Add mode collapse and fix architecture report

6 files changed, 12 insertions, 139 deletions

diff --git a/report/fig/cdc_collapse.png b/report/fig/cdc_collapse.png
new file mode 100644
index 0000000..01e5fd6
--- /dev/null
+++ b/report/fig/cdc_collapse.png
diff --git a/report/fig/cdcdesc.pdf b/report/fig/cdcdesc.pdf
new file mode 100644
index 0000000..07710e8
--- /dev/null
+++ b/report/fig/cdcdesc.pdf
diff --git a/report/fig/cdcgen.pdf b/report/fig/cdcgen.pdf
new file mode 100644
index 0000000..a25d0f5
--- /dev/null
+++ b/report/fig/cdcgen.pdf
diff --git a/report/fig/cdcloss3.png b/report/fig/cdcloss3.png
new file mode 100644
index 0000000..4b3c726
--- /dev/null
+++ b/report/fig/cdcloss3.png
diff --git a/report/fig/clustcollapse.png b/report/fig/clustcollapse.png
new file mode 100644
index 0000000..a1293c8
--- /dev/null
+++ b/report/fig/clustcollapse.png
diff --git a/report/paper.md b/report/paper.md
index 2177177..d89a394 100644
--- a/report/paper.md
+++ b/report/paper.md
@@ -85,15 +85,15 @@ but no mode collapse was observed even with the shallow model.
 
 ## CGAN Architecture description
 
-CGAN is a conditional version of a GAN which utilises labeled data. Unlike DCGAN, CGAN is trained with explicitly provided labels which allow CGAN to associate features with specific labels. This has the intrinsic advantage of allowing us to specify the label of generated data.  The baseline CGAN which we evaluate is visible in figure \ref{fig:cganarc}. The baseline CGAN architecture presents a series blocks each contained a dense layer, LeakyReLu layer (slope=0.2) and a Batch Normalisation layer. The baseline discriminator uses Dense layers, followed by LeakyReLu (slope=0.2) and a Droupout layer.
+CGAN is a conditional version of a GAN which utilises labeled data. Unlike DCGAN, CGAN is trained with explicitly provided labels which allow CGAN to associate features with specific labels. This has the intrinsic advantage of allowing us to specify the label of generated data.  The baseline CGAN which we evaluate is visible in figure \ref{fig:cganarc}. The baseline CGAN architecture presents a series blocks each contained a dense layer, `LeakyReLu` layer (slope=0.2) and a Batch Normalisation layer. The baseline discriminator uses Dense layers, followed by `LeakyReLu` (slope=0.2) and a Droupout layer.
 The optimizer used for training is `Adam`(`learning_rate=0.002`, `beta=0.5`).
 
 The Convolutional CGAN (CDCGAN) analysed follows the structure presented in the relevant Appendix section. It uses TODO ADD BRIEF DESCRIPTION
 
 We evaluate permutations of the architecture involving:
 
-* Shallow CGAN - 1 Dense-LeakyReLu-BN  block
-* Deep CGAN - 5 Dense-LeakyReLu-BN
+* Shallow CGAN - 1 `Dense-LeakyReLu-BN`  block
+* Deep CGAN - 5 `Dense-LeakyReLu-BN`
 * Deep Convolutional GAN - DCGAN + conditional label input
 * One-Sided Label Smoothing (LS)
 * Various Dropout (DO)- Use 0.1, 0.3 and 0.5
@@ -382,8 +382,6 @@ $$ L_{\textrm{total}} = \alpha L_{\textrm{LeNet}} + \beta L_{\textrm{generator}}
 
 <div id="refs"></div>
 
-\newpage
-
 # Appendix 
 
 ## DCGAN-Appendix
@@ -540,145 +538,20 @@ $$ L_{\textrm{total}} = \alpha L_{\textrm{LeNet}} + \beta L_{\textrm{generator}}
 
 ## CDCGAN Alternative Architecture
 
-### Generator
-```
-__________________________________________________________________________________________________
-Layer (type)                    Output Shape         Param #     Connected to                     
-==================================================================================================
-input_1 (InputLayer)            (None, 100)          0                                            
-__________________________________________________________________________________________________
-dense_2 (Dense)                 (None, 3136)         316736      input_1[0][0]                    
-__________________________________________________________________________________________________
-reshape_2 (Reshape)             (None, 7, 7, 64)     0           dense_2[0][0]                    
-__________________________________________________________________________________________________
-conv2d_transpose_1 (Conv2DTrans (None, 14, 14, 64)   36928       reshape_2[0][0]                  
-__________________________________________________________________________________________________
-batch_normalization_1 (BatchNor (None, 14, 14, 64)   256         conv2d_transpose_1[0][0]         
-__________________________________________________________________________________________________
-activation_1 (Activation)       (None, 14, 14, 64)   0           batch_normalization_1[0][0]      
-__________________________________________________________________________________________________
-input_2 (InputLayer)            (None, 1)            0                                            
-__________________________________________________________________________________________________
-conv2d_transpose_2 (Conv2DTrans (None, 28, 28, 64)   36928       activation_1[0][0]               
-__________________________________________________________________________________________________
-dense_1 (Dense)                 (None, 64)           128         input_2[0][0]                    
-__________________________________________________________________________________________________
-batch_normalization_2 (BatchNor (None, 28, 28, 64)   256         conv2d_transpose_2[0][0]         
-__________________________________________________________________________________________________
-reshape_1 (Reshape)             (None, 1, 1, 64)     0           dense_1[0][0]                    
-__________________________________________________________________________________________________
-activation_2 (Activation)       (None, 28, 28, 64)   0           batch_normalization_2[0][0]      
-__________________________________________________________________________________________________
-up_sampling2d_1 (UpSampling2D)  (None, 28, 28, 64)   0           reshape_1[0][0]                  
-__________________________________________________________________________________________________
-multiply_1 (Multiply)           (None, 28, 28, 64)   0           activation_2[0][0]               
-                                                                 up_sampling2d_1[0][0]            
-__________________________________________________________________________________________________
-conv2d_1 (Conv2D)               (None, 28, 28, 64)   36928       multiply_1[0][0]                 
-__________________________________________________________________________________________________
-batch_normalization_3 (BatchNor (None, 28, 28, 64)   256         conv2d_1[0][0]                   
-__________________________________________________________________________________________________
-activation_3 (Activation)       (None, 28, 28, 64)   0           batch_normalization_3[0][0]      
-__________________________________________________________________________________________________
-multiply_2 (Multiply)           (None, 28, 28, 64)   0           activation_3[0][0]               
-                                                                 up_sampling2d_1[0][0]            
-__________________________________________________________________________________________________
-conv2d_2 (Conv2D)               (None, 28, 28, 64)   36928       multiply_2[0][0]                 
-__________________________________________________________________________________________________
-batch_normalization_4 (BatchNor (None, 28, 28, 64)   256         conv2d_2[0][0]                   
-__________________________________________________________________________________________________
-activation_4 (Activation)       (None, 28, 28, 64)   0           batch_normalization_4[0][0]      
-__________________________________________________________________________________________________
-multiply_3 (Multiply)           (None, 28, 28, 64)   0           activation_4[0][0]               
-                                                                 up_sampling2d_1[0][0]            
-__________________________________________________________________________________________________
-conv2d_3 (Conv2D)               (None, 28, 28, 1)    577         multiply_3[0][0]                 
-__________________________________________________________________________________________________
-activation_5 (Activation)       (None, 28, 28, 1)    0           conv2d_3[0][0]                   
-==================================================================================================
-Total params: 466,177
-Trainable params: 465,665
-Non-trainable params: 512
-__________________________________________________________________________________________________
-```
-
-### Discriminator
-
-```
-__________________________________________________________________________________________________
-Layer (type)                    Output Shape         Param #     Connected to                     
-==================================================================================================
-input_3 (InputLayer)            (None, 28, 28, 1)    0                                            
-__________________________________________________________________________________________________
-input_2 (InputLayer)            (None, 1)            0                                            
-__________________________________________________________________________________________________
-conv2d_4 (Conv2D)               (None, 28, 28, 64)   640         input_3[0][0]                    
-__________________________________________________________________________________________________
-dense_3 (Dense)                 (None, 64)           128         input_2[0][0]                    
-__________________________________________________________________________________________________
-batch_normalization_5 (BatchNor (None, 28, 28, 64)   256         conv2d_4[0][0]                   
-__________________________________________________________________________________________________
-reshape_3 (Reshape)             (None, 1, 1, 64)     0           dense_3[0][0]                    
-__________________________________________________________________________________________________
-leaky_re_lu_1 (LeakyReLU)       (None, 28, 28, 64)   0           batch_normalization_5[0][0]      
-__________________________________________________________________________________________________
-up_sampling2d_2 (UpSampling2D)  (None, 28, 28, 64)   0           reshape_3[0][0]                  
-__________________________________________________________________________________________________
-multiply_4 (Multiply)           (None, 28, 28, 64)   0           leaky_re_lu_1[0][0]              
-                                                                 up_sampling2d_2[0][0]            
-__________________________________________________________________________________________________
-conv2d_5 (Conv2D)               (None, 28, 28, 64)   36928       multiply_4[0][0]                 
-__________________________________________________________________________________________________
-batch_normalization_6 (BatchNor (None, 28, 28, 64)   256         conv2d_5[0][0]                   
-__________________________________________________________________________________________________
-leaky_re_lu_2 (LeakyReLU)       (None, 28, 28, 64)   0           batch_normalization_6[0][0]      
-__________________________________________________________________________________________________
-multiply_5 (Multiply)           (None, 28, 28, 64)   0           leaky_re_lu_2[0][0]              
-                                                                 up_sampling2d_2[0][0]            
-__________________________________________________________________________________________________
-conv2d_6 (Conv2D)               (None, 28, 28, 64)   36928       multiply_5[0][0]                 
-__________________________________________________________________________________________________
-batch_normalization_7 (BatchNor (None, 28, 28, 64)   256         conv2d_6[0][0]                   
-__________________________________________________________________________________________________
-leaky_re_lu_3 (LeakyReLU)       (None, 28, 28, 64)   0           batch_normalization_7[0][0]      
-__________________________________________________________________________________________________
-multiply_6 (Multiply)           (None, 28, 28, 64)   0           leaky_re_lu_3[0][0]              
-                                                                 up_sampling2d_2[0][0]            
-__________________________________________________________________________________________________
-conv2d_7 (Conv2D)               (None, 14, 14, 64)   36928       multiply_6[0][0]                 
-__________________________________________________________________________________________________
-batch_normalization_8 (BatchNor (None, 14, 14, 64)   256         conv2d_7[0][0]                   
-__________________________________________________________________________________________________
-leaky_re_lu_4 (LeakyReLU)       (None, 14, 14, 64)   0           batch_normalization_8[0][0]      
-__________________________________________________________________________________________________
-conv2d_8 (Conv2D)               (None, 7, 7, 64)     36928       leaky_re_lu_4[0][0]              
-__________________________________________________________________________________________________
-batch_normalization_9 (BatchNor (None, 7, 7, 64)     256         conv2d_8[0][0]                   
-__________________________________________________________________________________________________
-leaky_re_lu_5 (LeakyReLU)       (None, 7, 7, 64)     0           batch_normalization_9[0][0]      
-__________________________________________________________________________________________________
-flatten_1 (Flatten)             (None, 3136)         0           leaky_re_lu_5[0][0]              
-__________________________________________________________________________________________________
-dropout_1 (Dropout)             (None, 3136)         0           flatten_1[0][0]                  
-__________________________________________________________________________________________________
-dense_4 (Dense)                 (None, 1)            3137        dropout_1[0][0]                  
-==================================================================================================
-Total params: 152,897
-Trainable params: 152,257
-Non-trainable params: 640
-__________________________________________________________________________________________________
-```
-
-## Retrain-Appendix
+\begin{figure}[H]
+\begin{center}
+\includegraphics[width=24em]{fig/cdcgen.pdf}
+\end{center}
+\end{figure}
 
-\begin{figure}
+\begin{figure}[H]
 \begin{center}
-\includegraphics[width=24em]{fig/train_few_real.png}
-\caption{Training with few real samples}
-\label{fig:few_real}
+\includegraphics[width=24em]{fig/cdcdesc.pdf}
 \end{center}
 \end{figure}
 
+## Retrain-Appendix
+
 \begin{figure}[H]
 \begin{center}
 \includegraphics[width=24em]{fig/fake_only.png}