CopyPastor

Detecting plagiarism made easy.

Score: 1; Reported for: String similarity Open both answers

Possible Plagiarism

Reposted on 2019-09-29
by James

Original Post

Original - Posted on 2019-09-29
by James



            

            

                
            

        

        

            Present in both answers;
            Present only in the new answer;
            Present only in the old answer;
        

    

    

    

        

            

                

                    
                        try this demo with keras==2.2.4 and tensorflow==1.13.1:
                        
                        

                        
                            from keras import Sequential, Model
                        
                            from keras.layers import Embedding, GlobalAveragePooling1D, Dense, concatenate
                        
                            import numpy as np
                        
                            
                        
                            model1 = Sequential()
                        
                            model1.add(Embedding(20, 10, trainable=True))
                        
                            model1.add(GlobalAveragePooling1D())
                        
                            model1.add(Dense(1, activation='sigmoid'))
                        
                            model2 = Sequential()
                        
                            model2.add(Embedding(20, 10, trainable=True))
                        
                            model2.add(GlobalAveragePooling1D())
                        
                            model2.add(Dense(1, activation='sigmoid'))
                        
                            
                        
                            model_concat = concatenate([model1.output, model2.output], axis=-1)
                        
                            model_concat = Dense(1, activation='softmax')(model_concat)
                        
                            model = Model(inputs=[model1.input, model2.input], outputs=model_concat)
                        
                            
                        
                            model.compile(loss='binary_crossentropy', optimizer='adam')
                        
                            
                        
                            X_train_source = np.random.randint(0, 20, (10000, 256))
                        
                            X_train_pod = np.random.randint(0, 20, (10000, 256))
                        
                            Y_train = np.random.randint(0, 2, 10000)
                        
                            
                        
                            model.fit([X_train_source, X_train_pod], Y_train, batch_size=1000, epochs=200,
                        
                                          verbose=True)
                        
                

            

            

                

                    
                        try this demo with keras==2.2.4 and tensorflow==1.13.1:
                        
                        

                        
                            from keras import Sequential, Model
                        
                            from keras.layers import Embedding, GlobalAveragePooling1D, Dense, concatenate
                        
                            import numpy as np
                        
                            
                        
                            model1 = Sequential()
                        
                            model1.add(Embedding(20, 10, trainable=True))
                        
                            model1.add(GlobalAveragePooling1D())
                        
                            model1.add(Dense(1, activation='sigmoid'))
                        
                            model2 = Sequential()
                        
                            model2.add(Embedding(20, 10, trainable=True))
                        
                            model2.add(GlobalAveragePooling1D())
                        
                            model2.add(Dense(1, activation='sigmoid'))
                        
                            
                        
                            model_concat = concatenate([model1.output, model2.output], axis=-1)
                        
                            model_concat = Dense(1, activation='softmax')(model_concat)
                        
                            model = Model(inputs=[model1.input, model2.input], outputs=model_concat)
                        
                            
                        
                            model.compile(loss='binary_crossentropy', optimizer='adam')
                        
                            
                        
                            X_train_source = np.random.randint(0, 20, (10000, 256))
                        
                            X_train_pod = np.random.randint(0, 20, (10000, 256))
                        
                            Y_train = np.random.randint(0, 2, 10000)
                        
                            
                        
                            model.fit([X_train_source, X_train_pod], Y_train, batch_size=1000, epochs=200,
                        
                                          verbose=True)
                        
                

            

        

    

    

        

        

            Present in both answers;
            Present only in the new answer;
            Present only in the old answer;
        

    



    











    

         Made by the SOBotics team.
            Published on StackApps.
            Source code on GitHub.
            Content provided by Stack Exchange API
            under the  CC BY-SA 3.0 license.