LSTM训练和预测算法关于航迹预测项目源码（下载即用）

#!/usr/bin/python # -*- coding:utf-8 -*- # @Author: DingQinbin # @Time : 2020/10/26 9:39 import numpy as np from keras.layers.core import Dense, Activation, Dropout from keras.layers import LSTM from keras.models import Sequential, load_model from keras.callbacks import Callback import keras.backend.tensorflow_backend as KTF import tensorflow as tf import pandas as pd import os import keras.callbacks import matplotlib.pyplot as plt # 设定为自增长 os.environ['CUDA_VISIBLE_DEVICES'] = '0' config = tf.ConfigProto() config.gpu_options.allow_growth = True session = tf.Session(config=config) KTF.set_session(session) def create_dataset(data, n_predictions, n_next): ''' 对数据进行处理 ''' dim = data.shape[1] train_X, train_Y = [], [] for i in range(data.shape[0] - n_predictions - n_next - 1): a = data[i:(i + n_predictions), :] train_X.append(a) tempb = data[(i + n_predictions):(i + n_predictions + n_next), :] b = [] for j in range(len(tempb)): for k in range(dim): b.append(tempb[j, k]) train_Y.append(b) train_X = np.array(train_X, dtype='float64') train_Y = np.array(train_Y, dtype='float64') test_X, test_Y = [], [] i = data.shape[0] - n_predictions - n_next - 1 a = data[i:(i + n_predictions), :] test_X.append(a) tempb = data[(i + n_predictions):(i + n_predictions + n_next), :] b = [] for j in range(len(tempb)): for k in range(dim): b.append(tempb[j, k]) test_Y.append(b) test_X = np.array(test_X, dtype='float64') test_Y = np.array(test_Y, dtype='float64') return train_X, train_Y, test_X, test_Y def NormalizeMult(data, set_range): ''' 返回归一化后的数据和最大最小值 ''' normalize = np.arange(2 * data.shape[1], dtype='float64') normalize = normalize.reshape(data.shape[1], 2) for i in range(0, data.shape[1]): if set_range == True: list = data[:, i] listlow, listhigh = np.percentile(list, [0, 100]) else: if i == 0: listlow = -90 listhigh = 90 else: listlow = -180 listhigh = 180 normalize[i, 0] = listlow normalize[i, 1] = listhigh delta = listhigh - listlow if delta != 0: for j in range(0, data.shape[0]): data[j, i] = (data[j, i] - listlow) / delta return data, normalize def trainModel(train_X, train_Y): ''' trainX，trainY: 训练LSTM模型所需要的数据 ''' model = Sequential() model.add(LSTM( 120, input_shape=(train_X.shape[1], train_X.shape[2]), return_sequences=True)) model.add(Dropout(0.3)) model.add(LSTM( 120, return_sequences=False)) model.add(Dropout(0.3)) model.add(Dense( train_Y.shape[1])) model.add(Activation("relu")) model.compile(loss='mse', optimizer='adam', metrics=['acc']) model.fit(train_X, train_Y, epochs=100, batch_size=64, verbose=1) model.summary() return model if __name__ == "__main__": train_num = 6 per_num = 1 # set_range = False set_range = True # 读入时间序列的文件数据 data = pd.read_csv('20080403010747.txt', sep=',').iloc[:, 0:2].values print("样本数：{0}，维度：{1}".format(data.shape[0], data.shape[1])) # print(data) # 画样本数据库 plt.scatter(data[:, 1], data[:, 0], c='b', marker='o', label='traj_A') plt.legend(loc='upper left') plt.grid() plt.show() # 归一化 data, normalize = NormalizeMult(data, set_range) # print(normalize) # 生成训练数据 train_X, train_Y, test_X, test_Y = create_dataset(data, train_num, per_num) print("x\n", train_X.shape) print("y\n", train_Y.shape) # 训练模型 model = trainModel(train_X, train_Y) loss, acc = model.evaluate(train_X, train_Y, verbose=2) print('Loss : {}, Accuracy: {}'.format(loss, acc * 100)) # 保存模型 np.save("./traj_model_trueNorm.npy", normalize) model.save("./traj_model_120.h5")