RBM with DL4Jfor Deep Learning
ujava.org 10th Deep Learning Workshop
2015-07-25
www.idosi.com
CEO 강신동
Shindong KANG
(주)지능도시
www.idosi.comujava.org
www.idosi.com spaceapi.org
www.idosi.com DL4J (Deeplearning4j, Deep Learning for Java)
www.idosi.com Hopfield Network
www.idosi.com Boltzmann Machine
www.idosi.com RBM (Restricted Boltzmann Machine)
www.idosi.com Gibbs Sampling and Contrastive Divergence
www.idosi.com Iris
Iris setosa Iris virginicaIris versicolor
Sepal length (꽃받침)Sepal width
Petal length (꽃잎) Petal width
www.idosi.com Iris Data set
www.idosi.com RBM code of DL4J
www.idosi.com RBM execution with CUDA
www.idosi.com layer of DL4J (real neuron layer)
www.idosi.com NeuralNetConfiguration
www.idosi.com NeuralNetConfiguration.Builder
www.idosi.com Gaussian function
www.idosi.com VisibleUnit.GAUSSIAN
www.idosi.com y = 1 + e^x
www.idosi.com y = log (x)
www.idosi.com ReLU (Rectified Linear Unit)
www.idosi.com Softplus function
www.idosi.com Activation Functions Graph
www.idosi.com Noisy ReLU & Leaky ReLU
www.idosi.com HiddenUnit.RECTIFIED
www.idosi.com iteration
www.idosi.com iteration
www.idosi.com weightInit
www.idosi.com Enum WeightInit
www.idosi.com UniformDistribution
www.idosi.com MSE (Mean Squared Error)
www.idosi.com RMSE (Root Mean Squared Error)
www.idosi.com Entropy of Thermodynamics
1862 Clausius S = Q/T
1865 Clausius dS = dQ / T
www.idosi.com Microstates for Boltzmann's Entropy
www.idosi.com Boltzmann's Entropy Equation
www.idosi.com The change in entropy
www.idosi.com Calculation Entropy for Process
www.idosi.com Logarithm for very big number and very small number
www.idosi.com logarithm of chemistry pH
www.idosi.com Loss Function
www.idosi.com Loss Function
www.idosi.com MSE's defect
www.idosi.com Cross Entropy Error
www.idosi.com Regularization
www.idosi.com L2 coefficient for regularization
L2 is used only when regularization(true)
L2 is for how much the regularization should count.
www.idosi.com Hessian Matrix for BFGS
www.idosi.com BFGS
www.idosi.com Limited-memory BFGS
www.idosi.com Momentum (운동량)
www.idosi.com Momentum in physics
www.idosi.com Momentum in Deep Learning
www.idosi.com Momentum Learning Rule
www.idosi.com interface Model
www.idosi.com interface Layer
www.idosi.com Layer.Type
www.idosi.com interface hierarchy
www.idosi.com LayerFactories
www.idosi.com ScoreIterationListener
www.idosi.com interface Model.fit()
www.idosi.com RBM : Early epochs of training
www.idosi.com RBM : Late epochs of training
7학년일반
www.idosi.com Target Value & Test Value
Target
Test
T
T
F
F
TT TF
FT FF
Not for RBM(RBM is unsupervised learning)
www.idosi.com Precision (정확률)
Target
Test
T
T
F
F
TT TF
FT FF
TT
TT + FT
www.idosi.com Recall (재현률)
Target
Test
T
T
F
F
TT TF
FT FF
TT
TT + TF
www.idosi.com Accuracy (정확도)
Target
Test
T
T
F
F
TT TF
FT FF
TT + FF
TT + TF +FT + FF
www.idosi.com Mean of Velocity
4 km/h
6 km/h
www.idosi.com Mean of Velocity
4 km/h
6 km/h
V (km/h) = 2L km
L km
4 km/h+
L km
6 km/h
= 2
1
4 +
1
6
= 4.8 km/h
www.idosi.com Harmonic Mean
www.idosi.com F1 score
2 x Precision x Recall
Precision + Recall
P
1
R
1+
2
1
Thank you !
(주)지능도시
Intelligent City Ltd.
강신동
Shindong KANG
[email protected]
