```
split = self.data['sold'].size - self.length
if (self.showGraph):
self.data.plot()
plt.show()
self.train = self.data[:split]
self.test = self.data[split:]
# trainDup = self.train
# self.train = trainDup.append(self.train)
self.scaler = MinMaxScaler()
self.trainS = self.scaler.fit_transform(self.train)
self.testS = self.scaler.transform(self.test)
self.batch_size = 1
self.trainGen = TimeseriesGenerator(self.trainS,targets=self.trainS,length=self.length,batch_size=1)
self.testGen = TimeseriesGenerator(self.testS,targets=self.testS,length=self.length-1,batch_size=1)
self.data.isnull()
batches = int(self.data['sold'].size / self.batch_size)
self.input_shape = (self.length,self.train.columns.values.size)
self.batch_input_shape = (self.batch_size,self.length,self.train.columns.values.size)
self.model = Sequential()
self.model.add(SimpleRNN(
12,return_sequences=False,input_shape=self.input_shape
))
self.model.add(Activation('relu'))
self.model.add(Dense(1))
self.model.add(Activation('relu'))
def loss(value,pred):
from sklearn.metrics import mean_absolute_error
return mean_absolute_error(value,pred)
self.model.compile(optimizer=Adam(learning_rate=0.001),loss='mse',metrics=['accuracy'])
print(self.model.summary())
```
预测是橙色 实际结果是蓝色年图。
我试过 LSTM，RNNsimple：
以及我尝试过的许多不同的变体
1024-6 个单位我尝试过 dropout 并且没有缩放器我已经更改了 batch_size 使用 L2、L1 调节器进行拟合，我已经改变了每周、每月、每年、每天的数据我尝试了一种复制技术来拥有 10000 个数据行我尝试过使用 ANN 和 batch_input_shape 等等。但我找不到灵魂提款机。
我的数据计数：
Daily = 1895
Yearly = 1895 / 365
Monthly = 1895 / 12
Weekly = 1895 / 7
Duplicated = 20100 AND I HAVE TRIED SHUFFLING.