尝试创建 GAN：InvalidArgumentError: Matrix size-incompatible

Question

我对这个领域很陌生，但我正在尝试创建一个生成对抗网络来生成音乐。我有一个模型，它是生成器和鉴别器的组合，但是当我训练它时，它给了我一个错误。关于它不喜欢的输出的一些东西。我正在使用 Keras 顺序。任何帮助将不胜感激。

据我了解，输入和输出在任何 Keras 模型中都应该具有相同的尺寸。我的输入形状 - (300, 30, 1)。输出形状 - (300, 1)。当我单独训练它们时，它们不会导致错误。但是当我将它们组合在一个单独的模型中时，它们开始给出错误 - 特别是在鉴别器的最后一行 -> Dense(1, activation='sigmoid')

    def __generator(self):
        """ Declare generator """
        model = Sequential()
        model.add(LSTM(256, input_shape=(self.n_prev, 1), return_sequences=True))
        model.add(Dropout(0.6))
        model.add(LSTM(128, input_shape=(self.n_prev, 1), return_sequences=True))
        model.add(Dropout(0.6))
        model.add(LSTM(64, input_shape=(self.n_prev, 1), return_sequences=False))
        model.add(Dropout(0.6))
        model.add(Dense(1))

        print(model.summary())
        return model


    def __discriminator1b (self, width=300, height=30, channels=1):

        shape = (width, height, channels)
        model = Sequential()
        model.add(Flatten(input_shape=((30, 1))))
        model.add(Dense((height * channels), input_shape=(30, 1)))
        model.add(LeakyReLU(alpha=0.2))
        model.add(Dense(np.int64((height * channels)/2)))
        model.add(LeakyReLU(alpha=0.2))

        model.add(Dense(1, activation='sigmoid'))   

        print(model.summary())
        return model


    def __gen_disc (self):
        model = Sequential()
        model.add(self.G)
        model.add(self.D)

        return model

Training:
self.G.train_on_batch(np.array(gen_noiseX), np.array(genY))
self.D.train_on_batch(np.array(gen_noiseX), disc_label)
self.GD.train_on_batch(np.array(gen_noiseX), y_mislabled)

Model Summaries:
Generator:
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_28 (LSTM)               (None, 30, 256)           264192    
_________________________________________________________________
dropout_52 (Dropout)         (None, 30, 256)           0         
_________________________________________________________________
lstm_29 (LSTM)               (None, 30, 128)           197120    
_________________________________________________________________
dropout_53 (Dropout)         (None, 30, 128)           0         
_________________________________________________________________
lstm_30 (LSTM)               (None, 64)                49408     
_________________________________________________________________
dropout_54 (Dropout)         (None, 64)                0         
_________________________________________________________________
dense_36 (Dense)             (None, 1)                 65        
=================================================================
Total params: 510,785
Trainable params: 510,785
Non-trainable params: 0
_________________________________________________________________
None


Discriminator:

Layer (type)                 Output Shape              Param #   
=================================================================
flatten_8 (Flatten)          (None, 30)                0         
_________________________________________________________________
dense_37 (Dense)             (None, 30)                930       
_________________________________________________________________
leaky_re_lu_15 (LeakyReLU)   (None, 30)                0         
_________________________________________________________________
dense_38 (Dense)             (None, 15)                465       
_________________________________________________________________
leaky_re_lu_16 (LeakyReLU)   (None, 15)                0         
_________________________________________________________________
dense_39 (Dense)             (None, 1)                 16        
=================================================================
Total params: 1,411
Trainable params: 1,411
Non-trainable params: 0
_________________________________________________________________
None

所以错误本身就是

InvalidArgumentError: Matrix size-incompatible: In[0]: [300,1], In[1]: [30,30]   [[{{node sequential_22/dense_37/MatMul}}]]

每当我删除鉴别器的 Dense(1, sigmoid) 层时，它都会起作用，但我需要该层进行二进制分类。也许我需要重建模型或只是做一个小修复，但无论如何所有建议都值得赞赏。

Answer 1

欢迎来到stackoverflow。 发生此错误的原因是，your model needs (30,30) but you are feeding it (300,1).
这里有一些更改会更好：

1. 在鉴别器中 model.add(Flatten(input_shape=((30, 1)))) 在错误的地方。它应该在dense 层之前。或者当您正在构建 RNN 时，我认为您不需要 Flatten 层。
2. 使用bidirectional LSTMs。
3. 在生成器中使用batchnormalization。

整个网络还需要一些其他的更改，你可以看到这个great article for music generation。希望这会有所帮助！