杭州 企业门户网站建设,网站侧边 跟随 样式,江西省寻乌县建设局网站,全国十大计算机培训机构1、摘要
本文主要讲解#xff1a;麻雀算法SSA优化LSTM长短期记忆网络实现分类算法 主要思路#xff1a;
准备一份分类数据#xff0c;数据介绍在第二章准备好麻雀算法SSA#xff0c;要用随机数据跑起来用lstm把分类数据跑起来将lstm的超参数交给SSA去优化优化完的最优参数…1、摘要
本文主要讲解麻雀算法SSA优化LSTM长短期记忆网络实现分类算法 主要思路
准备一份分类数据数据介绍在第二章准备好麻雀算法SSA要用随机数据跑起来用lstm把分类数据跑起来将lstm的超参数交给SSA去优化优化完的最优参数给lstm去做最后一次训练
2、数据介绍
Cll出料量 Lsp量水平 Djzsp电解质水平 Djwd工作温度 Fzb分子比 Fe铁含量 Si:硅含量 Ludiyajiang压降 Ddlsp打点量水平 Avv平均电压 wv工作电压 avaev平均故障发生时的电压 ae故障发生标签0未发生1发生 除分子比、压降、打点量水平外其余数据均是一天一采集数据异常时发生故障故障电压高于工作电压。 数据下载链接
3、相关技术
麻雀搜索算法(Sparrow Search Algorithm, SSA)是一种新型的群智能优化算法在2020年提出主要是受麻雀的觅食行为和反捕食行为的启发 以下是一些图片可加深你的理解 4、完整代码和步骤
此代码的依赖环境如下
tensorflow2.5.0
numpy1.19.5
keras2.6.0
matplotlib3.5.2麻雀算法用随机数据跑起来的代码
# -*- coding: utf-8 -*-
import numpy as np
import random
import matplotlib.pyplot as pltdef fun(x):a np.sum(x ** 2)return adef Bounds(x, lb, ub):temp x.reshape(-1, 2)I temp lbtemp[I] lb[I]J temp ubtemp[J] ub[J]return tempdef SSA(M, pop, dim, P_percent, c, d):# M 迭代次数 SSA(1000, 20, 2, 0.2, -10, 10)# pop 麻雀种群数量# dim 寻优维度# P_percent 麻雀在生产者的比例# c d分别是寻优范围的最小值与最大值pNum round(pop * P_percent) # pNum是生产者lb c * np.ones((1, dim)) # 下边界ub d * np.ones((1, dim)) # 上边界x np.zeros((pop, dim))fit np.zeros((pop, 1))# 种群初始化for i in range(pop):x[i, :] lb (ub - lb) * np.random.rand(1, dim)fit[i] fun(x[i, :])pFit fit.copy()pX x.copy()fMin np.min(fit)bestI np.argmin(fit)bestX x[bestI, :].copy()Convergence_curve np.zeros((M,))trace np.zeros((M, dim))for t in range(M):sortIndex np.argsort(pFit.reshape(-1, )).reshape(-1, )fmax np.max(pFit)B np.argmax(pFit)worse x[B, :].copy()r2 np.random.rand()## 这一部分为发现者探索者的位置更新if r2 0.8: # %预警值较小说明没有捕食者出现for i in range(pNum): # r2小于0.8时发现者改变位置r1 np.random.rand()x[sortIndex[i], :] pX[sortIndex[i], :] * np.exp(-i / (r1 * M))x[sortIndex[i], :] Bounds(x[sortIndex[i], :], lb, ub)temp fun(x[sortIndex[i], :])fit[sortIndex[i]] temp # 计算新的适应度值else: # 预警值较大说明有捕食者出现威胁到了种群的安全需要去其它地方觅食for i in range(pNum): # r2大于0.8时发现者改变位置r1 np.random.rand()x[sortIndex[i], :] pX[sortIndex[i], :] np.random.normal() * np.ones((1, dim))x[sortIndex[i], :] Bounds(x[sortIndex[i], :], lb, ub)fit[sortIndex[i]] fun(x[sortIndex[i], :]) # 计算新的适应度值bestII np.argmin(fit)bestXX x[bestII, :].copy()##这一部分为加入者追随者的位置更新for i in range(pNum 1, pop): # 剩下的个体变化A np.floor(np.random.rand(1, dim) * 2) * 2 - 1if i pop / 2: # 这个代表这部分麻雀处于十分饥饿的状态因为它们的能量很低也是是适应度值很差需要到其它地方觅食x[sortIndex[i], :] np.random.normal() * np.exp((worse - pX[sortIndex[i], :]) / (i ** 2))else: # 这一部分追随者是围绕最好的发现者周围进行觅食其间也有可能发生食物的争夺使其自己变成生产者x[sortIndex[i], :] bestXX np.abs(pX[sortIndex[i], :] - bestXX).dot(A.T * (A * A.T) ** (-1)) * np.ones((1, dim))x[sortIndex[i], :] Bounds(x[sortIndex[i], :], lb, ub) # 判断边界是否超出fit[sortIndex[i]] fun(x[sortIndex[i], :]) # 计算适应度值# 这一部分为意识到危险注意这里只是意识到了危险不代表出现了真正的捕食者的麻雀的位置更新c random.sample(range(sortIndex.shape[0]),sortIndex.shape[0]) # 这个的作用是在种群中随机产生其位置也就是这部分的麻雀位置一开始是随机的意识到危险了要进行位置移动b sortIndex[np.array(c)[0:round(pop * 0.2)]].reshape(-1, )for j in range(b.shape[0]):if pFit[sortIndex[b[j]]] fMin: # 处于种群外围的麻雀的位置改变x[sortIndex[b[j]], :] bestX np.random.normal(1, dim) * (np.abs(pX[sortIndex[b[j]], :] - bestX))else: # 处于种群中心的麻雀的位置改变x[sortIndex[b[j]], :] pX[sortIndex[b[j]], :] (2 * np.random.rand() - 1) * (np.abs(pX[sortIndex[b[j]], :] - worse)) / (pFit[sortIndex[b[j]]] - fmax 1e-50)x[sortIndex[b[j]], :] Bounds(x[sortIndex[b[j]], :], lb, ub)fit[sortIndex[b[j]]] fun(x[sortIndex[b[j]], :]) # 计算适应度值# 这部分是最终的最优解更新for i in range(pop):if fit[i] pFit[i]:pFit[i] fit[i].copy()pX[i, :] x[i, :].copy()if pFit[i] fMin:fMin pFit[i, 0].copy()bestX pX[i, :].copy()trace[t, :] bestXConvergence_curve[t] fMinreturn bestX, fMin, Convergence_curve, trace# In[]
bestX, fMin, Convergence_curve, trace SSA(1000, 20, 2, 0.2, -10, 10)
plt.figure()
plt.plot(Convergence_curve)
plt.show()
代码输出如下
lstm分类算法实现
import osimport matplotlib.pyplot as plt
import pandas as pd
from tensorflow.python.keras import Sequential
from tensorflow.python.keras.layers import Dense, CuDNNLSTM
from tensorflow.python.keras.layers import Dropout
from tensorflow.python.keras.models import Sequential
from numpy.random import seedseed(7)os.chdir(rC:\Projects\old\判断异常是否发生)
train_path data.csv
# train_path 15.xlsx
usecols [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
df pd.read_csv(train_path, usecolsusecols)
df[ae] df[ae].map(lambda x: 1 if x 1 else 0)
df.fillna(0, inplaceTrue)
train_size int(len(df) * 0.9)
train df.iloc[:train_size, :]
test df.iloc[train_size:, :]X_train train.loc[:, train.columns ! ae].values # converts the df to a numpy array
y_train train[ae].values
X_train X_train.astype(float)X_train X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
print(X_train.shape, y_train.shape)X_test test.loc[:, test.columns ! ae].values # converts the df to a numpy arrayy_test test[ae].values
X_test X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
X_test X_test.astype(float)
print(X_test.shape, X_test.shape)def create_model(input_length):model Sequential()model.add(CuDNNLSTM(units50, return_sequencesTrue, input_shape(input_length, 1)))model.add(Dropout(0.2))model.add(CuDNNLSTM(units50, return_sequencesFalse))model.add(Dropout(0.2))model.add(Dense(1, activationsigmoid))model.compile(lossbinary_crossentropy, optimizeradam, metrics[accuracy])model.summary()return modelmodel create_model(len(X_train[0]))
hist model.fit(X_train, y_train, batch_size64, validation_split0.2, epochs2, shuffleFalse, verbose1)plt.plot(hist.history[accuracy], labelacc)
plt.plot(hist.history[val_accuracy], labelval_acc)
plt.legend()
plt.show()plt.plot(hist.history[loss], labelloss)
plt.legend()
plt.show()
分类算法效果如下 损失图如下
合并麻雀算法和lstm算法用麻雀算法SSA优化LSTM长短期记忆网络实现分类算法
from random import seedimport matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.metrics import precision_score, recall_score, classification_report
from tensorflow.python.framework.random_seed import set_random_seed
from tensorflow.python.keras.layers import CuDNNLSTM
from tensorflow.python.keras.layers import Dense, Dropout
from tensorflow.python.keras.models import Sequential# 这两行代码解决 plt 中文显示的问题
plt.rcParams[font.sans-serif] [SimHei]
plt.rcParams[axes.unicode_minus] False
matplotlib.rcParams[font.sans-serif] [SimHei] # 用黑体显示中文
set_random_seed(11)
seed(7)def pdReadCsv(file, sep):try:data pd.read_csv(file, sepsep, encodingutf-8, error_bad_linesFalse, enginepython)return dataexcept:data pd.read_csv(file, sepsep, encodinggbk, error_bad_linesFalse, enginepython)return dataclass SSA():def __init__(self, func, n_dimNone, pop_size20, max_iter50, lb-512, ub512, verboseFalse):self.func funcself.n_dim n_dim # dimension of particles, which is the number of variables of funcself.pop pop_size # number of particlesP_percent 0.2 # # 生产者的人口规模占总人口规模的20%D_percent 0.1 # 预警者的人口规模占总人口规模的10%self.pNum round(self.pop * P_percent) # 生产者的人口规模占总人口规模的20%self.warn round(self.pop * D_percent) # 预警者的人口规模占总人口规模的10%self.max_iter max_iter # max iterself.verbose verbose # print the result of each iter or notself.lb, self.ub np.array(lb) * np.ones(self.n_dim), np.array(ub) * np.ones(self.n_dim)assert self.n_dim len(self.lb) len(self.ub), dim len(lb) len(ub) is not Trueassert np.all(self.ub self.lb), upper-bound must be greater than lower-boundself.X np.random.uniform(lowself.lb, highself.ub, size(self.pop, self.n_dim))self.Y [self.func(self.X[i]) for i in range(len(self.X))] # y f(x) for all particlesself.pbest_x self.X.copy() # personal best location of every particle in historyself.pbest_y [np.inf for i in range(self.pop)] # best image of every particle in historyself.gbest_x self.pbest_x.mean(axis0).reshape(1, -1) # global best location for all particlesself.gbest_y np.inf # global best y for all particlesself.gbest_y_hist [] # gbest_y of every iterationself.update_pbest()self.update_gbest()## record verbose valuesself.record_mode Falseself.record_value {X: [], V: [], Y: []}self.best_x, self.best_y self.gbest_x, self.gbest_y # history reasons, will be deprecatedself.idx_max 0self.x_max self.X[self.idx_max, :]self.y_max self.Y[self.idx_max]def cal_y(self, start, end):# calculate y for every x in Xfor i in range(start, end):self.Y[i] self.func(self.X[i])# return self.Ydef update_pbest(self):personal bestfor i in range(len(self.Y)):if self.pbest_y[i] self.Y[i]:self.pbest_x[i] self.X[i]self.pbest_y[i] self.Y[i]def update_gbest(self):idx_min self.pbest_y.index(min(self.pbest_y))if self.gbest_y self.pbest_y[idx_min]:self.gbest_x self.X[idx_min, :].copy()self.gbest_y self.pbest_y[idx_min]def find_worst(self):self.idx_max self.Y.index(max(self.Y))self.x_max self.X[self.idx_max, :]self.y_max self.Y[self.idx_max]def update_finder(self):self.idx sorted(enumerate(self.Y), keylambda x: x[1])self.idx [self.idx[i][0] for i in range(len(self.idx))]# 这一部位为发现者探索者的位置更新if r2 0.8: # 预警值较小说明没有捕食者出现for i in range(self.pNum):r1 np.random.rand(1)self.X[self.idx[i], :] self.X[self.idx[i], :] * np.exp(-(i) / (r1 * self.max_iter)) # 对自变量做一个随机变换self.X np.clip(self.X, self.lb, self.ub) # 对超过边界的变量进行去除# X[idx[i], :] Bounds(X[idx[i], :], lb, ub) # 对超过边界的变量进行去除# fit[sortIndex[0, i], 0] func(X[sortIndex[0, i], :]) # 算新的适应度值elif r2 0.8: # 预警值较大说明有捕食者出现威胁到了种群的安全需要去其它地方觅食for i in range(self.pNum):Q np.random.rand(1) # 也可以替换成 np.random.normal(loc0, scale1.0, size1)self.X[self.idx[i], :] self.X[self.idx[i], :] Q * np.ones((1, self.n_dim)) # Q是服从正态分布的随机数。L表示一个1×d的矩阵self.cal_y(0, self.pNum)def update_follower(self):# 这一部位为加入者追随者的位置更新for ii in range(self.pop - self.pNum):i ii self.pNumA np.floor(np.random.rand(1, self.n_dim) * 2) * 2 - 1best_idx self.Y[0:self.pNum].index(min(self.Y[0:self.pNum]))bestXX self.X[best_idx, :]if i self.pop / 2:Q np.random.rand(1)self.X[self.idx[i], :] Q * np.exp((self.x_max - self.X[self.idx[i], :]) / np.square(i))else:self.X[self.idx[i], :] bestXX np.dot(np.abs(self.X[self.idx[i], :] - bestXX),1 / (A.T * np.dot(A, A.T))) * np.ones((1, self.n_dim))self.X np.clip(self.X, self.lb, self.ub) # 对超过边界的变量进行去除# X[self.idx[i],:] Bounds(X[self.idx[i],lb,ub)# fit[self.idx[i],0] func(X[self.idx[i], :])self.cal_y(self.pNum, self.pop)def detect(self):arrc np.arange(self.pop)c np.random.permutation(arrc) # 随机排列序列b [self.idx[i] for i in c[0: self.warn]]e 10e-10for j in range(len(b)):if self.Y[b[j]] self.gbest_y:self.X[b[j], :] self.gbest_y np.random.rand(1, self.n_dim) * np.abs(self.X[b[j], :] - self.gbest_y)self.X np.clip(self.X, self.lb, self.ub) # 对超过边界的变量进行去除self.Y[b[j]] self.func(self.X[b[j]])def run(self, max_iterNone):self.max_iter max_iter or self.max_iterfor iter_num in range(self.max_iter):self.update_finder() # 更新发现者位置self.find_worst() # 取出最大的适应度值和最差适应度的Xself.update_follower() # 更新跟随着位置self.update_pbest()self.update_gbest()self.detect()self.update_pbest()self.update_gbest()self.gbest_y_hist.append(self.gbest_y)return self.best_x, self.best_yimport osos.chdir(rC:\Projects\old\判断异常是否发生)
train_path data.csv
# train_path 15.xlsx
usecols [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
df pd.read_csv(train_path, usecolsusecols)
df[ae] df[ae].map(lambda x: 1 if x 1 else 0)
df.fillna(0, inplaceTrue)
train_size int(len(df) * 0.9)
train df.iloc[:train_size, :]
test df.iloc[train_size:, :]X_train train.loc[:, train.columns ! ae].values # converts the df to a numpy array
y_train train[ae].values
X_train X_train.astype(float)
X_train X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
print(X_train.shape, y_train.shape)X_test test.loc[:, test.columns ! ae].values # converts the df to a numpy array
y_test test[ae].values
X_test X_test.astype(float)
X_test X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
print(X_test.shape, X_test.shape)precisions []
recalls []
accuracys []def create_model(units, dropout):model Sequential()model.add(CuDNNLSTM(unitsunits, return_sequencesTrue, input_shape(len(X_train[0]), 1)))model.add(Dropout(dropout))model.add(CuDNNLSTM(unitsunits, return_sequencesFalse))model.add(Dropout(dropout))model.add(Dense(1, activationsigmoid))model.compile(lossbinary_crossentropy, optimizeradam, metrics[accuracy])return modeldef f13(x):epochs int(x[0])units int(x[1])dropout x[2]batch_size int(x[3])model create_model(units, dropout)model.fit(X_train, y_train, batch_sizebatch_size, validation_split0.2, epochsepochs, shuffleFalse, verbose1)y_pred model.predict(X_test)y_pred_int np.argmax(y_pred, axis1)precision precision_score(y_test, y_pred_int, averagemacro)recall recall_score(y_test, y_pred_int, averagemacro)print(recall)print(recall)recalls.append(recall)print(precision)print(precision)t classification_report(y_test, y_pred_int, target_names[0, 1], output_dictTrue)accuracy t[accuracy]print(accuracy)print(accuracy)accuracys.append(accuracy)precisions.append(precision)return 1 - precision根据WIFI数据和成绩数据使用麻雀算法加lgb做一个挂科预测pr曲线roc曲线和auc值
if __name__ __main__:# 用于优化的四个参数范围 epochs units dropout batch_sizeup_params [8, 55, 0.55, 55]low_params [1, 5, 0.05, 5]# 开始优化ssa SSA(f13, n_dim4, pop_size10, max_iter2, lblow_params, ubup_params)ssa.run()print(best_params is , ssa.gbest_x)print(best_precision is, 1 - ssa.gbest_y)print(best_accuracy is, max(accuracys))print(best_recall is, max(recalls))epochs int(ssa.gbest_x[0])units int(ssa.gbest_x[1])dropout ssa.gbest_x[2]batch_size int(ssa.gbest_x[3])model create_model(units, dropout)model.fit(X_train, y_train, batch_sizebatch_size, validation_split0.2, epochsepochs, shuffleFalse, verbose1)y_pred model.predict(X_test)y_pred_int np.argmax(y_pred, axis1)precision precision_score(y_test, y_pred_int, averagemacro)recall recall_score(y_test, y_pred_int, averagemacro)print(recall)print(recall)recalls.append(recall)print(precision)print(precision)t classification_report(y_test, y_pred_int, target_names[0, 1], output_dictTrue)accuracy t[accuracy]print(accuracy)print(accuracy)输出如下
D:\Program\CONDA\python.exe D:/Program/JacksonProject/SSA/SSA_LSTM_CLASS.py
2023-03-06 22:51:51.834759: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2023-03-06 22:51:53.071157: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2023-03-06 22:51:53.071890: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
(880, 12, 1) (880,)
(98, 12, 1) (98, 12, 1)
2023-03-06 22:51:53.091260: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 3070 computeCapability: 8.6
coreClock: 1.755GHz coreCount: 46 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2023-03-06 22:51:53.091462: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2023-03-06 22:51:53.097473: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2023-03-06 22:51:53.097585: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2023-03-06 22:51:53.100620: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2023-03-06 22:51:53.101778: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2023-03-06 22:51:53.109522: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2023-03-06 22:51:53.112297: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2023-03-06 22:51:53.112870: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2023-03-06 22:51:53.112999: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2023-03-06 22:51:53.113308: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-03-06 22:51:53.114119: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 3070 computeCapability: 8.6
coreClock: 1.755GHz coreCount: 46 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 417.29GiB/s
2023-03-06 22:51:53.114374: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2023-03-06 22:51:53.114483: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2023-03-06 22:51:53.114591: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2023-03-06 22:51:53.114692: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2023-03-06 22:51:53.114785: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2023-03-06 22:51:53.114881: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2023-03-06 22:51:53.114980: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2023-03-06 22:51:53.115076: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2023-03-06 22:51:53.115187: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2023-03-06 22:51:53.585208: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2023-03-06 22:51:53.585330: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0
2023-03-06 22:51:53.585394: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N
2023-03-06 22:51:53.585616: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6573 MB memory) - physical GPU (device: 0, name: NVIDIA GeForce RTX 3070, pci bus id: 0000:01:00.0, compute capability: 8.6)
2023-03-06 22:51:53.586592: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2023-03-06 22:51:53.917241: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
Epoch 1/7
2023-03-06 22:51:54.744603: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2023-03-06 22:51:55.357995: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2023-03-06 22:51:55.362788: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll1/40 [..............................] - ETA: 1:33 - loss: 0.7500 - accuracy: 0.33332023-03-06 22:51:56.317708: I tensorflow/stream_executor/cuda/cuda_blas.cc:1838] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
40/40 [] - 3s 16ms/step - loss: 0.4230 - accuracy: 0.8329 - val_loss: 0.1822 - val_accuracy: 0.9545
Epoch 2/7
40/40 [] - 0s 7ms/step - loss: 0.3202 - accuracy: 0.9040 - val_loss: 0.1702 - val_accuracy: 0.9545
Epoch 3/7
40/40 [] - 0s 7ms/step - loss: 0.2662 - accuracy: 0.9040 - val_loss: 0.1241 - val_accuracy: 0.9545
Epoch 4/7
40/40 [] - 0s 7ms/step - loss: 0.1704 - accuracy: 0.9089 - val_loss: 0.0482 - val_accuracy: 1.0000
Epoch 5/7
40/40 [] - 0s 7ms/step - loss: 0.0522 - accuracy: 0.9981 - val_loss: 0.0148 - val_accuracy: 1.0000
Epoch 6/7
40/40 [] - 0s 7ms/step - loss: 0.0212 - accuracy: 1.0000 - val_loss: 0.0079 - val_accuracy: 1.0000
Epoch 7/7
40/40 [] - 0s 7ms/step - loss: 0.0123 - accuracy: 1.0000 - val_loss: 0.0048 - val_accuracy: 1.0000
D:\Program\CONDA\lib\site-packages\sklearn\metrics\_classification.py:1268: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use zero_division parameter to control this behavior._warn_prf(average, modifier, msg_start, len(result))
D:\Program\CONDA\lib\site-packages\sklearn\metrics\_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use zero_division parameter to control this behavior._warn_prf(average, modifier, msg_start, len(result))
recall
0.5
precision
0.47959183673469385
accuracy
0.9591836734693877
Epoch 1/6
20/20 [] - 1s 18ms/step - loss: 0.4540 - accuracy: 0.8943 - val_loss: 0.2004 - val_accuracy: 0.9545
Epoch 2/6
20/20 [] - 0s 10ms/step - loss: 0.3300 - accuracy: 0.9040 - val_loss: 0.1801 - val_accuracy: 0.9545
Epoch 3/6
20/20 [] - 0s 10ms/step - loss: 0.2932 - accuracy: 0.9040 - val_loss: 0.1763 - val_accuracy: 0.9545
Epoch 4/6
20/20 [] - 0s 9ms/step - loss: 0.2894 - accuracy: 0.9040 - val_loss: 0.1689 - val_accuracy: 0.9545
Epoch 5/6
20/20 [] - 0s 9ms/step - loss: 0.2875 - accuracy: 0.9040 - val_loss: 0.1584 - val_accuracy: 0.9545
Epoch 6/6
20/20 [] - 0s 10ms/step - loss: 0.2486 - accuracy: 0.9040 - val_loss: 0.1403 - val_accuracy: 0.9545
recall
0.5
precision
0.47959183673469385
accuracy
0.9591836734693877
Epoch 1/6
18/18 [] - 1s 22ms/step - loss: 0.4610 - accuracy: 0.8135 - val_loss: 0.1842 - val_accuracy: 0.9545
Epoch 2/6
18/18 [] - 0s 10ms/step - loss: 0.3438 - accuracy: 0.9025 - val_loss: 0.1815 - val_accuracy: 0.9545
Epoch 3/6
18/18 [] - 0s 10ms/step - loss: 0.3157 - accuracy: 0.9025 - val_loss: 0.1755 - val_accuracy: 0.9545
Epoch 4/6
18/18 [] - 0s 8ms/step - loss: 0.3025 - accuracy: 0.9025 - val_loss: 0.1599 - val_accuracy: 0.9545
Epoch 5/6
18/18 [] - 0s 8ms/step - loss: 0.2707 - accuracy: 0.9025 - val_loss: 0.1373 - val_accuracy: 0.9545
Epoch 6/6
18/18 [] - 0s 9ms/step - loss: 0.2300 - accuracy: 0.9025 - val_loss: 0.1047 - val_accuracy: 0.9545
recall
0.5
precision
0.47959183673469385
accuracy
0.9591836734693877
Epoch 1/5
完整代码和数据链接
