Caffe Tutorial, how to implement a model to classify MNIST handwritten digits

1. Caffe Hands on tutorial – build, train & predict
MUHANAD SHAB KALEIA
Caffe
cooking recipe

2. Caffe for Deep Learning
Caffe
Train new model Pre-trained model
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3. Caffe for Deep Learning
Installation

Caffe can be installed on both CPU only machine, or GPU
machine,

To install it step by step you can follow:
http://mohanadkaleia.com/install-caffe-on-ubuntu-with-no-gpu/

4. Caffe – Build Prediction Model
I. Design a network
II.Train the network
III.Predict new samples
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5. Caffe – Build the Model
Build a network

Protobuff format: a data structure format that can be used by
Caffe to generate the code of the network

Python layers

C++ layers

6. Caffe – Build the Model
Data types supported by Caffe

Image Data – read raw images

Database – read data from LMDB*

HDF5

Memory Data
https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database 6

7. Caffe – Build the Model
Layers supported by Caffe

Inner Product

Convolution

Normalization Layers: LRN, MVNm Batch Normalization

Activation: ReLU, PreLU, Sigmoid, TanH, power, Exp, ...

Recurrent, LSTM

Loss layers: Softmax, Sum-of-Squares, Hinge, accuracy, ..

Utility layers: Split, Concat, Slice, Reshape, ...

8. Caffe – Build the Model
MNIST

9. Caffe – Build the Model
Protobuff – Convolutional layer
This slide borrowed from Kate Saenko UMASS Lowell

10. Caffe – Build the Model
Protobuff – Data layer
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11. Caffe – Build the Model
2. Protobuff – Pooling layer
Layer definition
Layer parameters

12. Caffe – Build the Model
2. Protobuff – Fully Connected layer
Layer definition
Layer parameters
Learning rate
controlling
parameters

13. Caffe – Build the Model
2. Protobuff – ReLU Layer

14. Caffe – Build the Model
2. Protobuff – LOSS Layer
Takes labeled data
The output of
the last layer
Find the loss
for the back-prop
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15. Caffe – Build the Model
2. Protobuff – graphical generator
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●
The protobuff text can be converted into a graphical
form that represent the network using netscope:
http://ethereon.github.io/netscope/quickstart.html
●
If you are lazy enough try this one:
http://yanglei.me/gen_proto/

16. Caffe – Build the Model
Layers supported by Caffe
I. Design a network
II.Train the network
III.Predict new samples
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17. Caffe – Train (Optimize) the net
Caffe Solver

AdaDelta

SGD

AdaGrad

Adam

RMSprop

Nesterov

18. Caffe – Train (Optimize) the net
Caffe Solver – configuration file

19. Caffe – Train (Optimize) the net
Caffe Solver – run the solver
caffe train -solver lenet_solver.prototxt
lenet_10000.caffemodel
lenet_10000.solverstate
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20. Caffe – Train (Optimize) the net
Caffe Solver – run the solver
caffe train -solver lenet_solver.prototxt

21. Caffe – Build the Model
Layers supported by Caffe
I. Design a network
II.Train the network
III.Predict new samples
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22. Caffe – Predict / Classify samples
Caffe Classifier
Caffe Classifier
Trained
model
Deploy.prototxt
Image
sample
Class
(output)
Parameters

23. Caffe – Predict / Classify samples
Caffe Classifier
img = cv2.imread(IMAGE_FILE,0)

Define the Classifier object:
net = caffe.Classifier(MODEL_FILE, PRETRAINED_MODEL, caffe.TEST)

Apply forward pass on the network:
res = net.forward(data = img)
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24. Caffe – Predict / Classify samples
Caffe Classifier
By applying an image of number 9 as an example, the classifier was able
to classify it correctly as we can see from the image below:

25. Caffe – Hands on Tuturial
Step by step tutorial
The full details about this tutorial is available on my website:
http://mohanadkaleia.com/

26. Resources
[1] https://shadowthink.com/blog/tech/2016/08/28/Caffe-MNIST-tutorial
[2] http://caffe.berkeleyvision.org/gathered/examples/mnist.html
[3] http://shengshuyang.github.io/A-step-by-step-guide-to-Caffe.html
[4] https://software.intel.com/en-us/articles/training-and-deploying-deep-
learning-networks-with-caffe-optimized-for-intel-architecture#Training
[5] https://github.com/BVLC/caffe/wiki/Solver-Prototxt
[6] http://adilmoujahid.com/posts/2016/06/introduction-deep-learning-
python-caffe/
[7] http://stackoverflow.com/questions/33765029/caffe-how-to-predict-
from-a-pretrained-net

27. Resources
[8] https://github.com/9crk/caffe-mnist-test
[9] https://github.com/BVLC/caffe/wiki/Using-a-Trained-Network:-Deploy
[10] http://mohanadkaleia.com/install-caffe-on-ubuntu-with-no-gpu/
[11]
http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/01-
learning-lenet.ipynb
[12]https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G
71UMscNPlvArsWER41PsU/edit#slide=id.gc2fcdcce7_216_278