https://telecombcn-dl.github.io/2017-dlsl/ Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN. The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.

1. [course site]
Day 1 Lecture 5
Backpropagation in
Deep Networks
Elisa Sayrol

2. From previous lectures
L Hidden Layers
Hidden pre-activation (k>0)
Hidden activation (k=1,…L)
Output activation (k=L+1)
Figure Credit: Hugo Laroche NN course

3. Backpropagation algorithm
The output of the Network gives class scores that depens on the input
and the parameters
• Define a loss function that quantifies our unhappiness with the
scores across the training data.
• Come up with a way of efficiently finding the parameters that
minimize the loss function (optimization)

4. Probability Class given an input
(softmax)
h2
h3
a3
a4 h4
Loss
Hidden Hidden Output
W2
W3
x a2
Input
W1
Figure Credit: Kevin McGuiness
Forward Pass

5. Probability Class given an input
(softmax)
Loss function; e.g., negative
log-likelihood (good for classification)
h2
h3
a3
a4 h4
Loss
Hidden Hidden Output
W2
W3
x a2
Input
W1
Regularization term (L2 Norm)
aka as weight decay
Figure Credit: Kevin McGuiness
Forward Pass

6. Probability Class given an input
(softmax)
Minimize the loss (plus some
regularization term) w.r.t. Parameters
over the whole training set.
Loss function; e.g., negative
log-likelihood (good for classification)
h2
h3
a3
a4 h4
Loss
Hidden Hidden Output
W2
W3
x a2
Input
W1
Regularization term (L2 Norm)
aka as weight decay
Figure Credit: Kevin McGuiness
Forward Pass

7. Backpropagation algorithm
• We need a way to fit the model to data: find parameters (W(k)
, b(k)
) of the network that (locally)
minimize the loss function.
• We can use stochastic gradient descent. Or better yet, mini-batch stochastic gradient descent.
• To do this, we need to find the gradient of the loss function with respect to all the parameters of
the model (W(k)
, b(k)
)
• These can be found using the chain rule of differentiation.
• The calculations reveal that the gradient wrt. the parameters in layer k only depends on the error
from the above layer and the output from the layer below.
• This means that the gradients for each layer can be computed iteratively, starting at the last layer
and propagating the error back through the network. This is known as the backpropagation
algorithm.
Slide Credit: Kevin McGuiness

8. 1. Find the error in the top layer:
h2
h3
a3
a4 h4
Loss
Hidden Hidden Output
W2
W3
x a2
Input
W1
L
Figure Credit: Kevin McGuiness
Backward Pass

9. 1. Find the error in the top layer: 2. Compute weight updates
h2
h3
a3
a4 h4
Loss
Hidden Hidden Output
W2
W3
x a2
Input
W1
L
Figure Credit: Kevin McGuiness
Backward Pass
To simplify we don’t consider the biass

10. 1. Find the error in the top layer: 3. Backpropagate error to layer below2. Compute weight updates
h2
h3
a3
a4 h4
Loss
Hidden Hidden Output
W2
W3
x a2
Input
W1
L
Figure Credit: Kevin McGuiness
Backward Pass
To simplify we don’t consider the biass

11. Optimization
Stochastic Gradient Descent
Stochastic Gradient Descent with momentum
Stochastic Gradient Descent with L2 regularization
Backpropagation: http://cs231n.github.io/optimization-2/
: learning rate
: weight decay
Recommended lectures:
Optimization:
http://sebastianruder.com/optimizing-gradient-descent/
Sebastian Ruder Blog

12. In the backward pass you might be in the flat part of the sigmoid (or any other activation
function like tanh) so derivative tends to zero and your training loss will not go down
“Vanishing Gradients”