 1]}_{m( 2)}} & \dots & \dfrac{\partial a^{[ 1]}_{m}}{\partial w^{[ 1]}_{m( n)}} \end{smallmatrix}\right] =\left[\begin{smallmatrix} a^{[ 1]}_{1}\left( 1-a^{[ 1]}_{1}\right) \ x_{1} & a^{[ 1]}_{1}\left( 1-a^{[ 1]}_{1}\right) x_{2} & \dots & a^{[ 1]}_{1}\left( 1-a^{[ 1]}_{1}\right) x_{n}\\ a^{[ 1]}_{2}\left( 1-a^{[ 1]}_{2}\right) x_{1} & a^{[ 1]}_{2}\left( 1-a^{[ 1]}_{2}\right) x_{2} & \dots & a^{[ 1]}_{2}\left( 1-a^{[ 1]}_{2}\right) x_{n}\\ \vdots & \vdots & \dots & \vdots \\ a^{[ 1]}_{m}\left( 1-a^{[ 1]}_{m}\right) x_{1} & a^{[ 1]}_{m}\left( 1-a^{[ 1]}_{m}\right) x_{2} & \dots & a^{[ 1]}_{m}\left( 1-a^{[ 1]}_{m}\right) x_{n} \end{smallmatrix}\right]$

Step 2: Moving to the second layer. All the derivatives from first layer get passed on to the second layer, and using them we calculate:

$\dfrac{\partial a^{[ 2]}}{\partial w^{[ 1]}} =\dfrac{\partial a^{[ 2]}}{\partial z^{[ 2]}} \cdot \dfrac{\partial z^{[ 2]}}{\partial a^{[ 1]}} \cdot \dfrac{\partial a^{[ 1]}}{\partial w^{[ 1]}} =a^{[ 2]}\left( 1-a^{[ 2]}\right) \cdot w^{[ 2]} .\dfrac{\partial a^{[ 1]}}{\partial w^{[ 1]}}$

$\dfrac{\partial L}{\partial w^{[ 1]}} =\dfrac{\partial L}{\partial a^{[ 2]}} \cdot \dfrac{\partial a^{[ 2]}}{\partial w^{[ 1]}}$

Image credits: https://www.mathcha.io

  • 2
    $\begingroup$ It would be helpful to explain your notations. Doubtfully people will watch that course just to answer your question. $\endgroup$
    – Eugene
    Commented Dec 22, 2017 at 16:33

1 Answer 1


You described the simplest case of the neural network, where the center neuron has only one output $a$, which is connected to the final loss function. In general, there can be several outputs and the total error signal coming to this node equals the sum over all output connections. Plus the error messages across these connections are different. And the subsequent nodes can have multiple outputs as well -- in general, it's impossible to tell what the result sum will be until the forward pass reaches the loss function.

Here's a picture for two or more output connections:



... or to make it even more complicated...


That's why you can't just multiply $\frac{da}{dz} \cdot \frac{dz}{dw}$, because the total error signal $\delta$ may not be $\frac{da}{dz}$ or something multiplied by $\frac{da}{dz}$. What is known for sure is the local gradient $\frac{dz}{dw}$. But in order to get $\delta$, all of the later nodes must be processed in the reverse order.

Credit: the pictures are from CS224n class by Stanford.

  • $\begingroup$ Added a little more detail. Could you pls explain that too? $\endgroup$ Commented Jan 7, 2018 at 4:16
  • $\begingroup$ Take a look at the residual block ( described here - blog.waya.ai/9610bb62c355 ). At the forward pass, you want to multiply the local gradient at the input $x$ by some other gradient. Which one will it be, $F'(x)$ or $1$? Try to generalize your approach to the case with multiple branches at each node $\endgroup$
    – Maxim
    Commented Jan 7, 2018 at 11:04
  • $\begingroup$ Thanks Maxim :) However, I'm still confused :(.. could you pls see my second edit and point out why we cannot proceed that way? There, I have used vector algebra to calculate the various derivatives, and it seems that it would work out well even for deeper nets. The only downside I see is that each layer has to ask all the derivatives calculated till now by the previous layers. That may be a memory intensive way and may not be efficient. Thoughts? $\endgroup$ Commented Jan 8, 2018 at 6:22
  • $\begingroup$ Sorry for the delay. Your second attempt looks more promising, but I didn't notice at which moment you compute the gradient sum. So, essentially, in order to make it work you have to maintain all paths from the node to the terminal node and sums them up in the end. And all this for all nodes. If you think about it, it's more complicated yet less efficient than going backward one time. Besides, when you compute the sums for all past nodes, it's... the same backprop, because you need to revisit each node once again. $\endgroup$
    – Maxim
    Commented Jan 9, 2018 at 20:04

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