如何解决使用稀疏标签形状问题训练LSTM模型吗?
LSTM模型可预测股票的日收益。我曾经使用pd.qcut()
将数据四分位数以这些四分位数作为稀疏标签进行分组。
然后我建立了LSTM模型:
regressor = Sequential()
regressor.add(LSTM(units = 50,return_sequences = True,input_shape = (X_train_scaled_sequence.shape[1],X_train_scaled_sequence.shape[2])))
regressor.add(Dropout(DROUPOUT))
regressor.add(LSTM(units = 50,return_sequences = True))
regressor.add(Dropout(DROUPOUT))
regressor.add(LSTM(units = 50,return_sequences = True))
regressor.add(Dropout(DROUPOUT))
regressor.add(LSTM(units = 50))
regressor.add(Dropout(DROUPOUT))
regressor.add(Dense(units = 10,activation='softmax'))
opt = SGD(lr=0.001)
regressor.compile(loss = tf.keras.losses.SparseCategoricalCrossentropy(),optimizer = opt,metrics = [tf.keras.metrics.Accuracy()])
history = regressor.fit(X_train_scaled_sequence,Y_train_scaled_sequence,validation_data=(X_val_scaled_sequence,Y_val_scaled_sequence),epochs = EPOCHS,batch_size = BATCH_SIZE)
数据形状:
print(X_train_scaled_sequence.shape)
>>> (2575,60,154)
print(Y_train_scaled_sequence.shape)
>>> (2575,)
但是我得到了这个错误,
raise ValueError("Shapes %s and %s are incompatible" % (self,other))
ValueError: Shapes (None,10) and (None,1) are incompatible
版权声明:本文内容由互联网用户自发贡献,该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容, 请发送邮件至 dio@foxmail.com 举报,一经查实,本站将立刻删除。