十分钟看懂 Keras fit_generator multi-loss中的弯弯绕

拼搏现实的明天。 2022-02-28 14:20 101阅读 0赞

在训练神经网络的过程中发现Keras fit\_generator训练的效果莫名其妙地比我手写的网络好

虽然很恼火于没有找到原因

还是沦陷在Keras的便捷之下

初探fit\_generator有些不知所措，完全摸不着头绪

稍微弄明白一些之后写了一段示例代码

相信足够粗浅易懂

import tensorflow as tf
    from keras import backend as K
    from keras.layers import Conv2D,Activation,Add,Input,Lambda
    from keras.models import Model
    from keras.losses import mean_absolute_error, mean_squared_error
    import numpy as np
    def mae(a, b):
        return mean_absolute_error(a, b)
    
    def mse(a, b):
        return mean_squared_error(a, b)
    
    def error(a, b):
        return a
    
    def generator():
        while True:
            in_1=np.array([1])
            in_2=np.array([5])
            in_3=np.array([10])
            pre_1=np.array([2])
            pre_2=np.array([20])
            pre_3=np.array([40])
            yield [in_1,in_2,in_3],[pre_1,pre_2,pre_3]
    
    
    input_1 =Input(shape=(1,), name='input_1', dtype='float32')
    
    input_2 =Input(shape=(1,), name='input_2', dtype='float32')
    
    input_3 =Input(shape=(1,), name='input_3', dtype='float32')
    
    
    output_1 = Add(name='output_1_name')([input_1,input_2])
    
    output_2 = Add(name='output_2_name')([input_2,input_3])
    
    output_3 = Add(name='output_3_name')([input_1,input_3])
    
    model=Model([input_1,input_2,input_3], [output_1,output_2,output_3], name='VVD_Model')
    
    model.summary()
    
    model.compile(optimizer='nadam',
                  loss={
                      'output_1_name':mae,
                      'output_2_name':mae,
                      'output_3_name':mae,
                  },
                  metrics={'output_1_name':[mae,mse],
                           'output_2_name':[mae,mse],
                           'output_3_name':[error]})
    
    model.fit_generator(generator(),
                        steps_per_epoch=5,
                        epochs=1,
                        validation_data=generator(),
                        validation_steps=3 ,
                        callbacks=[])
    print('end')
    
    '''
       input_1           input_2         input_3                  
          1                 5               10
          |\               /|               /|
          |    \         /  |             /  |
          |        \   /    |           /    |
          |          / \    |         /      |
          |        /       \|       /        |
          |      /          |  \  /          |
          |    /            |   /  \         |
          |  /              | /        \     |
          |/                |              \ |
          +                 +                +
       output_1         output_2        output_3
          6                 15               11
        pre_1              pre_2            pre_3
          2                 20               40
    mae |6-2|             |15-20|          |11-40|
          4                 5                29
    mse  16                 25               29*29
      
      
    运行结果
    1/5 [=====>........................] - ETA: 3s - loss: 38.0000 - output_1_name_loss: 4.0000 - output_2_name_loss: 5.0000 - output_3_name_loss: 29.0000 - output_1_name_mae: 4.0000 - output_1_name_mse: 16.0000 - output_2_name_mae: 5.0000 - output_2_name_mse: 25.0000 - output_3_name_error: 40.0000
    5/5 [==============================] - 1s 173ms/step - loss: 38.0000 - output_1_name_loss: 4.0000 - output_2_name_loss: 5.0000 - output_3_name_loss: 29.0000 - output_1_name_mae: 4.0000 - output_1_name_mse: 16.0000 - output_2_name_mae: 5.0000 - output_2_name_mse: 25.0000 - output_3_name_error: 40.0000 - val_loss: 38.0000 - val_output_1_name_loss: 4.0000 - val_output_2_name_loss: 5.0000 - val_output_3_name_loss: 29.0000 - val_output_1_name_mae: 4.0000 - val_output_1_name_mse: 16.0000 - val_output_2_name_mae: 5.0000 - val_output_2_name_mse: 25.0000 - val_output_3_name_error: 40.0000
    end  
      
    '''