import syssys.tracebacklimit = 0import osos.environ['PYTHONUNBUFFERED'] = '1'import arcpy# 获取参数input_features = arcpy.GetParameterAsText(0)join_field = arcpy.GetParameterAsText(1)target_feature = arcpy.GetParameterAsText(2)target_field = arcpy.GetParameterAsText(3)area_threshold = arcpy.GetParameterAsText(4)# 创建空间连接join_result = arcpy.SpatialJoin_analysis(input_features, target_feature, "in_memory/spatial_join", "JOIN_ONE_TO_ONE", "KEEP_ALL", "", "INTERSECT")# 使用MakeFeatureLayer创建要素图层，并使用AddFieldDelimiters处理字段名称join_layer = arcpy.management.MakeFeatureLayer(join_result, "join_layer").getOutput(0)join_field_name = arcpy.AddFieldDelimiters(join_layer, join_field)# 使用SelectLayerByAttribute选择重叠面积大于阈值的要素arcpy.management.SelectLayerByAttribute(join_layer, "NEW_SELECTION", "Shape_Area > " + str(area_threshold))# 使用SummaryStatistics工具进行面积求和summary_table = arcpy.Statistics_analysis(join_layer, "in_memory/summary_table", [[join_field_name, "SUM_Shape_Area"]], [join_field_name])# 使用TableToNumPyArray将结果转换为字典sum_dict = {}with arcpy.da.TableToNumPyArray(summary_table, [join_field, "SUM_Shape_Area"]) as arr: for row in arr: sum_dict[row[0]] = row[1]# 使用UpdateCursor更新目标要素类的目标字段with arcpy.da.UpdateCursor(target_feature, [target_field, join_field], sql_clause=(None, "ORDER BY OBJECTID")) as cursor: for row in cursor: join_value = row[1] if join_value in sum_dict: area_sum = sum_dict[join_value] row[0] = area_sum cursor.updateRow(row)# 导出结果output_feature = arcpy.GetParameterAsText(5)arcpy.CopyFeatures_management(target_feature, output_feature)# 删除游标对象和要素图层对象del cursor, join_layer运行错误Traceback (most recent call last): ExecuteError: 执行失败。参数无效。 WARNING 000725: 输出表: 数据集 in_memory\summary_table 已存在。 ERROR 000800: 该值不是 SUM | MEAN | MIN | MAX | RANGE | STD | COUNT | FIRST | LAST 的成员。 ERROR 000728: 表中不存在字段 "数据" 执行(Statistics)失败。请改正代码

Python基于os.environ从windows获取环境变量

在上述代码中，os.environ.get()方法用于获取指定环境变量的值，如果该变量不存在，它会返回None或预设的默认值（如果提供了第二个参数）。os.environ.keys()返回一个迭代器，包含所有环境变量的键，而os....

the introduction of python os.zip_OND3_python os模块简介_youthot3

os.system()函数可以执行shell命令，os.environ是一个字典对象，用于获取和设置环境变量。例如，你可以通过os.environ['PATH']来查看或修改系统的PATH环境变量。在多线程或多进程编程中，os模块的os.fork()...

import sys sys.tracebacklimit = 0 import os os.environ['PYTHONUNBUFFERED'] = '1' import arcpy # 获取参数 input_features = arcpy.GetParameterAsText(0) join_field = arcpy.GetParameterAsText(1) target_feature = arcpy.GetParameterAsText(2) target_field = arcpy.GetParameterAsText(3) area_threshold = arcpy.GetParameterAsText(4) # 创建空间连接 join_result = arcpy.SpatialJoin_analysis(input_features, target_feature, "in_memory/spatial_join", "JOIN_ONE_TO_ONE", "KEEP_ALL", "", "INTERSECT") # 使用MakeFeatureLayer创建要素图层，并使用AddFieldDelimiters处理字段名称 join_layer = arcpy.management.MakeFeatureLayer(join_result, "join_layer").getOutput(0) join_field_name = arcpy.AddFieldDelimiters(join_layer, join_field) # 使用SelectLayerByAttribute选择重叠面积大于阈值的要素 arcpy.management.SelectLayerByAttribute(join_layer, "NEW_SELECTION", "Shape_Area > " + str(area_threshold)) # 使用SummaryStatistics工具进行面积求和 summary_table = arcpy.Statistics_analysis(join_layer, "in_memory/summary_table", [["Shape_Area", "SUM"]], [join_field_name]) # 使用TableToNumPyArray将结果转换为字典 sum_dict = {} with arcpy.da.TableToNumPyArray(summary_table, [join_field, "SUM_Shape_Area"]) as arr: for row in arr: sum_dict[row[0]] = row[1] # 使用UpdateCursor更新目标要素类的目标字段 with arcpy.da.UpdateCursor(target_feature, [target_field, join_field], sql_clause=(None, "ORDER BY OBJECTID")) as cursor: for row in cursor: join_value = row[1] if join_value in sum_dict: area_sum = sum_dict[join_value] row[0] = area_sum cursor.updateRow(row) # 导出结果 output_feature = arcpy.GetParameterAsText(5) arcpy.CopyFeatures_management(target_feature, output_feature) # 删除游标对象和要素图层对象 del cursor, join_layer运行错误：Traceback (most recent call last): ExecuteError: 执行失败。参数无效。 WARNING 000725: 输出表: 数据集 in_memory\summary_table 已存在。 ERROR 000728: 表中不存在字段 "数据" 执行(Statistics)失败。执行(ccc)失败。请改正代码

os.environ['PYTHONUNBUFFERED'] = '1' import arcpy # 获取参数 input_features = arcpy.GetParameterAsText(0) join_field = arcpy.GetParameterAsText(1) target_feature = arcpy.GetParameterAsText(2) target_...

import arcpy # 获取参数 input_features = arcpy.GetParameterAsText(0) join_field = arcpy.GetParameterAsText(1) target_feature = arcpy.GetParameterAsText(2) target_field = arcpy.GetParameterAsText(3) area_threshold = arcpy.GetParameterAsText(4) # 创建空间连接 join_result = arcpy.SpatialJoin_analysis(input_features, target_feature, "in_memory/spatial_join", "JOIN_ONE_TO_ONE", "KEEP_ALL", "", "INTERSECT") # 使用MakeFeatureLayer创建要素图层，并使用AddFieldDelimiters处理字段名称 join_layer = arcpy.management.MakeFeatureLayer(join_result, "join_layer").getOutput(0) join_field_name = arcpy.AddFieldDelimiters(join_layer, join_field) # 使用SelectLayerByAttribute选择重叠面积大于阈值的要素 arcpy.management.SelectLayerByAttribute(join_layer, "NEW_SELECTION", "Shape_Area > " + str(area_threshold)) # 使用SummaryStatistics工具进行面积求和 summary_table = arcpy.Statistics_analysis(join_layer, "in_memory/summary_table", [["Shape_Area", "SUM"]], join_field_name) # 使用TableToNumPyArray将结果转换为字典 sum_dict = {} with arcpy.da.TableToNumPyArray(summary_table, [join_field, "SUM_Shape_Area"]) as arr: for row in arr: sum_dict[row[0]] = row[1] # 使用UpdateCursor更新目标要素类的目标字段 with arcpy.da.UpdateCursor(target_feature, [target_field, join_field], sql_clause=(None, "ORDER BY OBJECTID")) as cursor: for row in cursor: join_value = row[1] if join_value in sum_dict: area_sum = sum_dict[join_value] row[0] = area_sum cursor.updateRow(row) # 导出结果 output_feature = arcpy.GetParameterAsText(5) arcpy.CopyFeatures_management(target_feature, output_feature) # 删除游标对象和要素图层对象 del cursor, join_layer运行错误： Traceback (most recent call last): File "D:\实验2\空间连接.py", line 25, in <module> AttributeError: exit 执行(ccc)失败。请改正代码

os.environ['PYTHONUNBUFFERED'] = '1' 2. 检查是否有其他程序或进程占用了要素类或者表格，导致程序无法正常执行。你可以尝试手动删除要素类或表格，然后重新运行程序。 3. 确认所使用的参数是否正确，特别是...

import os import sys import PyQt5 dirname = os.path.dirname(PyQt5.file) plugin_path = os.path.join(dirname,'Qt', 'plugins', 'platforms') os.environ['QT_QPA_PLATFORM_PLUGIN_PATH'] = plugin_path from PyQt5 import QtWidgets app = QtWidgets.QApplication(sys.argv)

首先，导入了必要的模块os、sys、PyQt5和QtWidgets。然后，使用os模块获取PyQt5模块所在的目录路径，并拼接出QT平台插件的路径。接着，将该路径设置到环境变量QT_QPA_PLATFORM_PLUGIN_PATH中。最后，使用QtWidgets...

import tensorflow.compat.v1 as tf import os from tensorflow import keras import numpy as np import random from collections import Counter from sklearn.model_selection import LeaveOneGroupOut from Utils.mean_average_precision.mean_average_precision import MeanAveragePrecision2d from numpy import argmax from sklearn.metrics import accuracy_score import time from training_utils import * from define_model import * random.seed(1) tf.disable_v2_behavior() os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true' 导入import tensorflow.compat.v1 as tf下compat红色波浪线

这个红色波浪线可能是因为您的代码编辑器无法正确识别 tensorflow.compat.v1 中的 compat 模块。但是这不会影响您的代码执行。compat 模块是用来兼容旧版 TensorFlow 的，它提供了一些兼容性的函数和类，使得...

"""Activate virtualenv for current interpreter: Use exec(open(this_file).read(), {'file': this_file}). This can be used when you must use an existing Python interpreter, not the virtualenv bin/python. """ import os import site import sys try: abs_file = os.path.abspath(file) except NameError: raise AssertionError("You must use exec(open(this_file).read(), {'file': this_file}))") bin_dir = os.path.dirname(abs_file) base = bin_dir[: -len("Scripts") - 1] # strip away the bin part from the file, plus the path separator # prepend bin to PATH (this file is inside the bin directory) os.environ["PATH"] = os.pathsep.join([bin_dir] + os.environ.get("PATH", "").split(os.pathsep)) os.environ["VIRTUAL_ENV"] = base # virtual env is right above bin directory # add the virtual environments libraries to the host python import mechanism prev_length = len(sys.path) for lib in "..\Lib\site-packages".split(os.pathsep): path = os.path.realpath(os.path.join(bin_dir, lib)) site.addsitedir(path.decode("utf-8") if "" else path) sys.path[:] = sys.path[prev_length:] + sys.path[0:prev_length] sys.real_prefix = sys.prefix sys.prefix = base

这段代码是用来激活当前解释器的虚拟环境的。当你必须使用现有的Python解释器而不是虚拟...首先，通过os模块获取当前文件的绝对路径，然后通过切片操作获取虚拟环境的根目录。最后，可以使用这个根目录来激活虚拟环境。

import numpy import numpy as np import matplotlib.pyplot as plt import math import torch from torch import nn from torch.utils.data import DataLoader, Dataset import os os.environ['KMP_DUPLICATE_LIB_OK']='True' dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) dataset = dataset.astype('float32') max_value = np.max(dataset) min_value = np.min(dataset) scalar = max_value - min_value print(scalar) dataset = list(map(lambda x: x / scalar, dataset)) def create_dataset(dataset, look_back=3): dataX, dataY = [], [] for i in range(len(dataset) - look_back): a = dataset[i:(i + look_back)] dataX.append(a) dataY.append(dataset[i + look_back]) return np.array(dataX), np.array(dataY) data_X, data_Y = create_dataset(dataset) train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] train_X = train_X.reshape(-1, 1, 3).astype('float32') train_Y = train_Y.reshape(-1, 1, 3).astype('float32') test_X = test_X.reshape(-1, 1, 3).astype('float32') train_X = torch.from_numpy(train_X) train_Y = torch.from_numpy(train_Y) test_X = torch.from_numpy(test_X) class RNN(nn.Module): def init(self, input_size, hidden_size, output_size=1, num_layer=2): super(RNN, self).init() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layer = num_layer self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): out, h = self.rnn(x) out = self.linear(out[0]) return out net = RNN(3, 20) criterion = nn.MSELoss(reduction='mean') optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) train_loss = [] test_loss = [] for e in range(1000): pred = net(train_X) loss = criterion(pred, train_Y) optimizer.zero_grad() # 反向传播 loss.backward() optimizer.step() if (e + 1) % 100 == 0: print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) train_loss.append(loss.item()) plt.plot(train_loss, label='train_loss') plt.legend() plt.show()请适当修改代码，并写出预测值和真实值的代码

os.environ['KMP_DUPLICATE_LIB_OK']='True' dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) dataset = dataset.as...

import numpy as np import matplotlib.pyplot as plt import math import torch from torch import nn import pdb from torch.autograd import Variable import os os.environ['KMP_DUPLICATE_LIB_OK']='True' dataset = [] for data in np.arange(0, 3, .01): data = math.sin(data * math.pi) dataset.append(data) dataset = np.array(dataset) dataset = dataset.astype('float32') max_value = np.max(dataset) min_value = np.min(dataset) scalar = max_value - min_value dataset = list(map(lambda x: x / scalar, dataset)) def create_dataset(dataset, look_back=3): dataX, dataY = [], [] for i in range(len(dataset) - look_back): a = dataset[i:(i + look_back)] dataX.append(a) dataY.append(dataset[i + look_back]) return np.array(dataX), np.array(dataY) data_X, data_Y = create_dataset(dataset) # 对训练集测试集划分,划分比例0.8 train_X, train_Y = data_X[:int(0.8 * len(data_X))], data_Y[:int(0.8 * len(data_Y))] test_X, test_Y = data_Y[int(0.8 * len(data_X)):], data_Y[int(0.8 * len(data_Y)):] train_X = train_X.reshape(-1, 1, 3).astype('float32') train_Y = train_Y.reshape(-1, 1, 3).astype('float32') test_X = test_X.reshape(-1, 1, 3).astype('float32') class RNN(nn.Module): def init(self, input_size, hidden_size, output_size=1, num_layer=2): super(RNN, self).init() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layer = num_layer self.rnn = nn.RNN(input_size, hidden_size, batch_first=True) self.linear = nn.Linear(hidden_size, output_size) def forward(self, x): # 补充forward函数 out, h = self.rnn(x) out = self.linear(out[0]) # print("output的形状", out.shape) return out net = RNN(3, 20) criterion = nn.MSELoss(reduction='mean') optimizer = torch.optim.Adam(net.parameters(), lr=1e-2) train_loss = [] test_loss = [] for e in range(1000): pred = net(train_X) loss = criterion(pred, train_Y) optimizer.zero_grad() # 反向传播 loss.backward() optimizer.step() if (e + 1) % 100 == 0: print('Epoch:{},loss:{:.10f}'.format(e + 1, loss.data.item())) train_loss.append(loss.item()) plt.plot(train_loss, label='train_loss') plt.legend() plt.show()画出预测值真实值图

dataY_plot = data_Y.reshape(-1) data_predict_plot = data_predict.reshape(-1) plt.plot(dataY_plot, label='real') plt.plot(data_predict_plot, label='predict') plt.legend() plt.show()

import numpy as np from osgeo import gdal from xml.dom import minidom import sys import os os.environ['PROJ_LIB'] = r"D:\test\proj.db" gdal.UseExceptions() # 引入异常处理 gdal.AllRegister() # 注册所有的驱动 def atmospheric_correction(image_path, output_path, solar_elevation, aerosol_optical_depth): # 读取遥感影像 dataset = gdal.Open(image_path, gdal.GA_ReadOnly) if dataset is None: print('Could not open %s' % image_path) return band = dataset.GetRasterBand(1) image = band.ReadAsArray().astype(np.float32) # 进行大气校正 corrected_image = (image - aerosol_optical_depth) / np.sin(np.radians(solar_elevation)) # 创建输出校正结果的影像 driver = gdal.GetDriverByName('GTiff') if driver is None: print('Could not find GTiff driver') return output_dataset = driver.Create(output_path, dataset.RasterXSize, dataset.RasterYSize, 1, gdal.GDT_Float32) if output_dataset is None: print('Could not create output dataset %s' % output_path) return output_dataset.SetProjection(dataset.GetProjection()) output_dataset.SetGeoTransform(dataset.GetGeoTransform()) # 写入校正结果 output_band = output_dataset.GetRasterBand(1) output_band.WriteArray(corrected_image) # 关闭数据集 output_band = None output_dataset = None band = None dataset = None print('Atmospheric correction completed.') if name == 'main': if len(sys.argv) == 1: workspace = r"D:\test\FLAASH_ALL_ALL_V1.0.xml" else: workspace = sys.argv[1] # 解析xml文件接口 Product = minidom.parse(workspace).documentElement # 解析xml文件(句柄或文件路径) a1 = Product.getElementsByTagName('ParaValue') # 获取输入路径的节点名 ParaValue = [] for i in a1: ParaValue.append(i.childNodes[0].data) # 获取存储路径的节点名 image_path = ParaValue[0] output_path = ParaValue[1] # image_path = r"D:\Project1\data\input\11.tif" # output_path = r"D:\test\result\2.tif" solar_elevation = 30 # 太阳高度角（单位：度） aerosol_optical_depth = 0.2 # 气溶胶光学厚度 atmospheric_correction(image_path, output_path, solar_elevation, aerosol_optical_depth) 根据这段代码写一个技术路线流程

1. 引入必要的 Python 库(numpy, osgeo, xml.dom, sys, os) 2. 设置 PROJ_LIB 环境变量，用于指定投影库的路径 3. 使用 gdal.UseExceptions() 引入异常处理 4. 使用 gdal.AllRegister() 注册所有的驱动 5. 编写 ...

seed=2 torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) torch.manual_seed(seed) torch.backends.cudnn.benchmark=False torch.backends.cudnn.deterministic=True os.environ["H5PY_DEFAULT_READONLY"] = "1"

os.environ["H5PY_DEFAULT_READONLY"] = "1" 这一行代码设置了环境变量，将HDF5库（用于处理HDF5格式数据）默认以只读模式打开。这些设置可以帮助确保在相同的随机数种子下，每次运行时生成的随机数序列和计算...

args = parser.parse_args() args.root_model = f'{args.root_path}/{args.dataset}/{args.mark}' os.makedirs(args.root_model, exist_ok=True) if args.gpu is not None: warnings.warn('You have chosen a specific GPU. This will completely ' 'disable data parallelism.') if args.dist_url == "env://" and args.world_size == -1: args.world_size = int(os.environ["WORLD_SIZE"]) args.distributed = args.world_size > 1 or args.multiprocessing_distributed

- if args.dist_url == "env://" and args.world_size == -1: ...：如果分布式训练的URL参数是默认值且world_size参数也是默认值，则尝试从环境变量中获取world_size的值。 - args.distributed = args.world_size...

def setup_seed(seed): torch.manual_seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True torch.backends.cudnn.enabled = True

2. 使用 os.environ['PYTHONHASHSEED'] = str(seed) 设置 Python 的哈希种子。 3. 使用 torch.cuda.manual_seed(seed) 设置 PyTorch CUDA 的随机种子。 4. 使用 torch.cuda.manual_seed_all(seed) 设置 ...

下面代码转化为paddle2.2.2代码：from future import division import os, time, scipy.io import torch import torch.nn as nn import torch.optim as optim import numpy as np import glob import cv2 import argparse from PIL import Image from skimage.measure import compare_psnr,compare_ssim from tensorboardX import SummaryWriter from models import RViDeNet from utils import * parser = argparse.ArgumentParser(description='Pretrain denoising model') parser.add_argument('--gpu_id', dest='gpu_id', type=int, default=0, help='gpu id') parser.add_argument('--num_epochs', dest='num_epochs', type=int, default=33, help='num_epochs') parser.add_argument('--patch_size', dest='patch_size', type=int, default=128, help='patch_size') parser.add_argument('--batch_size', dest='batch_size', type=int, default=1, help='batch_size') args = parser.parse_args() os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_id) save_dir = './pretrain_model' if not os.path.isdir(save_dir): os.makedirs(save_dir) gt_paths1 = glob.glob('./data/SRVD_data/raw_clean/MOT17-02_raw/.tiff') gt_paths2 = glob.glob('./data/SRVD_data/raw_clean/MOT17-09_raw/.tiff') gt_paths3 = glob.glob('./data/SRVD_data/raw_clean/MOT17-10_raw/.tiff') gt_paths4 = glob.glob('./data/SRVD_data/raw_clean/MOT17-11_raw/.tiff') gt_paths = gt_paths1 + gt_paths2 + gt_paths3 + gt_paths4 ps = args.patch_size # patch size for training batch_size = args.batch_size # batch size for training

import os import time import argparse import glob import cv2 import numpy as np from PIL import Image from skimage.measure import compare_psnr, compare_ssim from tensorboardX import SummaryWriter from...

帮我把下面这个代码从TensorFlow改成pytorch import tensorflow as tf import os import numpy as np import matplotlib.pyplot as plt os.environ["CUDA_VISIBLE_DEVICES"] = "0" base_dir = 'E:/direction/datasetsall/' train_dir = os.path.join(base_dir, 'train_img/') validation_dir = os.path.join(base_dir, 'val_img/') train_cats_dir = os.path.join(train_dir, 'down') train_dogs_dir = os.path.join(train_dir, 'up') validation_cats_dir = os.path.join(validation_dir, 'down') validation_dogs_dir = os.path.join(validation_dir, 'up') batch_size = 64 epochs = 50 IMG_HEIGHT = 128 IMG_WIDTH = 128 num_cats_tr = len(os.listdir(train_cats_dir)) num_dogs_tr = len(os.listdir(train_dogs_dir)) num_cats_val = len(os.listdir(validation_cats_dir)) num_dogs_val = len(os.listdir(validation_dogs_dir)) total_train = num_cats_tr + num_dogs_tr total_val = num_cats_val + num_dogs_val train_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255) validation_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255) train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size, directory=train_dir, shuffle=True, target_size=(IMG_HEIGHT, IMG_WIDTH), class_mode='categorical') val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size, directory=validation_dir, target_size=(IMG_HEIGHT, IMG_WIDTH), class_mode='categorical') sample_training_images, _ = next(train_data_gen) model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Flatten(), tf.keras.layers.Dense(256, activation='relu'), tf.keras.layers.Dense(2, activation='softmax') ]) model.compile(optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics=['accuracy']) model.summary() history = model.fit_generator( train_data_gen, steps_per_epoch=total_train // batch_size, epochs=epochs, validation_data=val_data_gen, validation_steps=total_val // batch_size ) # 可视化训练结果 acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs_range = range(epochs) model.save("./model/timo_classification_128_maxPool2D_dense256.h5")

import torch import os import numpy as np import matplotlib.pyplot as plt os.environ["CUDA_VISIBLE_DEVICES"] = "0" base_dir = 'E:/direction/datasetsall/' train_dir = os.path.join(base_dir, 'train_img/...

帮我把这段代码从tensorflow框架改成pytorch框架： import tensorflow as tf import os import numpy as np import matplotlib.pyplot as plt os.environ["CUDA_VISIBLE_DEVICES"] = "0" base_dir = 'E:/direction/datasetsall/' train_dir = os.path.join(base_dir, 'train_img/') validation_dir = os.path.join(base_dir, 'val_img/') train_cats_dir = os.path.join(train_dir, 'down') train_dogs_dir = os.path.join(train_dir, 'up') validation_cats_dir = os.path.join(validation_dir, 'down') validation_dogs_dir = os.path.join(validation_dir, 'up') batch_size = 64 epochs = 50 IMG_HEIGHT = 128 IMG_WIDTH = 128 num_cats_tr = len(os.listdir(train_cats_dir)) num_dogs_tr = len(os.listdir(train_dogs_dir)) num_cats_val = len(os.listdir(validation_cats_dir)) num_dogs_val = len(os.listdir(validation_dogs_dir)) total_train = num_cats_tr + num_dogs_tr total_val = num_cats_val + num_dogs_val train_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255) validation_image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1. / 255) train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size, directory=train_dir, shuffle=True, target_size=(IMG_HEIGHT, IMG_WIDTH), class_mode='categorical') val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size, directory=validation_dir, target_size=(IMG_HEIGHT, IMG_WIDTH), class_mode='categorical') sample_training_images, _ = next(train_data_gen) model = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Conv2D(64, 3, padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(), tf.keras.layers.Flatten(), tf.keras.layers.Dense(256, activation='relu'), tf.keras.layers.Dense(2, activation='softmax') ]) model.compile(optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics=['accuracy']) model.summary() history = model.fit_generator( train_data_gen, steps_per_epoch=total_train // batch_size, epochs=epochs, validation_data=val_data_gen, validation_steps=total_val // batch_size ) # 可视化训练结果 acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs_range = range(epochs) model.save("./model/timo_classification_128_maxPool2D_dense256.h5")

import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader from torchvision import datasets, transforms import os BATCH_SIZE = 64 EPOCHS = 50 IMG_HEIGHT = ...

from pyomo.environ import * import numpy as np import pandas as pd # 参数 filename1 = 'D:\shumo\国赛\算法\python代码实现\训练2\output3.xlsx' sheet_name1 = 'Sheet1' data1 = pd.read_excel(filename1, sheet_name=sheet_name1, header=None) num_rows1, num_cols1 = data1.shape filename2 = 'D:\shumo\国赛\算法\python代码实现\训练2\output5.xlsx' sheet_name2 = 'Sheet1' data2 = pd.read_excel(filename2, sheet_name=sheet_name2, header=None) print(data2.values[1, 0]) model = ConcreteModel() model.I = RangeSet(0, num_rows1 - 1) print(model.I[1]) model.J = RangeSet(0, num_cols1 - 1) model.x = Var(model.I, model.J, within=Integers, bounds=(0, 1)) model.c = Param(model.I, model.J, initialize=data1.values) model.ND = Param(model.J, initialize=data2.values) def obj_rule(model): return summation(model.c, model.x) # 定义目标函数 (5.1) def constrs_rule1(model, i): return sum([model.x[i, j] for j in model.J]) == 3 # 定义约束 (5.3) def constrs_rule2(model, j): return sum([model.x[i, j] for i in model.I]) == model.ND[j, 0] # 定义约束式(5.2) model.obj = Objective(rule=obj_rule, sense=minimize) model.constrs1 = Constraint(model.I, rule=constrs_rule1) model.constrs2 = Constraint(model.J, rule=constrs_rule2) opt = SolverFactory('gurobi') # 指定求解器 solution = opt.solve(model) # 调用求解器求解 solution.write() # 输出结果 x_opt = np.array([value(model.x[i, j]) for i in model.I for j in model.J]).reshape((len(model.I), len(model.J))) # 提取最优解 obj_values = value(model.obj) # 提取最优目标函数值 print("optimum point: \n {} ".format(x_opt)) print("optimal objective: {}".format(obj_values))

接着，你定义了目标函数 obj_rule 和约束条件 constrs_rule1、constrs_rule2。在设置完模型后，你使用 Gurobi 求解器进行求解，并将结果保存在 solution 对象中。然后，你提取最优解，并打印出最优解和...

数学建模学习资料神经网络算法参考资料-Matlab 共26页.pptx

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数学建模学习资料 神经网络算法 参考资料-Matlab 共26页.pptx

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