代码代码代码代码代码代码

# -*- 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() train_labels_one_hot = tf.keras.utils.to_categorical(train_y, num_classes=2) print(train_labels_one_hot.shape) train_y=train_labels_one_hot # 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) print(x_train.shape) print(len(x_train), len(x_test)) # 1243 311 x_train = x_train.reshape(len(x_train),-1) # 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),-1) y_test = y_test.reshape(len(x_test),out_len) print(x_test.shape) print(y_test.shape) def create_model_1(): model = keras.models.Sequential([ keras.layers.Dense(1024, activation='relu',input_shape=(int_a*int_sequence_len,)), # 全连接 keras.layers.Dense(512, activation='relu'), # 全连接 keras.layers.Dense(64, activation='relu'), # 全连接 keras.layers.Dense(2,activation='softmax'), # 1个全链接 ]) model.compile(loss=['binary_crossentropy'], metrics=["categorical_accuracy"], optimizer='Adam') # 回归损失函数和优化器 Adam SGD return model model1 = create_model_1() model1.summary() history=model1.fit(x_train, y_train, validation_data=(x_train, y_train), epochs=50, batch_size=16, 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() # 调用 # 引用上边的模型实例 model_jiazai_1 = create_model_1() # 加载保存好的模型 model_jiazai_1.load_weights('lstmmoxing') y1_pred_lstm = model_jiazai_1.predict(x_test) y_true=[] y_pred=[] print(y1_pred_lstm.shape) print(y_test.shape) for i in range(len(y1_pred_lstm)): # print(np.argmax(y1_pred_lstm[i])) # print(np.argmax(y_test[i])) y_true.append(np.argmax(y_test[i])) y_pred.append(np.argmax(y1_pred_lstm[i])) from metra import acc_metra acc_metra(y_true, y_pred,label=['0','1'])