main_model = Sequential() main_model.add(Conv3D(32, 3, 3,3, input_shape=(20,50,50,1)))' main_model.add(Activation('relu')) main_model.add(MaxPooling3D(pool_size=(2, 2,2)) main_model.add(Conv3D(64, 3, 3,3)) main_model.add(Activation('relu')) main_model.add(MaxPooling3D(pool_size=(2, 2,2))) main_model.add(Dropout(0.8)) main_model.add(Flatten()) #lower features model - CNN2 lower_model1 = Sequential() lower_model1.add(Conv3D(32, 3, 3,3, input_shape=(20,50,50,1))) lower_model1.add(Activation('relu')) lower_model1.add(MaxPooling3D(pool_size=(2, 2,2))) lower_model1.add(Dropout(0.8)) lower_model1.add(Flatten()) #lower features model - CNN3 lower_model2 = Sequential() lower_model2.add(Conv3D(32, 3, 3,3, input_shape=(20,50,50,1))) lower_model2.add(Activation('relu')) lower_model2.add(MaxPooling3D(pool_size=(2, 2,2))) lower_model2.add(Dropout(0.8)) lower_model2.add(Flatten()) merged_model = Merge([main_model, lower_model1,lower_model2],mode='concat') final_model = Sequential() final_model.add(merged_model) final_model.add(Dense(1024,init='normal')) final_model.add(Activation('relu')) final_model.add(Dropout(0.5)) final_model.add(Dense(2,init='normal')) final_model.add(Activation('softmax')) final_model.compile(loss='binary_crossentropy', optimizer='Adam', metrics= ['accuracy']) train=train_data[-10:] test=train_data[-2:] X = np.array([i[0] for i in train]).reshape(-1,20,50,50,1) Y = [i[1] for i in train] test_x = np.array([i[0] for i in test]).reshape(-1,20,50,50,1) test_y = [i[1] for i in test] final_model.fit(np.array(X),np.array(Y),validation_data= (np.array(test_x),np.array(test_y)),batch_size=batch_size,nb_epoch = nb_epoch,validation_split=0.2,shuffle=True,verbose=1) i'm using 50x50 images contained in 20 chunk and that's y my numpy array is 20x50x50 1st and 2nd modelsI'm using sequential model for multi scale 3d cnn network...i don't know y i'm getting this kind of result
