├── README.md
├── Rcode
├── Coupled VAE R Code.R
├── Test
├── ml005_alpha2
├── ml005_alpha2_d2
├── ml01_alpha2
├── ml025_alpha2
├── ml025_alpha2_d2
├── ml075_alpha2_d2
├── ml0_alpha2
├── ml0_alpha2_d2
├── sigma005_alpha2
├── sigma005_alpha2_d2
├── sigma01_alpha2
├── sigma025_alpha2
├── sigma025_alpha2_d2
├── sigma075_alpha2_d2
├── sigma0_alpha2
└── sigma0_alpha2_d2
├── Results
├── cluster plot_dimz=2_kappa=0.025.png
├── cluster plot_dimz=2_kappa=0.05.png
├── cluster plot_dimz=2_kappa=0.075.png
├── cluster plot_dimz=2_kappa=0.png
├── input images.png
├── input_ images.png
├── likelihood histogram_dim=2_kappa=0.025.png
├── likelihood histogram_dim=2_kappa=0.05.png
├── likelihood histogram_dim=2_kappa=0.075.png
├── likelihood histogram_dim=2_kappa=0.png
├── likelihood histogram_kappa=0.025.png
├── likelihood histogram_kappa=0.05.png
├── likelihood histogram_kappa=0.1.png
├── likelihood histogram_kappa=0.png
├── output images-kappa=0.png
├── output images_kappa=0.025.png
├── output images_kappa=0.05.png
├── output images_kappa=0.1.png
├── rose plot_dimz=2_kappa=0.025.png
├── rose plot_dimz=2_kappa=0.05.png
├── rose plot_dimz=2_kappa=0.075.png
├── rose plot_dimz=2_kappa=0.png
├── rose plot_kappa=0.025.png
├── rose plot_kappa=0.05.png
├── rose plot_kappa=0.1.png
├── rose plot_kappa=0.png
├── sigma near the metrics_kappa=0.025(magnified).png
├── sigma near the metrics_kappa=0.025.png
├── sigma near the metrics_kappa=0.1(magnified).png
├── sigma near the metrics_kappa=0.1.png
├── sigma near the metrics_kappa=0.5(magnified).png
├── sigma near the metrics_kappa=0.5.png
├── sigma near the metrics_kappa=0.png
├── visualization plot_dimz=2_kappa=0.025.png
├── visualization plot_dimz=2_kappa=0.05.png
├── visualization plot_dimz=2_kappa=0.075.png
└── visualization plot_dimz=2_kappa=0.png
└── VAE
├── Test.py
├── mnist_data.py
├── run_main.py
└── vae.py
/README.md:
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1 | # Coupled-VAE-Improved-Robustness-and-Accuracy-of-a-Variational-Autoencoder
2 | We present a coupled Variational Auto-Encoder (VAE) method that improves the accuracy and robustness of the probabilistic inferences on represented data. The new method models the dependency between input feature vectors (images) and weighs the outliers with a higher penalty by generalizing the original loss function to the coupled entropy function, using the principles of nonlinear statistical coupling. We evaluate the performance of the coupled VAE model using the MNIST dataset. Compared with the traditional VAE algorithm, the output images generated by the coupled VAE method are clearer and less blurry. The visualization of the input images embedded in 2D latent variable space provides a deeper insight into the structure of new model with coupled loss function: the latent variable has a smaller deviation and the output values are generated by a more compact latent space. We analyze the histograms of probabilities for the input images using the generalized mean metrics, in which increased geometric mean illustrates that the average likelihood of input data is improved. Increases in the -2/3 mean, which is sensitive to outliers, indicates improved robustness. The decisiveness, measured by the arithmetic mean of the likelihoods, is unchanged and -2/3 mean shows that the new model has better robustness.
3 |
4 | ## Results
5 |
6 |
7 | ### Output images with different k values
8 |
9 |
10 | | Input image |
11 | k = 0 |
12 | k = 0.025 |
13 | k = 0.05 |
14 | k = 0.075 |
15 |
16 |
17 | 
18 | | 
19 | | 
20 | | 
21 | | 
22 | |
23 |
24 |
25 | ### The histograms of likelihood for the input images with various k values
26 |
27 |
28 | | k = 0 |
29 | k = 0.025 |
30 |
31 |
32 | 
33 | | 
34 | |
35 |
36 |
37 |
38 |
39 | | k = 0.05 |
40 | k = 0.1 |
41 |
42 |
43 | 
44 | | 
45 | |
46 |
47 |
48 | ### The relationship between coupling k with the probabilities for input data
49 | | Coupling k | Arithmetic mean metric | Geometric mean metric | -2/3 mean metric |
50 | | :--------: | :-------------------------: | :-------------------: | :------------------: |
51 | | 0 | 1.31 × 10-15 | 2.4110 × -39 | 1.4010 × -79 |
52 | | 0.025 | 6.6111 × -15 | 5.9810 × -35 | 9.9110 × -81 |
53 | | 0.05 | 7.1810 × -12 | 5.8010 × -32 | 1.3110 × -73 |
54 | | 0.1 | 1.3410 × -12 | 7.0910 × -29 | 2.5710 × -71 |
55 |
56 | ### The standard deviation of latent variable samples near the three generalized mean metrics
57 |
58 |
59 | | k = 0 |
60 | k = 0.025 |
61 |
62 |
63 | 
64 | | 
65 | |
66 |
67 |
68 |
69 |
70 | | k = 0.05 |
71 | k = 0.1 |
72 |
73 |
74 | 
75 | | 
76 | |
77 |
78 |
79 | ### The standard deviation of latent variable samples near the three generalized mean metrics (Magnified)
80 |
81 |
82 | | k = 0.025 |
83 | k = 0.05 |
84 | k = 0.1 |
85 |
86 |
87 | .png)
88 | | .png)
89 | | .png)
90 | |
91 |
92 |
93 | ### The rose plots of the various standard deviation values in 20 dimensions. The range of standard deviation reduces as coupling k increasing
94 |
95 |
96 | | k = 0 |
97 | k = 0.025 |
98 | k = 0.05 |
99 | k = 0.1 |
100 |
101 |
102 | 
103 | | 
104 | | 
105 | | 
106 | |
107 |
108 |
109 | ### The histogram likelihood plots with a two-dimensional latent variable
110 |
111 |
112 | | k = 0 |
113 | k = 0.025 |
114 |
115 |
116 | 
117 | | 
118 | |
119 |
120 |
121 |
122 |
123 | | k = 0.05 |
124 | k = 0.075 |
125 |
126 |
127 | 
128 | | 
129 | |
130 |
131 |
132 | ### The rose plots of the various standard deviation values in 2 dimensions. The range of standard deviation reduces as coupling k increasing
133 |
134 |
135 | | k = 0 |
136 | k = 0.025 |
137 | k = 0.05 |
138 | k = 0.075 |
139 |
140 |
141 | 
142 | | 
143 | | 
144 | | 
145 | |
146 |
147 |
148 | ### The plot of the latent space of VAE trained for 200 epochs on MNIST with various k values
149 |
150 |
151 | | k = 0 |
152 | k = 0.025 |
153 | k = 0.05 |
154 | k = 0.075 |
155 |
156 |
157 | 
158 | | 
159 | | 
160 | | 
161 | |
162 |
163 |
164 | ### The plot of visualization of learned data manifold for generative models with the axes to be the values of each dimension of latent variables
165 |
166 |
167 | | k = 0 |
168 | k = 0.025 |
169 | k = 0.05 |
170 | k = 0.075 |
171 |
172 |
173 | 
174 | | 
175 | | 
176 | | 
177 | |
178 |
179 |
180 | ## References
181 |
182 | [1] D. P. Kingma and M. Welling, “Auto-Encoding Variational Bayes,” in International Conference on Learning Representations (ICLR), 2014, p. Arxiv: 1312.6114v10.pd.
183 | [2] D. Tran, M. D. Hoffman, R. A. Saurous, E. Brevdo, K. Murphy, and D. M. Blei, “Deep probabilistic programming,” pp. 1–18, 2017.,
184 | [3] S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio, “Generating Sentences from a Continuous Space,” Nov. 2015.
185 | [4] J. Zalger, “Application of variational autoencoders for aircraft turbomachinery design.”
186 | [5] H. Xu et al., “Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications,” in Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW ’18, 2018, pp. 187–196.
187 | [6] K. P. Nelson and S. Umarov, “Nonlinear statistical coupling,” Phys. A Stat. Mech. its Appl., vol. 389, no. 11, pp. 2157–2163, Jun. 2010.
188 | [7] K. P. Nelson, S. R. Umarov, and M. A. Kon, “On the average uncertainty for systems with nonlinear coupling,” Phys. A Stat. Mech. its Appl., vol. 468, pp. 30–43, Feb. 2017.
189 | [8] K. P. Nelson, “Reduced Perplexity: A simplified perspective on assessing probabilistic forecasts,” Mar. 2016.
190 | [9] C. Tsallis, Introduction to nonextensive statistical mechanics: approaching a complex world. 2009.
191 | [10] O. Niemitalo, “A method for training artificial neural networks to generate missing data within a variable context,” 2010.
192 | [11] T. Huang, Z. Zeng, C. Li, and C. Leung, “Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings,” 2012.
193 | [12] A. Rajaraman and J. D. Ullman, “Mining of massive datasets,” in Mining of Massive Datasets, 2011, vol. 9781107015, pp. 1–315.
194 | [13] J. Donahue, P. Krähenbühl, and T. Darrell, “Adversarial Feature Learning,” May 2016.
195 | [14] V. Dumoulin et al., “Adversarially Learned Inference,” Jun. 2016.
196 | [15] J. Pearl, “Bayesian netwcrks: A model cf self-activated memory for evidential reasoning,” 1985.
197 | [16] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. 2016.
198 | [17] J. Ebbers, J. Heymann, L. Drude, T. Glarner, R. Haeb-Umbach, and B. Raj, “Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery,” 2017.
199 | [18] N. Dilokthanakul et al., “Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders,” Nov. 2016.,
200 | [19] A. Srivastava and C. Sutton, “Autoencoding Variational Inference For Topic Models,” Mar. 2017.
201 | [20] Yanna LeCun, Corinna Cortes, and Christopher J.C. Burges, “MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges.” [Online]. Available: http://yann.lecun.com/exdb/mnist/index.html. [Accessed: 18-Apr-2019].
202 | [21] D. McAlister, “The law of the geometric mean,” Proc. R. Soc. London, vol. 29, no. 196–199, pp. 367–376, 1879.
203 | [22] L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” J. Mach. Learn. Res., vol. 9, no. 2579–2605, p. 85, 2008.
204 |
205 |
206 | ### Code References:
207 |
208 | [1] https://github.com/tensorflow/tensorflow
209 | [2] https://github.com/hwalsuklee/tensorflow-mnist-VAE
210 |
211 | ## Acknowledgements
212 | The results has been tested with Tensorflow r1.13.1 -Gpu-version on Windows 10.
213 |
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/Rcode/Coupled VAE R Code.R:
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1 | #Figure 3: The likelihood for the input images under the VAE model
2 | ml.coupled2 <- scan(file = "~/Desktop/Project/ml0_alpha2")
3 | mean(ml.coupled2)
4 | expml.coupled2 <- exp(ml.coupled2)
5 | max(expml.coupled2)
6 | min(expml.coupled2)
7 | avg2 <- mean(expml.coupled2)
8 | logavg2 <- log10(avg2)
9 | geo2 <- exp(mean(log(expml.coupled2)))
10 | loggeo2 <- log10(geo2)
11 | rob2 <- (sum(expml.coupled2^(-2/3))/5000)^(-3/2)
12 | logrob2 <- log10(rob2)
13 | b <- seq(-100,1,by = 1)
14 | logscale<-log10(expml.coupled2)
15 | h <- hist(logscale, breaks = b)
16 | new_counts <- log10(h$counts)+1
17 | new_counts[which(new_counts == -Inf)] <- 0
18 | h$counts <- new_counts
19 | plot(h, main = "", ylab = "Frequency in logscale ", ylim = c(0, 4), xlab = "Likelihood in logscale", xaxt="n", yaxt = "n",cex.lab=1.55)
20 | #axis(1,at = c(-180, -160, -140, -120, -100, -80, -60, -40, -20, 0), labels = c(10^(-180), 10^(-160), 10^(-140), 10^(-120), 10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.5)
21 | axis(1,at = c(-100, -80, -60, -40, -20, 0), labels = c(10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.55)
22 | axis(2,at = c(0, 1, 2, 3,4), labels = c(0.1, 10, 100, 1000,10000),cex.axis = 1.55)
23 | abline(v=logavg2,col="red", lwd = 2)
24 | abline(v=loggeo2,col="blue", lwd = 2)
25 | abline(v=logrob2,col="green", lwd = 2)
26 | legend(-65, 4.5, "Accuracy", text.col = "blue",bty = "n", cex = 1.5)
27 | legend(-33, 4.5, "Decisiveness", text.col = "red", bty = "n", cex = 1.5)
28 | legend(-99, 4.5, "Robustness", text.col = "green", bty = "n", cex = 1.5)
29 | legend(-112,4.8,expression(paste(kappa," = 0")), bty = "n", cex = 1.5)
30 |
31 | #Figure 5: The histograms of likelihood for the input images with various kappa values.
32 | #(b) kappa = 0.025
33 | ml.coupled2 <- scan(file = "~/Desktop/Project/ml025_alpha2")
34 | mean(ml.coupled2)
35 | expml.coupled2 <- exp(ml.coupled2)
36 | max(expml.coupled2)
37 | min(expml.coupled2)
38 | avg2 <- mean(expml.coupled2)
39 | logavg2 <- log10(avg2)
40 | geo2 <- exp(mean(log(expml.coupled2)))
41 | loggeo2 <- log10(geo2)
42 | rob2 <- (sum(expml.coupled2^(-2/3))/5000)^(-3/2)
43 | logrob2 <- log10(rob2)
44 | b <- seq(-100,1,by = 1)
45 | logscale<-log10(expml.coupled2)
46 | h <- hist(logscale, breaks = b)
47 | new_counts <- log10(h$counts)+1
48 | new_counts[which(new_counts == -Inf)] <- 0
49 | h$counts <- new_counts
50 | plot(h, main = "", ylab = "Frequency in logscale ", ylim = c(0, 4), xlab = "Likelihood in logscale", xaxt="n", yaxt = "n",cex.lab=1.55)
51 | #axis(1,at = c(-180, -160, -140, -120, -100, -80, -60, -40, -20, 0), labels = c(10^(-180), 10^(-160), 10^(-140), 10^(-120), 10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.5)
52 | axis(1,at = c(-100, -80, -60, -40, -20, 0), labels = c(10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.55)
53 | axis(2,at = c(0, 1, 2, 3,4), labels = c(0.1, 10, 100, 1000,10000),cex.axis = 1.55)
54 | abline(v=logavg2,col="red", lwd = 2)
55 | abline(v=loggeo2,col="blue", lwd = 2)
56 | abline(v=logrob2,col="green", lwd = 2)
57 | legend(-55, 4.5, "Accuracy", text.col = "blue",bty = "n", cex = 1.5)
58 | legend(-33, 4.5, "Decisiveness", text.col = "red", bty = "n", cex = 1.5)
59 | legend(-99, 4.5, "Robustness", text.col = "green", bty = "n", cex = 1.5)
60 | legend(-112,4.8,expression(paste(kappa," = 0.025")), bty = "n", cex = 1.5)
61 |
62 | #(c) kappa = 0.05
63 | ml.coupled2 <- scan(file = "~/Desktop/Project/ml005_alpha2")
64 | mean(ml.coupled2)
65 | expml.coupled2 <- exp(ml.coupled2)
66 | max(expml.coupled2)
67 | min(expml.coupled2)
68 | avg2 <- mean(expml.coupled2)
69 | logavg2 <- log10(avg2)
70 | geo2 <- exp(mean(log(expml.coupled2)))
71 | loggeo2 <- log10(geo2)
72 | rob2 <- (sum(expml.coupled2^(-2/3))/5000)^(-3/2)
73 | logrob2 <- log10(rob2)
74 | b <- seq(-100,1,by = 1)
75 | logscale<-log10(expml.coupled2)
76 | h <- hist(logscale, breaks = b)
77 | new_counts <- log10(h$counts)+1
78 | new_counts[which(new_counts == -Inf)] <- 0
79 | h$counts <- new_counts
80 | plot(h, main = "", ylab = "Frequency in logscale ", ylim = c(0, 4), xlab = "Likelihood in logscale", xaxt="n", yaxt = "n",cex.lab=1.55)
81 | #axis(1,at = c(-180, -160, -140, -120, -100, -80, -60, -40, -20, 0), labels = c(10^(-180), 10^(-160), 10^(-140), 10^(-120), 10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.5)
82 | axis(1,at = c(-100, -80, -60, -40, -20, 0), labels = c(10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.55)
83 | axis(2,at = c(0, 1, 2, 3,4), labels = c(0.1, 10, 100, 1000,10000),cex.axis = 1.55)
84 | abline(v=logavg2,col="red", lwd = 2)
85 | abline(v=loggeo2,col="blue", lwd = 2)
86 | abline(v=logrob2,col="green", lwd = 2)
87 | legend(-55, 4.5, "Accuracy", text.col = "blue",bty = "n", cex = 1.5)
88 | legend(-33, 4.5, "Decisiveness", text.col = "red", bty = "n", cex = 1.5)
89 | legend(-99, 4.5, "Robustness", text.col = "green", bty = "n", cex = 1.5)
90 | legend(-112,4.8,expression(paste(kappa," = 0.05")), bty = "n", cex = 1.5)
91 |
92 | #(d) kappa = 0.1
93 | ml.coupled2 <- scan(file = "~/Desktop/Project/ml01_alpha2")
94 | mean(ml.coupled2)
95 | expml.coupled2 <- exp(ml.coupled2)
96 | max(expml.coupled2)
97 | min(expml.coupled2)
98 | avg2 <- mean(expml.coupled2)
99 | logavg2 <- log10(avg2)
100 | geo2 <- exp(mean(log(expml.coupled2)))
101 | loggeo2 <- log10(geo2)
102 | rob2 <- (sum(expml.coupled2^(-2/3))/5000)^(-3/2)
103 | logrob2 <- log10(rob2)
104 | b <- seq(-100,1,by = 1)
105 | logscale<-log10(expml.coupled2)
106 | h <- hist(logscale, breaks = b)
107 | new_counts <- log10(h$counts)+1
108 | new_counts[which(new_counts == -Inf)] <- 0
109 | h$counts <- new_counts
110 | plot(h, main = "", ylab = "Frequency in logscale ", ylim = c(0, 4), xlab = "Likelihood in logscale", xaxt="n", yaxt = "n",cex.lab=1.55)
111 | #axis(1,at = c(-180, -160, -140, -120, -100, -80, -60, -40, -20, 0), labels = c(10^(-180), 10^(-160), 10^(-140), 10^(-120), 10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.5)
112 | axis(1,at = c(-100, -80, -60, -40, -20, 0), labels = c(10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.55)
113 | axis(2,at = c(0, 1, 2, 3,4), labels = c(0.1, 10, 100, 1000,10000),cex.axis = 1.55)
114 | abline(v=logavg2,col="red", lwd = 2)
115 | abline(v=loggeo2,col="blue", lwd = 2)
116 | abline(v=logrob2,col="green", lwd = 2)
117 | legend(-55, 4.5, "Accuracy", text.col = "blue",bty = "n", cex = 1.5)
118 | legend(-33, 4.5, "Decisiveness", text.col = "red", bty = "n", cex = 1.5)
119 | legend(-99, 4.5, "Robustness", text.col = "green", bty = "n", cex = 1.5)
120 | legend(-112,4.8,expression(paste(kappa," = 0.1")), bty = "n", cex = 1.5)
121 |
122 | #Figure 6: The rose plots of the various standard deviation values in 20 dimensions.
123 | #(a) kappa = 0
124 | sigma00 <- scan(file = "~/Desktop/Project/sigma0_alpha2")
125 | h <- hist(sigma00,breaks = seq(0,1,by = 0.01))
126 | dt <- data.frame(A = h$counts/10000, B=seq(0,0.99,by = 0.01))
127 | p <- ggplot(dt, aes(x=B, y=A, fill=B))+
128 | geom_bar(stat = "identity",color="black",alpha=0.7)+
129 | coord_polar()+
130 | theme_bw()+
131 | labs(x="",y="")+
132 | theme(legend.position = "none")
133 | p
134 |
135 | #(b) kappa = 0.025
136 | sigma00 <- scan(file = "~/Desktop/Project/sigma025_alpha2")
137 | h <- hist(sigma00,breaks = seq(0,1,by = 0.01))
138 | dt <- data.frame(A = h$counts/10000, B=seq(0,0.99,by = 0.01))
139 | p <- ggplot(dt, aes(x=B, y=A, fill=B))+
140 | geom_bar(stat = "identity",color="black",alpha=0.7)+
141 | coord_polar()+
142 | theme_bw()+
143 | labs(x="",y="")+
144 | theme(legend.position = "none")
145 | p
146 |
147 | #(c) kappa = 0.05
148 | sigma00 <- scan(file = "~/Desktop/Project/sigma005_alpha2")
149 | h <- hist(sigma00,breaks = seq(0,1,by = 0.01))
150 | dt <- data.frame(A = h$counts/10000, B=seq(0,0.99,by = 0.01))
151 | p <- ggplot(dt, aes(x=B, y=A, fill=B))+
152 | geom_bar(stat = "identity",color="black",alpha=0.7)+
153 | coord_polar()+
154 | theme_bw()+
155 | labs(x="",y="")+
156 | theme(legend.position = "none")
157 | p
158 |
159 | #(d) kappa = 0.1
160 | sigma00 <- scan(file = "~/Desktop/Project/sigma01_alpha2")
161 | h <- hist(sigma00,breaks = seq(0,1,by = 0.01))
162 | dt <- data.frame(A = h$counts/10000, B=seq(0,0.99,by = 0.01))
163 | p <- ggplot(dt, aes(x=B, y=A, fill=B))+
164 | geom_bar(stat = "identity",color="black",alpha=0.7)+
165 | coord_polar()+
166 | theme_bw()+
167 | labs(x="",y="")+
168 | theme(legend.position = "none")
169 | p
170 |
171 | #Figure 7: The standard deviation of latent variable samples near the three generalized mean metrics.
172 | #(a) kappa = 0
173 | sigma00 <- scan(file = "~/Desktop/Project/sigma0_alpha2")
174 | ml.coupled2 <- scan(file = "~/Desktop/Project/ml0_alpha2")
175 | mean(sigma00)
176 | max(sigma00)
177 | ml_00 <- ml.coupled2
178 | mean(ml_00)
179 | expml00 <- exp(ml_00)
180 | rob00 <- (sum(expml00^(-2/3))/5000)^(-3/2)
181 | logrob00 <- log10(rob00)
182 | dif <- abs(rob00-expml00)
183 | which.min(dif)
184 | sigmarob00 <- sigma00[(4303*20+1):(4304*20)]
185 | avg00 <- mean(expml00)
186 | logavg00 <- log10(avg00)
187 | dif <- abs(avg00-expml00)
188 | which.min(dif)
189 | sigmaavg00 <- sigma00[(2401*20+1):(2402*20)]
190 | geo00 <- exp(mean(log(expml00)))
191 | loggeo00 <- log10(geo00)
192 | dif <- abs(geo00-expml00)
193 | which.min(dif)
194 | sigmageo00 <- sigma00[(2164*20+1):(2165*20)]
195 | plot(sigmaavg00,type = "b",main = expression(paste(kappa," = 0")),ylim = c(0,0.7),
196 | ylab = expression(paste(sigma," of each dimension of z")), xlab = "dimensions of z",col="red", cex.lab = 1.5,cex.axis=1.5)
197 | lines(sigmarob00,col="green",type = "b")
198 | lines(sigmageo00,col="blue",type = "b")
199 |
200 | #(b) kappa = 0.025
201 | sigma00 <- scan(file = "~/Desktop/Project/sigma025_alpha2")
202 | ml.coupled2 <- scan(file = "~/Desktop/Project/ml025_alpha2")
203 | mean(sigma00)
204 | max(sigma00)
205 | ml_00 <- ml.coupled2
206 | mean(ml_00)
207 | expml00 <- exp(ml_00)
208 | rob00 <- (sum(expml00^(-2/3))/5000)^(-3/2)
209 | logrob00 <- log10(rob00)
210 | dif <- abs(rob00-expml00)
211 | which.min(dif)
212 | sigmarob00 <- sigma00[(891*20+1):(892*20)]
213 | avg00 <- mean(expml00)
214 | logavg00 <- log10(avg00)
215 | dif <- abs(avg00-expml00)
216 | which.min(dif)
217 | sigmaavg00 <- sigma00[(3921*20+1):(3922*20)]
218 | geo00 <- exp(mean(log(expml00)))
219 | loggeo00 <- log10(geo00)
220 | dif <- abs(geo00-expml00)
221 | which.min(dif)
222 | sigmageo00 <- sigma00[(4095*20+1):(4096*20)]
223 | plot(sigmaavg00,type = "b",main = expression(paste(kappa," = 0.025")),ylim = c(0,0.7),
224 | ylab = expression(paste(sigma," of each dimension of z")), xlab = "dimensions of z",col="red", cex.lab = 1.5,cex.axis=1.5)
225 | lines(sigmarob00,col="green",type = "b")
226 | lines(sigmageo00,col="blue",type = "b")
227 | # magnified plot
228 | plot(sigmaavg00,type = "b",col="red",ylim =c(0, 0.2),cex.axis=1.5,xlab = "", ylab = "")
229 | lines(sigmarob00,col="green",type = "b")
230 | lines(sigmageo00,col="blue",type = "b")
231 |
232 | #(c) kappa = 0.05
233 | sigma00 <- scan(file = "~/Desktop/Project/sigma005_alpha2")
234 | ml.coupled2 <- scan(file = "~/Desktop/Project/ml005_alpha2")
235 | mean(sigma00)
236 | max(sigma00)
237 | ml_00 <- ml.coupled2
238 | mean(ml_00)
239 | expml00 <- exp(ml_00)
240 | rob00 <- (sum(expml00^(-2/3))/5000)^(-3/2)
241 | logrob00 <- log10(rob00)
242 | dif <- abs(rob00-expml00)
243 | which.min(dif)
244 | sigmarob00 <- sigma00[(895*20+1):(896*20)]
245 | avg00 <- mean(expml00)
246 | logavg00 <- log10(avg00)
247 | dif <- abs(avg00-expml00)
248 | which.min(dif)
249 | sigmaavg00 <- sigma00[(617*20+1):(618*20)]
250 | geo00 <- exp(mean(log(expml00)))
251 | loggeo00 <- log10(geo00)
252 | dif <- abs(geo00-expml00)
253 | which.min(dif)
254 | sigmageo00 <- sigma00[(4202*20+1):(4203*20)]
255 | plot(sigmaavg00,type = "b",main = expression(paste(kappa," = 0.05")),ylim = c(0,0.7),
256 | ylab = expression(paste(sigma," of each dimension of z")), xlab = "dimensions of z",col="red", cex.lab = 1.5,cex.axis=1.5)
257 | lines(sigmarob00,col="green",type = "b")
258 | lines(sigmageo00,col="blue",type = "b")
259 | # magnified plot
260 | plot(sigmaavg00,type = "b",col="red",ylim =c(0, 0.03),cex.axis=1.5,xlab = "", ylab = "")
261 | lines(sigmarob00,col="green",type = "b")
262 | lines(sigmageo00,col="blue",type = "b")
263 |
264 | #(d) kappa = 0.1
265 | sigma00 <- scan(file = "~/Desktop/Project/sigma01_alpha2")
266 | ml.coupled2 <- scan(file = "~/Desktop/Project/ml01_alpha2")
267 | mean(sigma00)
268 | max(sigma00)
269 | ml_00 <- ml.coupled2
270 | mean(ml_00)
271 | expml00 <- exp(ml_00)
272 | rob00 <- (sum(expml00^(-2/3))/5000)^(-3/2)
273 | logrob00 <- log10(rob00)
274 | dif <- abs(rob00-expml00)
275 | which.min(dif)
276 | sigmarob00 <- sigma00[(4501*20+1):(4502*20)]
277 | avg00 <- mean(expml00)
278 | logavg00 <- log10(avg00)
279 | dif <- abs(avg00-expml00)
280 | which.min(dif)
281 | sigmaavg00 <- sigma00[(2733*20+1):(2734*20)]
282 | geo00 <- exp(mean(log(expml00)))
283 | loggeo00 <- log10(geo00)
284 | dif <- abs(geo00-expml00)
285 | which.min(dif)
286 | sigmageo00 <- sigma00[(1216*20+1):(1217*20)]
287 | plot(sigmaavg00,type = "b",main = expression(paste(kappa," = 0.01")),ylim = c(0,0.7),
288 | ylab = expression(paste(sigma," of each dimension of z")), xlab = "dimensions of z",col="red", cex.lab = 1.5,cex.axis=1.5)
289 | lines(sigmarob00,col="green",type = "b")
290 | lines(sigmageo00,col="blue",type = "b")
291 | # magnified plot
292 | plot(sigmaavg00,type = "b",col="red",ylim =c(0, 0.012),cex.axis=1.5,xlab = "", ylab = "")
293 | lines(sigmarob00,col="green",type = "b")
294 | lines(sigmageo00,col="blue",type = "b")
295 |
296 | #Figure 8: The histogram likelihood plots with a two-dimensional latent variable.
297 | #(a) kappa = 0
298 | ml.coupled2 <- scan(file = "~/Desktop/Project/ml0_alpha2_d2")
299 | mean(ml.coupled2)
300 | expml.coupled2 <- exp(ml.coupled2)
301 | max(expml.coupled2)
302 | min(expml.coupled2)
303 | avg2 <- mean(expml.coupled2)
304 | logavg2 <- log10(avg2)
305 | geo2 <- exp(mean(log(expml.coupled2)))
306 | loggeo2 <- log10(geo2)
307 | rob2 <- (sum(expml.coupled2^(-2/3))/5000)^(-3/2)
308 | logrob2 <- log10(rob2)
309 | b <- seq(-180,1,by = 1)
310 | logscale<-log10(expml.coupled2)
311 | h <- hist(logscale, breaks = b)
312 | new_counts <- log10(h$counts)+1
313 | new_counts[which(new_counts == -Inf)] <- 0
314 | h$counts <- new_counts
315 | plot(h, main = "", ylab = "Frequency in logscale ", ylim = c(0, 4), xlab = "Likelihood in logscale", xaxt="n", yaxt = "n",cex.lab=1.55)
316 | axis(1,at = c(-180, -160, -140, -120, -100, -80, -60, -40, -20, 0), labels = c(10^(-180), 10^(-160), 10^(-140), 10^(-120), 10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.5)
317 | #axis(1,at = c(-100, -80, -60, -40, -20, 0), labels = c(10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.55)
318 | axis(2,at = c(0, 1, 2, 3,4), labels = c(0.1, 10, 100, 1000,10000),cex.axis = 1.55)
319 | abline(v=logavg2,col="red", lwd = 2)
320 | abline(v=loggeo2,col="blue", lwd = 2)
321 | abline(v=logrob2,col="green", lwd = 2)
322 | legend(-90, 4.5, "Accuracy", text.col = "blue",bty = "n", cex = 1.5)
323 | legend(-50, 4.5, "Decisiveness", text.col = "red", bty = "n", cex = 1.5)
324 | legend(-180, 4.5, "Robustness", text.col = "green", bty = "n", cex = 1.5)
325 | legend(-205,4.7,expression(paste(kappa," = 0")), bty = "n", cex = 1.5)
326 |
327 | #(b) kappa = 0.025
328 | ml.coupled2 <- scan(file = "~/Desktop/Project/ml025_alpha2_d2")
329 | mean(ml.coupled2)
330 | expml.coupled2 <- exp(ml.coupled2)
331 | max(expml.coupled2)
332 | min(expml.coupled2)
333 | avg2 <- mean(expml.coupled2)
334 | logavg2 <- log10(avg2)
335 | geo2 <- exp(mean(log(expml.coupled2)))
336 | loggeo2 <- log10(geo2)
337 | rob2 <- (sum(expml.coupled2^(-2/3))/5000)^(-3/2)
338 | logrob2 <- log10(rob2)
339 | b <- seq(-180,1,by = 1)
340 | logscale<-log10(expml.coupled2)
341 | h <- hist(logscale, breaks = b)
342 | new_counts <- log10(h$counts)+1
343 | new_counts[which(new_counts == -Inf)] <- 0
344 | h$counts <- new_counts
345 | plot(h, main = "", ylab = "Frequency in logscale ", ylim = c(0, 4), xlab = "Likelihood in logscale", xaxt="n", yaxt = "n",cex.lab=1.55)
346 | axis(1,at = c(-180, -160, -140, -120, -100, -80, -60, -40, -20, 0), labels = c(10^(-180), 10^(-160), 10^(-140), 10^(-120), 10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.5)
347 | #axis(1,at = c(-100, -80, -60, -40, -20, 0), labels = c(10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.55)
348 | axis(2,at = c(0, 1, 2, 3,4), labels = c(0.1, 10, 100, 1000,10000),cex.axis = 1.55)
349 | abline(v=logavg2,col="red", lwd = 2)
350 | abline(v=loggeo2,col="blue", lwd = 2)
351 | abline(v=logrob2,col="green", lwd = 2)
352 | legend(-80, 4.5, "Accuracy", text.col = "blue",bty = "n", cex = 1.5)
353 | legend(-40, 4.5, "Decisiveness", text.col = "red", bty = "n", cex = 1.5)
354 | legend(-180, 4.5, "Robustness", text.col = "green", bty = "n", cex = 1.5)
355 | legend(-205,4.7,expression(paste(kappa," = 0.025")), bty = "n", cex = 1.5)
356 |
357 | #(c) kappa = 0.05
358 | ml.coupled2 <- scan(file = "~/Desktop/Project/ml005_alpha2_d2")
359 | mean(ml.coupled2)
360 | expml.coupled2 <- exp(ml.coupled2)
361 | max(expml.coupled2)
362 | min(expml.coupled2)
363 | avg2 <- mean(expml.coupled2)
364 | logavg2 <- log10(avg2)
365 | geo2 <- exp(mean(log(expml.coupled2)))
366 | loggeo2 <- log10(geo2)
367 | rob2 <- (sum(expml.coupled2^(-2/3))/5000)^(-3/2)
368 | logrob2 <- log10(rob2)
369 | b <- seq(-180,1,by = 1)
370 | logscale<-log10(expml.coupled2)
371 | h <- hist(logscale, breaks = b)
372 | new_counts <- log10(h$counts)+1
373 | new_counts[which(new_counts == -Inf)] <- 0
374 | h$counts <- new_counts
375 | plot(h, main = "", ylab = "Frequency in logscale ", ylim = c(0, 4), xlab = "Likelihood in logscale", xaxt="n", yaxt = "n",cex.lab=1.55)
376 | axis(1,at = c(-180, -160, -140, -120, -100, -80, -60, -40, -20, 0), labels = c(10^(-180), 10^(-160), 10^(-140), 10^(-120), 10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.5)
377 | #axis(1,at = c(-100, -80, -60, -40, -20, 0), labels = c(10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.55)
378 | axis(2,at = c(0, 1, 2, 3,4), labels = c(0.1, 10, 100, 1000,10000),cex.axis = 1.55)
379 | abline(v=logavg2,col="red", lwd = 2)
380 | abline(v=loggeo2,col="blue", lwd = 2)
381 | abline(v=logrob2,col="green", lwd = 2)
382 | legend(-70, 4.5, "Accuracy", text.col = "blue",bty = "n", cex = 1.5)
383 | legend(-40, 4.5, "Decisiveness", text.col = "red", bty = "n", cex = 1.5)
384 | legend(-170, 4.5, "Robustness", text.col = "green", bty = "n", cex = 1.5)
385 | legend(-205,4.7,expression(paste(kappa," = 0.05")), bty = "n", cex = 1.5)
386 |
387 | #(d) kappa = 0.075
388 | ml.coupled2 <- scan(file = "~/Desktop/Project/ml075_alpha2_d2")
389 | mean(ml.coupled2)
390 | expml.coupled2 <- exp(ml.coupled2)
391 | max(expml.coupled2)
392 | min(expml.coupled2)
393 | avg2 <- mean(expml.coupled2)
394 | logavg2 <- log10(avg2)
395 | geo2 <- exp(mean(log(expml.coupled2)))
396 | loggeo2 <- log10(geo2)
397 | rob2 <- (sum(expml.coupled2^(-2/3))/5000)^(-3/2)
398 | logrob2 <- log10(rob2)
399 | b <- seq(-180,1,by = 1)
400 | logscale<-log10(expml.coupled2)
401 | h <- hist(logscale, breaks = b)
402 | new_counts <- log10(h$counts)+1
403 | new_counts[which(new_counts == -Inf)] <- 0
404 | h$counts <- new_counts
405 | plot(h, main = "", ylab = "Frequency in logscale ", ylim = c(0, 4), xlab = "Likelihood in logscale", xaxt="n", yaxt = "n",cex.lab=1.55)
406 | axis(1,at = c(-180, -160, -140, -120, -100, -80, -60, -40, -20, 0), labels = c(10^(-180), 10^(-160), 10^(-140), 10^(-120), 10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.5)
407 | #axis(1,at = c(-100, -80, -60, -40, -20, 0), labels = c(10^(-100), 10^(-80), 10^(-60), 10^(-40), 10^(-20), 1), cex.axis = 1.55)
408 | axis(2,at = c(0, 1, 2, 3,4), labels = c(0.1, 10, 100, 1000,10000),cex.axis = 1.55)
409 | abline(v=logavg2,col="red", lwd = 2)
410 | abline(v=loggeo2,col="blue", lwd = 2)
411 | abline(v=logrob2,col="green", lwd = 2)
412 | legend(-70, 4.5, "Accuracy", text.col = "blue",bty = "n", cex = 1.5)
413 | legend(-40, 4.5, "Decisiveness", text.col = "red", bty = "n", cex = 1.5)
414 | legend(-170, 4.5, "Robustness", text.col = "green", bty = "n", cex = 1.5)
415 | legend(-205,4.7,expression(paste(kappa," = 0.075")), bty = "n", cex = 1.5)
416 |
417 | #Figure 9: The rose plots of the various standard deviation values in 2 dimensions.
418 | #(a) kappa = 0
419 | sigma00 <- scan(file = "~/Desktop/Project/sigma0_alpha2_d2")
420 | h <- hist(sigma00,breaks = seq(0,1,by = 0.01))
421 | dt <- data.frame(A = h$counts/10000, B=seq(0,0.99,by = 0.01))
422 | p <- ggplot(dt, aes(x=B, y=A, fill=B))+
423 | geom_bar(stat = "identity",color="black",alpha=0.7)+
424 | coord_polar()+
425 | theme_bw()+
426 | labs(x="",y="")+
427 | theme(legend.position = "none")
428 | p
429 |
430 | #(b) kappa = 0.025
431 | sigma00 <- scan(file = "~/Desktop/Project/sigma025_alpha2_d2")
432 | h <- hist(sigma00,breaks = seq(0,1,by = 0.01))
433 | dt <- data.frame(A = h$counts/10000, B=seq(0,0.99,by = 0.01))
434 | p <- ggplot(dt, aes(x=B, y=A, fill=B))+
435 | geom_bar(stat = "identity",color="black",alpha=0.7)+
436 | coord_polar()+
437 | theme_bw()+
438 | labs(x="",y="")+
439 | theme(legend.position = "none")
440 | p
441 |
442 | #(c) kappa = 0.05
443 | sigma00 <- scan(file = "~/Desktop/Project/sigma005_alpha2_d2")
444 | h <- hist(sigma00,breaks = seq(0,1,by = 0.01))
445 | dt <- data.frame(A = h$counts/10000, B=seq(0,0.99,by = 0.01))
446 | p <- ggplot(dt, aes(x=B, y=A, fill=B))+
447 | geom_bar(stat = "identity",color="black",alpha=0.7)+
448 | coord_polar()+
449 | theme_bw()+
450 | labs(x="",y="")+
451 | theme(legend.position = "none")
452 | p
453 |
454 | #(d) kappa = 0.075
455 | sigma00 <- scan(file = "~/Desktop/Project/sigma075_alpha2_d2")
456 | h <- hist(sigma00,breaks = seq(0,1,by = 0.01))
457 | dt <- data.frame(A = h$counts/10000, B=seq(0,0.99,by = 0.01))
458 | p <- ggplot(dt, aes(x=B, y=A, fill=B))+
459 | geom_bar(stat = "identity",color="black",alpha=0.7)+
460 | coord_polar()+
461 | theme_bw()+
462 | labs(x="",y="")+
463 | theme(legend.position = "none")
464 | p
465 |
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/Rcode/Test:
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2 |
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4993 | -7.583448791503906250e+01
4994 | -8.147340393066406250e+01
4995 | -7.424622344970703125e+01
4996 | -6.395936203002929688e+01
4997 | -6.384303283691406250e+01
4998 | -2.699598693847656250e+01
4999 | -2.765171051025390625e+01
5000 | -8.338285064697265625e+01
5001 |
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/VAE/Test.py:
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1 | print("This is a test file")
2 |
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/VAE/mnist_data.py:
--------------------------------------------------------------------------------
1 | from __future__ import absolute_import
2 | from __future__ import division
3 | from __future__ import print_function
4 |
5 | import gzip
6 | import os
7 |
8 | import numpy
9 | from scipy import ndimage
10 |
11 | from six.moves import urllib
12 |
13 | import tensorflow as tf
14 |
15 | SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
16 | DATA_DIRECTORY = "data"
17 |
18 |
19 | IMAGE_SIZE = 28
20 | NUM_CHANNELS = 1
21 | PIXEL_DEPTH = 255
22 | NUM_LABELS = 10
23 | VALIDATION_SIZE = 5000
24 |
25 | # DOWNLOAD
26 | def try_download(filename):
27 | if not tf.gfile.Exists(DATA_DIRECTORY):
28 | tf.gfile.MakeDirs(DATA_DIRECTORY)
29 | filepath = os.path.join(DATA_DIRECTORY, filename)
30 | if not tf.gfile.Exists(filepath):
31 | filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
32 | with tf.gfile.GFile(filepath) as f:
33 | size = f.size()
34 | print('Successfully downloaded', filename, size, 'bytes.')
35 | return filepath
36 |
37 | # GET IMAGES
38 | def get_data(filename, num_images, norm_shift=False, norm_scale=True):
39 | print('geting', filename)
40 | with gzip.open(filename) as bytestream:
41 | bytestream.read(16)
42 | buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS)
43 | data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)
44 | if norm_shift:
45 | data = data - (PIXEL_DEPTH / 2.0)
46 | if norm_scale:
47 | data = data / PIXEL_DEPTH
48 | data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)
49 | data = numpy.reshape(data, [num_images, -1])
50 | return data
51 |
52 | # GET LABELS
53 | def get_labels(filename, num_images):
54 | """get the labels into a vector of int64 label IDs."""
55 | print('geting', filename)
56 | with gzip.open(filename) as bytestream:
57 | bytestream.read(8)
58 | buf = bytestream.read(1 * num_images)
59 | labels = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.int64)
60 | num_labels_data = len(labels)
61 | one_hot_encoding = numpy.zeros((num_labels_data,NUM_LABELS))
62 | one_hot_encoding[numpy.arange(num_labels_data),labels] = 1
63 | one_hot_encoding = numpy.reshape(one_hot_encoding, [-1, NUM_LABELS])
64 | return one_hot_encoding
65 |
66 | # Augment training data
67 | def expend_training_data(images, labels):
68 |
69 | expanded_images = []
70 | expanded_labels = []
71 |
72 | j = 0
73 | for x, y in zip(images, labels):
74 | j = j+1
75 | if j%100==0:
76 | print ('expanding data : %03d / %03d' % (j,numpy.size(images,0)))
77 |
78 | # register original data
79 | expanded_images.append(x)
80 | expanded_labels.append(y)
81 |
82 | # get a value for the background
83 | # zero is the expected value, but median() is used to estimate background's value
84 | bg_value = numpy.median(x)
85 | image = numpy.reshape(x, (-1, 28))
86 |
87 | for i in range(4):
88 | angle = numpy.random.randint(-15,15,1)
89 | new_img = ndimage.rotate(image,angle,reshape=False, cval=bg_value)
90 |
91 | # shift the image with random distance
92 | shift = numpy.random.randint(-2, 2, 2)
93 | new_img_ = ndimage.shift(new_img,shift, cval=bg_value)
94 |
95 | # register new training data
96 | expanded_images.append(numpy.reshape(new_img_, 784))
97 | expanded_labels.append(y)
98 |
99 | # images and labels are concatenated for random-shuffle at each epoch
100 | # notice that pair of image and label should not be broken
101 | expanded_train_total_data = numpy.concatenate((expanded_images, expanded_labels), axis=1)
102 | numpy.random.shuffle(expanded_train_total_data)
103 |
104 | return expanded_train_total_data
105 |
106 | # MNIST DATA
107 | def prepare_MNIST_data(use_norm_shift=False, use_norm_scale=True, use_data_augmentation=False):
108 | # Get the data.
109 | train_data_filename = try_download('train-images-idx3-ubyte.gz')
110 | train_labels_filename = try_download('train-labels-idx1-ubyte.gz')
111 | test_data_filename = try_download('t10k-images-idx3-ubyte.gz')
112 | test_labels_filename = try_download('t10k-labels-idx1-ubyte.gz')
113 |
114 | # get it into numpy arrays.
115 | train_data = get_data(train_data_filename, 60000, use_norm_shift, use_norm_scale)
116 | train_labels = get_labels(train_labels_filename, 60000)
117 | test_data = get_data(test_data_filename, 10000, use_norm_shift, use_norm_scale)
118 | test_labels = get_labels(test_labels_filename, 10000)
119 |
120 | # Generate a validation set.
121 | validation_data = train_data[:VALIDATION_SIZE, :]
122 | validation_labels = train_labels[:VALIDATION_SIZE,:]
123 | train_data = train_data[VALIDATION_SIZE:, :]
124 | train_labels = train_labels[VALIDATION_SIZE:,:]
125 |
126 | # Concatenate train_data & train_labels for random shuffle
127 | if use_data_augmentation:
128 | train_total_data = expend_training_data(train_data, train_labels)
129 | else:
130 | train_total_data = numpy.concatenate((train_data, train_labels), axis=1)
131 |
132 | train_size = train_total_data.shape[0]
133 |
134 | return train_total_data, train_size, validation_data, validation_labels, test_data, test_labels
135 |
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/VAE/run_main.py:
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1 | import tensorflow as tf
2 | import numpy as np
3 | import mnist_data
4 | import os
5 | import vae
6 | import plot_utils
7 | import glob
8 | import argparse
9 |
10 | IMAGE_SIZE_MNIST = 28
11 |
12 | """
13 | parsing and configuration
14 | Referenced from https://github.com/hwalsuklee/tensorflow-mnist-VAE
15 | """
16 |
17 | def parse_args():
18 | desc = "Tensorflow implementation of 'Variational AutoEncoder (VAE)'"
19 | parser = argparse.ArgumentParser(description=desc)
20 |
21 | parser.add_argument('--results_path', type=str, default='results',
22 | help='File path of output images')
23 |
24 | parser.add_argument('--add_noise', type=bool, default=False, help='Boolean for adding salt & pepper noise to input image')
25 |
26 | parser.add_argument('--dim_z', type=int, default='20', help='Dimension of latent vector', required = True)
27 |
28 | parser.add_argument('--n_hidden', type=int, default=500, help='Number of hidden units in MLP')
29 |
30 | parser.add_argument('--learn_rate', type=float, default=1e-3, help='Learning rate for Adam optimizer')
31 |
32 | parser.add_argument('--num_epochs', type=int, default=20, help='The number of epochs to run')
33 |
34 | parser.add_argument('--batch_size', type=int, default=128, help='Batch size')
35 |
36 | parser.add_argument('--PRR', type=bool, default=True,
37 | help='Boolean for plot-reproduce-result')
38 |
39 | parser.add_argument('--PRR_n_img_x', type=int, default=10,
40 | help='Number of images along x-axis')
41 |
42 | parser.add_argument('--PRR_n_img_y', type=int, default=10,
43 | help='Number of images along y-axis')
44 |
45 | parser.add_argument('--PRR_resize_factor', type=float, default=1.0,
46 | help='Resize factor for each displayed image')
47 |
48 | parser.add_argument('--PMLR', type=bool, default=False,
49 | help='Boolean for plot-manifold-learning-result')
50 |
51 | parser.add_argument('--PMLR_n_img_x', type=int, default=20,
52 | help='Number of images along x-axis')
53 |
54 | parser.add_argument('--PMLR_n_img_y', type=int, default=20,
55 | help='Number of images along y-axis')
56 |
57 | parser.add_argument('--PMLR_resize_factor', type=float, default=1.0,
58 | help='Resize factor for each displayed image')
59 |
60 | parser.add_argument('--PMLR_z_range', type=float, default=2.0,
61 | help='Range for unifomly distributed latent vector')
62 |
63 | parser.add_argument('--PMLR_n_samples', type=int, default=5000,
64 | help='Number of samples in order to get distribution of labeled data')
65 |
66 | return check_args(parser.parse_args())
67 |
68 | """checking arguments"""
69 | def check_args(args):
70 |
71 | # --results_path
72 | try:
73 | os.mkdir(args.results_path)
74 | except(FileExistsError):
75 | pass
76 | # delete all existing files
77 | files = glob.glob(args.results_path+'/*')
78 | for f in files:
79 | os.remove(f)
80 |
81 | # --add_noise
82 | try:
83 | assert args.add_noise == True or args.add_noise == False
84 | except:
85 | print('add_noise must be boolean type')
86 | return None
87 |
88 | # --dim-z
89 | try:
90 | assert args.dim_z > 0
91 | except:
92 | print('dim_z must be positive integer')
93 | return None
94 |
95 | # --n_hidden
96 | try:
97 | assert args.n_hidden >= 1
98 | except:
99 | print('number of hidden units must be larger than one')
100 |
101 | # --learn_rate
102 | try:
103 | assert args.learn_rate > 0
104 | except:
105 | print('learning rate must be positive')
106 |
107 | # --num_epochs
108 | try:
109 | assert args.num_epochs >= 1
110 | except:
111 | print('number of epochs must be larger than or equal to one')
112 |
113 | # --batch_size
114 | try:
115 | assert args.batch_size >= 1
116 | except:
117 | print('batch size must be larger than or equal to one')
118 |
119 | # --PRR
120 | try:
121 | assert args.PRR == True or args.PRR == False
122 | except:
123 | print('PRR must be boolean type')
124 | return None
125 |
126 | if args.PRR == True:
127 | # --PRR_n_img_x, --PRR_n_img_y
128 | try:
129 | assert args.PRR_n_img_x >= 1 and args.PRR_n_img_y >= 1
130 | except:
131 | print('PRR : number of images along each axis must be larger than or equal to one')
132 |
133 | # --PRR_resize_factor
134 | try:
135 | assert args.PRR_resize_factor > 0
136 | except:
137 | print('PRR : resize factor for each displayed image must be positive')
138 |
139 | # --PMLR
140 | try:
141 | assert args.PMLR == True or args.PMLR == False
142 | except:
143 | print('PMLR must be boolean type')
144 | return None
145 |
146 | if args.PMLR == True:
147 | try:
148 | assert args.dim_z == 2
149 | except:
150 | print('PMLR : dim_z must be two')
151 |
152 | # --PMLR_n_img_x, --PMLR_n_img_y
153 | try:
154 | assert args.PMLR_n_img_x >= 1 and args.PMLR_n_img_y >= 1
155 | except:
156 | print('PMLR : number of images along each axis must be larger than or equal to one')
157 |
158 | # --PMLR_resize_factor
159 | try:
160 | assert args.PMLR_resize_factor > 0
161 | except:
162 | print('PMLR : resize factor for each displayed image must be positive')
163 |
164 | # --PMLR_z_range
165 | try:
166 | assert args.PMLR_z_range > 0
167 | except:
168 | print('PMLR : range for unifomly distributed latent vector must be positive')
169 |
170 | # --PMLR_n_samples
171 | try:
172 | assert args.PMLR_n_samples > 100
173 | except:
174 | print('PMLR : Number of samples in order to get distribution of labeled data must be large enough')
175 |
176 | return args
177 | """main function"""
178 | def main(args):
179 |
180 | """ parameters """
181 | RESULTS_DIR = args.results_path
182 |
183 | # network architecture
184 | ADD_NOISE = args.add_noise
185 |
186 | n_hidden = args.n_hidden
187 | dim_img = IMAGE_SIZE_MNIST**2 # number of pixels for a MNIST image
188 | dim_z = args.dim_z
189 |
190 | # train
191 | n_epochs = args.num_epochs
192 | batch_size = args.batch_size
193 | learn_rate = args.learn_rate
194 |
195 | # Plot
196 | PRR = args.PRR # Plot Reproduce Result
197 | PRR_n_img_x = args.PRR_n_img_x # number of images along x-axis in a canvas
198 | PRR_n_img_y = args.PRR_n_img_y # number of images along y-axis in a canvas
199 | PRR_resize_factor = args.PRR_resize_factor # resize factor for each image in a canvas
200 |
201 | PMLR = args.PMLR # Plot Manifold Learning Result
202 | PMLR_n_img_x = args.PMLR_n_img_x # number of images along x-axis in a canvas
203 | PMLR_n_img_y = args.PMLR_n_img_y # number of images along y-axis in a canvas
204 | PMLR_resize_factor = args.PMLR_resize_factor# resize factor for each image in a canvas
205 | PMLR_z_range = args.PMLR_z_range # range for random latent vector
206 | PMLR_n_samples = args.PMLR_n_samples # number of labeled samples to plot a map from input data space to the latent space
207 |
208 | """ prepare MNIST data """
209 |
210 | train_total_data, train_size, _, _, test_data, test_labels = mnist_data.prepare_MNIST_data()
211 | n_samples = train_size
212 |
213 | """ build graph """
214 |
215 | # input placeholders
216 | # In denoising-autoencoder, x_hat == x + noise, otherwise x_hat == x
217 | x_hat = tf.placeholder(tf.float32, shape=[None, dim_img], name='input_img')
218 | x = tf.placeholder(tf.float32, shape=[None, dim_img], name='target_img')
219 |
220 | # dropout
221 | keep_prob = tf.placeholder(tf.float32, name='keep_prob')
222 |
223 | # input for PMLR
224 | z_in = tf.placeholder(tf.float32, shape=[None, dim_z], name='latent_variable')
225 |
226 | # network architecture
227 | y, z, loss, neg_marginal_likelihood, KL_divergence, mu, sigma = vae.autoencoder(x_hat, x, dim_img, dim_z, n_hidden, keep_prob)
228 | # print(mu.shape)
229 |
230 |
231 | # optimization
232 | train_op = tf.train.AdamOptimizer(learn_rate).minimize(loss)
233 |
234 | """ training """
235 |
236 | # Plot for reproduce performance
237 | if PRR:
238 | PRR = plot_utils.Plot_Reproduce_Performance(RESULTS_DIR, PRR_n_img_x, PRR_n_img_y, IMAGE_SIZE_MNIST, IMAGE_SIZE_MNIST, PRR_resize_factor)
239 |
240 | x_PRR = test_data[0:PRR.n_tot_imgs, :]
241 |
242 | x_PRR_img = x_PRR.reshape(PRR.n_tot_imgs, IMAGE_SIZE_MNIST, IMAGE_SIZE_MNIST)
243 | PRR.save_images(x_PRR_img, name='input.jpg')
244 |
245 | if ADD_NOISE:
246 | x_PRR = x_PRR * np.random.randint(2, size=x_PRR.shape)
247 | x_PRR += np.random.randint(2, size=x_PRR.shape)
248 |
249 | x_PRR_img = x_PRR.reshape(PRR.n_tot_imgs, IMAGE_SIZE_MNIST, IMAGE_SIZE_MNIST)
250 | PRR.save_images(x_PRR_img, name='input_noise.jpg')
251 |
252 | # Plot for manifold learning result
253 | if PMLR and dim_z == 2:
254 |
255 | PMLR = plot_utils.Plot_Manifold_Learning_Result(RESULTS_DIR, PMLR_n_img_x, PMLR_n_img_y, IMAGE_SIZE_MNIST, IMAGE_SIZE_MNIST, PMLR_resize_factor, PMLR_z_range)
256 |
257 | x_PMLR = test_data[0:PMLR_n_samples, :]
258 | id_PMLR = test_labels[0:PMLR_n_samples, :]
259 |
260 | if ADD_NOISE:
261 | x_PMLR = x_PMLR * np.random.randint(2, size=x_PMLR.shape)
262 | x_PMLR += np.random.randint(2, size=x_PMLR.shape)
263 |
264 | decoded = vae.decoder(z_in, dim_img, n_hidden)
265 |
266 | # train
267 | total_batch = int(n_samples / batch_size)
268 | min_tot_loss = 1e99
269 |
270 | with tf.Session() as sess:
271 |
272 | sess.run(tf.global_variables_initializer(), feed_dict={keep_prob : 0.9})
273 |
274 | for epoch in range(n_epochs):
275 |
276 | # Random shuffling
277 | np.random.shuffle(train_total_data)
278 | train_data_ = train_total_data[:, :-mnist_data.NUM_LABELS]
279 |
280 | # Loop over all batches
281 | for i in range(total_batch):
282 | # Compute the offset of the current minibatch in the data.
283 | offset = (i * batch_size) % (n_samples)
284 | batch_xs_input = train_data_[offset:(offset + batch_size), :]
285 |
286 | batch_xs_target = batch_xs_input
287 |
288 | # add salt & pepper noise
289 | if ADD_NOISE:
290 | batch_xs_input = batch_xs_input * np.random.randint(2, size=batch_xs_input.shape)
291 | batch_xs_input += np.random.randint(2, size=batch_xs_input.shape)
292 |
293 | _, tot_loss, loss_likelihood, loss_divergence = sess.run(
294 | (train_op, loss, neg_marginal_likelihood, KL_divergence),
295 | feed_dict={x_hat: batch_xs_input, x: batch_xs_target, keep_prob : 0.9})
296 | sigma1 = sess.run(sigma, feed_dict={x_hat: batch_xs_input, x: batch_xs_target, keep_prob : 0.9})
297 | mu1 = sess.run(mu, feed_dict={x_hat: batch_xs_input, x: batch_xs_target, keep_prob: 0.9})
298 |
299 | # print cost every epoch
300 | # print("epoch %d: L_tot %03.2f L_likelihood %03.2f L_divergence %03.2f" % (epoch, tot_loss, loss_likelihood, loss_divergence))
301 | print("epoch %d: L_tot %03.2f L_likelihood %03.2f" % (epoch, tot_loss, loss_likelihood))
302 |
303 |
304 | # if minimum loss is updated or final epoch, plot results
305 | if min_tot_loss > tot_loss or epoch+1 == n_epochs:
306 | min_tot_loss = tot_loss
307 | # Plot for reproduce performance
308 | if PRR:
309 | y_PRR = sess.run(y, feed_dict={x_hat: x_PRR, keep_prob : 1})
310 | y_PRR_img = y_PRR.reshape(PRR.n_tot_imgs, IMAGE_SIZE_MNIST, IMAGE_SIZE_MNIST)
311 | PRR.save_images(y_PRR_img, name="/PRR_epoch_%02d" %(epoch) + ".jpg")
312 |
313 | # Plot for manifold learning result
314 | if PMLR and dim_z == 2:
315 | y_PMLR = sess.run(decoded, feed_dict={z_in: PMLR.z, keep_prob : 1})
316 | y_PMLR_img = y_PMLR.reshape(PMLR.n_tot_imgs, IMAGE_SIZE_MNIST, IMAGE_SIZE_MNIST)
317 | PMLR.save_images(y_PMLR_img, name="/PMLR_epoch_%02d" % (epoch) + ".jpg")
318 |
319 | # plot distribution of labeled images
320 | z_PMLR = sess.run(z, feed_dict={x_hat: x_PMLR, keep_prob : 1})
321 | PMLR.save_scattered_image(z_PMLR,id_PMLR, name="/PMLR_map_epoch_%02d" % (epoch) + ".jpg")
322 | # np.savetxt('marginal_divergence', loss_divergence)
323 | np.savetxt('marginal_likelihood', loss_divergence)
324 | np.savetxt('sigma', sigma1)
325 | np.savetxt('mu', mu1)
326 |
327 | if __name__ == '__main__':
328 |
329 | # parse arguments
330 | args = parse_args()
331 | if args is None:
332 | exit()
333 |
334 | # main
335 | main(args)
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/VAE/vae.py:
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1 | import tensorflow as tf
2 | import math as m
3 | import tensorflow_probability as tfp
4 | tfd = tfp.distributions
5 |
6 | # Gaussian MLP as encoder
7 | def gaussian_MLP_encoder(x, n_hidden, n_output, keep_prob):
8 | with tf.variable_scope("gaussian_MLP_encoder"):
9 | # initializers
10 | w_init = tf.contrib.layers.variance_scaling_initializer()
11 | b_init = tf.constant_initializer(0.)
12 |
13 | # 1st hidden layer
14 | w0 = tf.get_variable('w0', [x.get_shape()[1], n_hidden], initializer=w_init)
15 | b0 = tf.get_variable('b0', [n_hidden], initializer=b_init)
16 | h0 = tf.matmul(x, w0) + b0
17 | h0 = tf.nn.elu(h0)
18 | h0 = tf.nn.dropout(h0, keep_prob)
19 |
20 | # 2nd hidden layer
21 | w1 = tf.get_variable('w1', [h0.get_shape()[1], n_hidden], initializer=w_init)
22 | b1 = tf.get_variable('b1', [n_hidden], initializer=b_init)
23 | h1 = tf.matmul(h0, w1) + b1
24 | h1 = tf.nn.tanh(h1)
25 | h1 = tf.nn.dropout(h1, keep_prob)
26 |
27 | # output layer
28 | wo = tf.get_variable('wo', [h1.get_shape()[1], n_output * 2], initializer=w_init)
29 | bo = tf.get_variable('bo', [n_output * 2], initializer=b_init)
30 | gaussian_params = tf.matmul(h1, wo) + bo
31 |
32 | # The mean parameter is unconstrained
33 | mean = gaussian_params[:, :n_output]
34 | # The standard deviation must be positive. Parametrize with a softplus and
35 | # add a small epsilon for numerical stability
36 | stddev = 1e-6 + tf.nn.softplus(gaussian_params[:, n_output:])
37 |
38 | return mean, stddev
39 |
40 | # Bernoulli MLP as decoder
41 | def bernoulli_MLP_decoder(z, n_hidden, n_output, keep_prob, reuse=False):
42 |
43 | with tf.variable_scope("bernoulli_MLP_decoder", reuse=reuse):
44 | w_init = tf.contrib.layers.variance_scaling_initializer()
45 | b_init = tf.constant_initializer(0.)
46 |
47 | # 1st hidden layer
48 | w0 = tf.get_variable('w0', [z.get_shape()[1], n_hidden], initializer=w_init)
49 | b0 = tf.get_variable('b0', [n_hidden], initializer=b_init)
50 | h0 = tf.matmul(z, w0) + b0
51 | h0 = tf.nn.tanh(h0)
52 | h0 = tf.nn.dropout(h0, keep_prob)
53 |
54 | # 2nd hidden layer
55 | w1 = tf.get_variable('w1', [h0.get_shape()[1], n_hidden], initializer=w_init)
56 | b1 = tf.get_variable('b1', [n_hidden], initializer=b_init)
57 | h1 = tf.matmul(h0, w1) + b1
58 | h1 = tf.nn.elu(h1)
59 | h1 = tf.nn.dropout(h1, keep_prob)
60 |
61 | # output layer-mean
62 | wo = tf.get_variable('wo', [h1.get_shape()[1], n_output], initializer=w_init)
63 | bo = tf.get_variable('bo', [n_output], initializer=b_init)
64 | y = tf.sigmoid(tf.matmul(h1, wo) + bo)
65 | return y
66 |
67 | # Autoencoder
68 | def autoencoder(x_hat, x, dim_img, dim_z, n_hidden, keep_prob):
69 | mean, stddev = gaussian_MLP_encoder(x_hat, n_hidden, dim_z, keep_prob)
70 | z = mean + stddev * tf.random_normal(tf.shape(mean), 0, 1, dtype=tf.float32)
71 | y = bernoulli_MLP_decoder(z, n_hidden, dim_img, keep_prob)
72 | y = tf.clip_by_value(y, 1e-8, 1 - 1e-8)
73 |
74 | # loss
75 | k = - 0.025
76 | d = 20
77 | d1 = 1 + d * k + 2 * k
78 | marginal_likelihood = tf.reduce_sum(x * tf.subtract(tf.pow(y, (2 * k) / (1 + k)), 1) / (2 * k) + (1 - x)
79 | * tf.subtract(tf.pow(1 - y, (2 * k) / (1 + k)), 1) / (k * 2), 1)
80 | KL_d1 = tf.reduce_prod(tf.pow(2 * tf.constant(m.pi), k / (1 + d * k)) * tf.sqrt(d1 / (d1 - 2 * k * tf.square(stddev)))
81 | * tf.exp(tf.square(mean) * d1 * k / (1 + d * k) / (d1 - 2 * k * tf.square(stddev))), 1)
82 | KL_d2 = tf.reduce_prod(tf.pow(2 * tf.constant(m.pi) * tf.square(stddev),
83 | k / (1 + k * d)) * tf.sqrt(d1 / (1 + d * k)), 1)
84 | KL_divergence = (KL_d1 - KL_d2) / k / 2
85 | ml = marginal_likelihood
86 | marginal_likelihood = tf.reduce_mean(marginal_likelihood)
87 | KL_divergence = tf.reduce_mean(KL_divergence)
88 | ELBO = marginal_likelihood - KL_divergence
89 | loss = -ELBO
90 |
91 | return y, z, loss, -marginal_likelihood, ml, mean, stddev
92 |
93 | def decoder(z, dim_img, n_hidden):
94 |
95 | y = bernoulli_MLP_decoder(z, n_hidden, dim_img, 1.0, reuse=True)
96 |
97 | return y
98 |
99 |
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