Delete plot_functions.py

plot_functions.py

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-def plot_function(self,Models,y,logwriter,logfile,strategy,average_dis,distribution,l_list,ite,validate_interval,prefix):
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-            image = tf.cast(image,self.precision)
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-            features,feat = strategy.run(self.predict_encoder,args=(image,))
-            
-            feat.numpy().astype(np.float32)
-            features.numpy().astype(np.float32)
-            classes,invers_counts = np.unique(features,axis=0,return_inverse=True)
-        
-    
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-            self.gui_plot(int(ite/validate_interval),feat,np.asarray(l_list)[1:],np.asarray(average_dis),distribution,colors=y,prefix=prefix) # plot the data with label colors
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-    
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-            if bool(self.verbose) > 0: 
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-                
-                c = np.arange(classes.shape[0])[invers_counts]
-                acc = np.round(accuracy_score(y, c), 5) # accuracy
-                nmi = np.round(normalized_mutual_info_score(y, c), 5) # normalized mutual information
-                ari = np.round(adjusted_rand_score(y, c), 5) # 
-                print('Iter %d: of %d acc=%.5f, nmi=%.5f, loss=%.5f, dis_avg=%.5f' % (ite,self.steps, acc, nmi, l_list[-1], average_dis[-1])) # print into console , specific statistics
-                logdict = dict(iter=ite, acc=acc, nmi=nmi, loss=l_list[-1], dis_avg=average_dis[-1]) # log it into logfile.
-                logwriter.writerow(logdict) # write row
-                logfile.flush()# flush into file to avoid memory consumption
-            else:
-                    print('Iter %d: of %d ,loss=%.5f, dis_avg=%.5f' % (ite,self.steps, l_list[-1], average_dis[-1])) # print information 
-                    logdict = dict(iter=ite, loss=l_list[-1], dis_avg=average_dis[-1]) # log it into logfile.
-                    logwriter.writerow(logdict)
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-
-def plot_gan(self,Models,logwriter,logfile,strategy,d_list,l_list,ite,validate_interval,prefix,generator):
-            image = tf.cast(image,self.precision)
-            models = strategy.run(generator,args=(Models,))
-            view_models =  self.project_a_model(models)
-            self.gui_plot(int(ite/validate_interval),view_models,np.asarray(d_list)[1:],np.asarray(l_list)[1:],models,prefix=prefix) # plot the data with label colors
-            print('Iter %d: of %d loss_generator=%.5f, loss_descriminator=%.5f' % (ite,self.steps)) # print into console , specific statistics
-            logdict = dict(iter=ite,  loss=l_list[-1]) # log it into logfile.
-            logwriter.writerow(logdict) # write row
-            logfile.flush()# flush into file to avoid memory consumption
-        
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-def gui_plot(self,image_real,loss_generator,loss_discriminator,model_similarity,prefix=None):
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-    fig, ((ax1, ax2),(ax3, ax4)) = plt.subplots(nrows=2,ncols=2)
-    s = image_real.shape[1]
-    top_r = np.reshape(image_real,(2*s,2*s))
-    top_f = np.reshape(image_fake,(2*s,2*s))
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-    
-    pca_all = TSNE(2).fit_transform(features)
-    plt.tight_layout()
-    ax1.imshow(top_r)
-    ax1.set_title("Projections from Orthogonal Vectors")
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-    ax2.plot(np.arange(loss_generator.shape[0])*self.validate_interval, loss_generator, 'go--', linewidth=1, markersize=8)
-    ax3.plot(np.arange(loss_discriminator.shape[0])*self.validate_interval, loss_discriminator, 'ro--', linewidth=1, markersize=8)
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-    ax2.set_ylabel('loss', fontsize=10)
-    ax2.set_title("Generative Loss")
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-    ax3.imshow(top_f)
-    ax3.set_title("Discriminative  Loss")
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-    ax4.imshow(np.dot(model_similarity,model_similarity.T))
-    ax4.set_ylabel('Cross correlation', fontsize=10)
-    ax4.set_title("Model similarity")
-    
-    plt.savefig(join(self.results,'%s_output.png' %prefix))
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-    plt.clf()
-    plt.close('all')
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