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# -*- coding: utf-8 -*- # 导入库pip install openpyxl -i https://pypi.tuna.tsinghua.edu.cn/simple import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn import metrics from sklearn.metrics import mean_squared_error # 评价指标 import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM, GRU from keras import optimizers import keras import tensorflow as tf # mse rmse mae rmape # adam sgd plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False import warnings warnings.filterwarnings("ignore") # 忽略一些警告 不影响运行 from data_read import date_process train_x,train_y=date_process() # 序列长度 int_sequence_len = train_x.shape[1] # 每个序列的长度 int_a = train_x.shape[2] # 输出几个元素 几步： out_len = train_y.shape[1] # 划分验证集和测试集 x_train, x_test, y_train, y_test = train_test_split(np.array(train_x), np.array(train_y), test_size=0.2, random_state=1,shuffle=False) print(x_train.shape) print(len(x_train), len(x_test)) # 1243 311 x_train = x_train.reshape(len(x_train),int_sequence_len, int_a) # 三维度数据 全部数据长度 序列长度 每个序列维度 y_train = y_train.reshape(len(x_train),out_len) print(x_train.shape) print(y_train.shape) x_test = x_test.reshape(len(x_test),int_sequence_len, int_a) y_test = y_test.reshape(len(x_test),out_len) print(x_test.shape) print(y_test.shape) from keras import backend as K from keras.layers import Layer # def create_model_1(): # model = keras.models.Sequential([ # keras.layers.LSTM(100, activation='relu', input_shape=(int_sequence_len, int_a)), # (1,9) 要与三维度的(1243,1,9) 一一对应 # # 序列长度 每个序列维度 # # (1,9) 要与三维度的(1243,1,9) 一一对应 # # 序列长度 每个序列维度 # AttentionLayer(), # keras.layers.Dense(128, activation='relu'), # 全连接 # keras.layers.Dense(64), # 全连接 # keras.layers.Dense(out_len) # 1个全链接 # ]) # op=optimizers.adam_v2.Adam(lr=0.01) # model.compile(loss='mean_absolute_error', optimizer=op) # 回归损失函数和优化器 Adam SGD # return model from keras.models import Model from keras.layers import Input, LSTM, Dense, Permute, Multiply, Activation, Lambda def build_model(n_timesteps, n_features): inputs = Input(shape=(n_timesteps, n_features)) lstm_out = LSTM(10, return_sequences=True)(inputs) print(lstm_out.shape) # Attention层 attention = Dense(1, activation='tanh')(lstm_out) attention = Permute([2, 1])(attention) attention = Activation('softmax')(attention) attention = Multiply()([lstm_out, attention]) attention = Lambda(lambda x: K.sum(x, axis=1))(attention) outputs = Dense(128, activation='relu')(attention) outputs = Dense(1, activation='relu')(outputs) model = Model(inputs=inputs, outputs=outputs) return model model1 = build_model(int_sequence_len,int_a) op=optimizers.adam_v2.Adam(lr=0.01) model1.compile(loss='mean_absolute_error', optimizer=op) # 回归损失函数和优化器 Adam SGD model1.summary() history=model1.fit(x_train, y_train, validation_data=(x_train, y_train), epochs=50, batch_size=32, shuffle=True) # 训练世代 batch # model1.save_weights('lstmmoxing') # 模型保存 import matplotlib.pyplot as plt training_loss = history.history['loss'] test_loss = history.history['val_loss'] # 创建迭代数量 epoch_count = range(1, len(training_loss) + 1) # 可视化损失历史 plt.plot(epoch_count, training_loss, 'r--') plt.plot(epoch_count, test_loss, 'b-') plt.legend(['Training Loss', 'Test Loss']) plt.title("train loss and test loss") plt.xlabel('Epoch') plt.ylabel('Loss') plt.show() from sklearn.metrics import mean_squared_error # 均方误差 from sklearn.metrics import mean_absolute_error # 平方绝对误差 from sklearn.metrics import r2_score # R square # 调用 # 引用上边的模型实例 # 加载保存好的模型 y1_pred_lstm = model1.predict(x_test) y_true=[] y_pred=[] for i in range(len(y1_pred_lstm)): y_true.extend(y_test[i]) y_pred.extend(y1_pred_lstm[i]) from metra import metric mae, mse, rmse, mape, mspe,r2=metric(np.array(y_pred),np.array(y_true)) print('mae, mse, rmse, mape, mspe,r2') print(mae, mse, rmse, mape, mspe,r2) # 所有画图 len_ = [i for i in range(len(y_true))] plt.xlabel('数据id', fontsize=8) plt.ylabel('收盘价', fontsize=8) plt.plot(len_, y_true, label='y_true', color="blue") plt.plot(len_, y_pred, label='y_pred', color="yellow") plt.title("收盘价预测图") plt.legend() plt.show() plt.clf() # # 输出到excle # name = ['真实值', '预测值'] # test = pd.DataFrame(columns=name, data=result) # test.to_excel('result_.xlsx') # # 输出 。。。excle表