yangwohenmai / lstm Goto Github PK

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM

def fit_lstm(train, n_lag, n_seq, n_batch, nb_epoch, n_neurons):
    # (train, 历史1lag, 预测3个步长, 用1 batch数据, 15网络次数_权重更新, 1个神经元)
    # 重塑训练数据格式 [samples, timesteps, features]
    X, y = train[:, 0:n_lag], train[:, n_lag:]  # 1列22个历史数据，3列22个未来数据
    X = X.reshape(X.shape[0], 1, X.shape[1]) # (22,1,1)
    # 配置一个LSTM神经网络，添加网络参数
    model = Sequential()
    #1 model.add(LSTM(n_neurons,input_length=X.shape[1], input_dim=X.shape[2])) #TEST: input_length
    #1 return Unrecognized keyword arguments passed to LSTM: {'input_length': 1}
    #2 model.add(LSTM(units=n_neurons,batch_size=n_batch,input_shape=(X.shape[1], X.shape[2]))) #TEST: batch_size
    #2 return Unrecognized keyword arguments passed to LSTM: {'batch_size': 1}
    #3 model.add(LSTM(units=n_neurons,activation='tanh',batch_input_shape=(None, X.shape[1], X.shape[2]),stateful=True)) #TEST: batch_input_shape
    #3 return Unrecognized keyword arguments passed to LSTM: {'batch_input_shape': (1, 1, 1)}
    model.add(LSTM(units=n_neurons,input_shape=(X.shape[1], X.shape[2]))) 
    # return 'Sequential' object has no attribute 'reset_states'
    # stateful = True --> 需要shuffle = False
    model.add(Dense(y.shape[1]))
    model.compile(loss='mean_squared_error', optimizer='adam')
    # 调用网络，迭代数据对神经网络进行训练，最后输出训练好的网络模型
    for i in range(nb_epoch):
        model.fit(X, y, epochs=1, batch_size=n_batch, verbose=0, shuffle=False)
        model.reset_states()
    return model

model = fit_lstm(train, n_lag, n_seq, n_batch, n_epochs, n_neurons)