如何正确设置keras密集输出层的大小

Question

我正在尝试使用来自midi文件的数据在keras中运行RNN来生成音乐，但我在模型的最后一层遇到了一个问题，该错误出现在错误中：“检查目标时出错：预期的density_26为形状为（1，），但数组的形状为（110539，）”。当标签的大小为（110539）时，为什么网络希望输出层大小为1，密集层应为多少？我的代码如下：

input_data=[np.random.randint(0,110539) for i in range(32427)]
input_temp =[]
output_temp = []
for i in range(0,len(input_data)-seq_length,1):
    input_temp.append(input_data[i:i+seq_length])
    output_temp.append(input_data[i+seq_length])

sequences = len(output_temp)

x = np.reshape(input_temp,(sequences,seq_length,1))
x = x/classes
y = keras.utils.to_categorical(output_temp)
classes = y[0].shape[0]

model = Sequential()
model.add(CuDNNLSTM(512,input_shape=(seq_length,1),return_sequences=True))
model.add(Dropout(0.2))
model.add(CuDNNLSTM(512))
model.add(Dropout(0.2))
model.add(Dense(256,activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(classes,activation='softmax'))
opt = keras.optimizers.Adam(lr=1e-3,decay=1e-5)
model.compile(loss='sparse_categorical_crossentropy',optimizer=opt)
model.fit(x,y,epochs=3)

当我打印x.shape时，我得到（32327，100，1）并且y的尺寸是（32327，类）。感谢您的帮助

edit：model.summary（）的输出

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
cu_dnnlstm_1 (CuDNNLSTM)     (None, 100, 512)          1054720   
_________________________________________________________________
dropout_1 (Dropout)          (None, 100, 512)          0         
_________________________________________________________________
cu_dnnlstm_2 (CuDNNLSTM)     (None, 512)               2101248   
_________________________________________________________________
dropout_2 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 256)               131328    
_________________________________________________________________
dropout_3 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 110539)            28408523  
=================================================================