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Theano tutorial
part 2
AACIMP 2015
Sergii Gavrylov
Overview
● Brief recap
● Multivariate logistic regression
● Multilayer perceptron
● Convolution
● Convolutional neural network
● scan
● Recurrent neural network
Brief recap
● Symbolic variables
● Functions
● Shared variables / updates
● Gradients
● Substitution
Computational graph
X
+
Z
Y
● Code generation
● Symbolic differentiation
Multivariate logistic
regression
0
1
2
3
softmax(x * W)
Multilayer perceptron
0
1
2
3
relu(x * W) softmax(x * W)
1D Convolution
1D Convolution
1D Convolution
1D Convolution
2D Convolution
filter =
Max pooling
cs231n.github.io/convolutional-networks
ConvPoolLayer
Convolutional NN
cs231n.github.io/convolutional-networks
deeplearning.net/tutorial/lenet.html
Convolutional NN
deeplearning.net/tutorial/lenet.html
scan
(Symbolic loop in theano)
Recurrent neural network
www.iro.umontreal.ca/~bengioy/dlbook/rnn.html
“Vanilla” RNN
www.iro.umontreal.ca/~bengioy/dlbook/rnn.html
Conclusion
● Theano has a lot of useful building blocks (convolution, scan).
● Theano supports both cpu and gpu backends.

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