class args(): # training args epochs = 4 #"number of training epochs, default is 2" batch_size = 4 #"batch size for training, default is 4" dataset = "MSCOCO 2014 path" HEIGHT = 256 WIDTH = 256 save_model_dir = "models" #"path to folder where trained model will be saved." save_loss_dir = "models/loss" # "path to folder where trained model will be saved." image_size = 256 #"size of training images, default is 256 X 256" cuda = 1 #"set it to 1 for running on GPU, 0 for CPU" seed = 42 #"random seed for training" ssim_weight = [1,10,100,1000,10000] ssim_path = ['1e0', '1e1', '1e2', '1e3', '1e4'] lr = 1e-4 #"learning rate, default is 0.001" lr_light = 1e-4 # "learning rate, default is 0.001" log_interval = 5 #"number of images after which the training loss is logged, default is 500" resume = None resume_auto_en = None resume_auto_de = None resume_auto_fn = None # for test Final_cat_epoch_9_Wed_Jan__9_04_16_28_2019_1.0_1.0.model model_path_gray = "./models/densefuse_gray.model" model_path_rgb = "./models/densefuse_rgb.model"

cpp代码-C和C++奇怪内容 ## # __VA_ARGS__和... (可变参数) 宽字符

word2vec_demo:#hourofcode 使用 gensim 的 word2vec 演示

conda create -n word2vec_demo python=2.7 ipython-notebook matplotlib pandas pip seaborn gensim; source ~/anaconda/bin/activate word2vec_demo; pip install flask-restful python word2vec_convert.py ...

va_args_count:使用AC宏在C99 __VA_ARGS__宏中查找参数的数量

va_args_count 一个AC宏，用于在C99 __VA_ARGS__宏中查找参数的数量。用法直接使用VA_ARGS_COUNT()宏以int返回传递给它的参数数量。 printf("There are %i arguments.\n", VA_ARGS_COUNT(1, "two", 3.0)); 计数...

from transformers import Trainer, TrainingArguments training_args = TrainingArguments( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=64, warmup_steps=500, weight_decay=0.01, logging_dir='./logs', logging_steps=10, ) trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, ) trainer.train()

- per_device_train_batch_size：每个设备上的训练批量大小。 - per_device_eval_batch_size：每个设备上的评估批量大小。 - warmup_steps：学习率线性预热的步数。 - weight_decay：权重衰减的系数。 - logging_dir...

os.makedirs("2results", exist_ok=True) args = Seq2SeqTrainingArguments( output_dir="2results", # 模型保存路径 num_train_epochs=epochs, do_train=True, do_eval=True, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, learning_rate=learning_rate, warmup_steps=500, weight_decay=0.001, predict_with_generate=True, logging_dir="logs", logging_steps=500, evaluation_strategy="steps", save_total_limit=3, generation_max_length=max_target_length, # 生成的最大长度 generation_num_beams=1, # beam search load_best_model_at_end=True, )这里面的各个参数是什么意思

这里是对 Seq2Seq 模型训练过程中的参数进行设置，具体解释如下： - output_dir: 模型训练结果保存的路径。 - num_train_epochs: 训练的 epoch 数量，即遍历整个训练数据集的次数。 - do_train: 是否进行训练...

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

batch_size = args.batch_size # batch size for training num_epochs = args.num_epochs train_dataset = DatasetDenoising(gt_paths, ps=ps) train_loader = paddle.io.DataLoader(train_dataset, batch_size=...

if name == "main": args = parse_args() print("A list all args: \n======================") pprint(vars(args)) print() #设置 CPU 生成随机数的种子，方便下次复现实验结果。 torch.manual_seed(args.seed) np.random.seed(args.seed) #路径拼接文件路径，可以传入多个路径 PATH = os.path.join("resources", args.data) EMBEDDING_PATH = "resources/" static_feat = ["sex", "age", "pur_power"] dynamic_feat = ["category", "shop", "brand"] device = torch.device("cuda" if torch.cuda.is_available() else "cpu") n_epochs = args.n_epochs batch_size = args.batch_size lr = args.lr item_embed_size = args.embed_size feat_embed_size = args.embed_size hidden_size = (256, 128) #CosineEmbeddingLoss余弦相似度损失函数，用于判断输入的两个向量是否相似 #BCEWithLogitsLoss就是把Sigmoid-BCELoss合成一步,计算交叉损失熵 criterion = ( nn.CosineEmbeddingLoss() if args.loss == "cosine" else nn.BCEWithLogitsLoss() ) #lower将字符串中的所有大写字母转换为小写字母 criterion_type = ( "cosine" if "cosine" in criterion.class.name.lower() else "bce" ) neg_label = -1. if criterion_type == "cosine" else 0. neg_item = args.neg_item columns = ["user", "item", "label", "time", "sex", "age", "pur_power", "category", "shop", "brand"] ( n_users, n_items, train_user_consumed, eval_user_consumed, train_data, eval_data, user_map, item_map, feat_map#feature是特征比如数据集里 age, brand 之类的 ) = process_feat_data( PATH, columns, test_size=0.2, time_col="time", static_feat=static_feat, dynamic_feat=dynamic_feat ) print(f"n_users: {n_users}, n_items: {n_items}, " f"train_shape: {train_data.shape}, eval_shape: {eval_data.shape}") train_user, train_item, train_label = sample_items_random( train_data, n_items, train_user_consumed, neg_label, neg_item ) eval_user, eval_item, eval_label = sample_items_random( eval_d

这个代码段的作用是： 1. 检查当前运行的代码...2. 如果是主程序，则调用 parse_args() 函数解析命令行参数，并将结果保存在 args 变量中； 3. 打印所有解析后的参数列表，并用 pprint() 函数以易于阅读的格式输出。

val_dataset = get_segmentation_dataset(args.dataset, split='val', mode='val', **data_kwargs) args.iters_per_epoch = len(train_dataset) // (args.num_gpus * args.batch_size) args.max_iters = args.epochs * args.iters_per_epoch

接下来的代码中，通过计算训练数据集的长度(len(train_dataset))以及一些其他参数(args.num_gpus和args.batch_size)，来计算每个epoch中的迭代次数(args.iters_per_epoch)。然后，通过将每个epoch中的迭代次数(args....

请解释以下每一行代码的含义# 导入库 import argparse # 1. 定义命令行解析器对象 parser = argparse.ArgumentParser(description='Demo of argparse') # 2. 添加命令行参数 parser.add_argument('--epochs', type=int, default=30) parser.add_argument('--batch', type=int, default=4) # 3. 从命令行中结构化解析参数 args = parser.parse_args() print(args) epochs = args.epochs batch = args.batch print('show {} {}'.format(epochs, batch))

这段代码是使用Python中的argparse库来解析命令行参数的示例...- 第13和14行将args中的'epochs'和'batch'属性分别赋值给epochs和batch变量。 - 第15行使用format()方法将epochs和batch的值插入到字符串中，并打印出来。

if name == "main": parser = argparse.ArgumentParser() parser.add_argument("--epochs", type=int, default=100, help="number of epochs") parser.add_argument("--batch_size", type=int, default=4, help="size of each image batch") parser.add_argument("--gradient_accumulations", type=int, default=2, help="number of gradient accums before step") parser.add_argument("--model_def", type=str, default="config/yolov3.cfg", help="path to model definition file") parser.add_argument("--data_config", type=str, default="config/coco.data", help="path to data config file") parser.add_argument("--pretrained_weights", type=str, help="if specified starts from checkpoint model") parser.add_argument("--n_cpu", type=int, default=0, help="number of cpu threads to use during batch generation") parser.add_argument("--img_size", type=int, default=416, help="size of each image dimension") parser.add_argument("--checkpoint_interval", type=int, default=1, help="interval between saving model weights") parser.add_argument("--evaluation_interval", type=int, default=1, help="interval evaluations on validation set") parser.add_argument("--compute_map", default=False, help="if True computes mAP every tenth batch") parser.add_argument("--multiscale_training", default=True, help="allow for multi-scale training") opt = parser.parse_args() print(opt)

这段代码是一个 Python 脚本，用于训练 YOLOv3 目标检测模型。它使用 argparse 模块来解析命令行参数，并将这些参数存储在 opt 对象中。这些参数包括训练轮数、批量大小、梯度积累次数、模型定义文件路径、数据配置...

KeyError Traceback (most recent call last) Cell In[6], line 10 8 history1 = model1.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(x_test,y_test)) 9 # 再训练model2 ---> 10 history2 = model2.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(x_test,y_test)) File ~\anaconda3\lib\site-packages\keras\src\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs) 67 filtered_tb = _process_traceback_frames(e.traceback) 68 # To get the full stack trace, call: 69 # tf.debugging.disable_traceback_filtering() ---> 70 raise e.with_traceback(filtered_tb) from None 71 finally: 72 del filtered_tb File ~\AppData\Local\Temp_autograph_generated_filejh6by608.py:15, in outer_factory.<locals>.inner_factory.<locals>.tf__train_function(iterator) 13 try: 14 do_return = True ---> 15 retval = ag__.converted_call(ag__.ld(step_function), (ag.ld(self), ag.ld(iterator)), None, fscope) 16 except: 17 do_return = False KeyError: in user code: 'The optimizer cannot recognize variable dense_1/kernel:0. This usually means you are trying to call the optimizer to update different parts of the model separately. Please call optimizer.build(variables) with the full list of trainable variables before the training loop or use legacy optimizer tf.keras.optimizers.legacy.SGD.

这个错误通常是由于在训练不同的模型时，优化器无法识别变量导致的。...在你的代码中，你需要确保在训练 model2 之前，已经调用了 optimizer.build(variables) 或者使用了旧版优化器。这样可以避免出现这个错误。

def main(): global args, best_EPE #args = parser.parse_args() args = parser.parse_args(args=[]) save_path = '{},{},{}epochs{},b{},lr{}'.format( args.arch, args.solver, args.epochs, ',epochSize'+str(args.epoch_size) if args.epoch_size > 0 else '', args.batch_size, args.lr) print('=> will save everything to {}'.format(save_path)) if not os.path.exists(save_path): os.makedirs(save_path) train_writer = SummaryWriter(os.path.join(save_path,'train')) test_writer = SummaryWriter(os.path.join(save_path,'test'))

这段代码是一个 Python 函数，函数名为 main()。...同时，函数还使用全局变量 args 和 best_EPE。其中，args 是通过解析命令行参数得到的一个 argparse.Namespace 对象，best_EPE 则是一个最佳 EPE 值。

import numpy as np import pandas as pd import matplotlib as plt from model import MLP import torch.optim as optim import torch import torch.nn as nn import argparse parser = argparse.ArgumentParser() parser.add_argument('--batchSize', type=int, default=4, help='input batch size') parser.add_argument('--nEpochs', type=int, default=100, help='number of epochs to train for') parser.add_argument('--LR', type=float, default=0.001, help='learning rate for net') opt = parser.parse_args() # 数据集预处理 df = pd.read_csv("C:/Users/shen/Desktop/housing.csv", delim_whitespace=True) # Turn into Numpy arrays arr = df.to_numpy(dtype='float') # Split the features and the labels X = arr[:, :-1] y = np.expand_dims(arr[:, -1], 1) ones = np.ones((X.shape[0], 1)) X_new = np.hstack((ones, X)) # Split the dataset into train set and test set indices = np.random.permutation(X_new.shape[0]) train_indices, test_indices = indices[:int(0.9X_new.shape[0])], indices[int(0.9X_new.shape[0]):] X_train, X_test = X_new[train_indices, :], X_new[test_indices, :] y_train, y_test = y[train_indices, :], y[test_indices, :] model = MLP() device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model.to(device) model.train() optim_model = optim.Adam(model.parameters(), lr=opt.LR)#Adam优化器 # for epoch in range(opt.nEpochs): # # START CODE HERE # # END CODE HERE # torch.save(model.state_dict(),"C:/Users/shen/Desktop/checkpoint/net_weight.pth")帮我解释这里面每行代码的意思

这段代码主要是在导入一些Python的库（如numpy，pandas，...接下来使用argparse库创建了一个命令行参数解析器，可以通过命令行来指定一些参数（比如batchSize，nEpochs和LR）。最后通过parse_args()函数解析这些参数。

TypeError Traceback (most recent call last) <ipython-input-1-4fdf5de4d000> in <module> 70 # 训练模型 71 model.fit(train_x, train_y, epochs=10, batch_size=64) ---> 72 model = tf.keras.Sequential(...) 73 model.compile(...) 74 train_history = model.fit(...) D:\ProgramData\Anaconda3\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\training\checkpointable\base.py in _method_wrapper(self, *args, **kwargs) 440 self._setattr_tracking = False # pylint: disable=protected-access 441 try: --> 442 method(self, *args, **kwargs) 443 finally: 444 self._setattr_tracking = previous_value # pylint: disable=protected-access D:\ProgramData\Anaconda3\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\keras\engine\sequential.py in init(self, layers, name) 106 # Add to the model any layers passed to the constructor. 107 if layers: --> 108 for layer in layers: 109 self.add(layer) 110 TypeError: 'ellipsis' object is not iterabl

这个错误是因为在创建 tf.keras.Sequential 模型时传入了 ...，这是一个占位符对象，不是一个可迭代对象。要创建一个有效的模型，你需要传入一个层的列表作为参数，例如： python model = tf.keras....

解释import tensorflow as tf from im_dataset import train_image, train_label, test_image, test_label from AlexNet8 import AlexNet8 from baseline import baseline from InceptionNet import Inception10 from Resnet18 import ResNet18 import os import matplotlib.pyplot as plt import argparse import numpy as np parse = argparse.ArgumentParser(description="CVAE model for generation of metamaterial") hyperparameter_set = parse.add_argument_group(title='HyperParameter Setting') dim_set = parse.add_argument_group(title='Dim setting') hyperparameter_set.add_argument("--num_epochs",type=int,default=200,help="Number of train epochs") hyperparameter_set.add_argument("--learning_rate",type=float,default=4e-3,help="learning rate") hyperparameter_set.add_argument("--image_size",type=int,default=16*16,help="vector size of image") hyperparameter_set.add_argument("--batch_size",type=int,default=16,help="batch size of database") dim_set.add_argument("--z_dim",type=int,default=20,help="dim of latent variable") dim_set.add_argument("--feature_dim",type=int,default=32,help="dim of feature vector") dim_set.add_argument("--phase_curve_dim",type=int,default=41,help="dim of phase curve vector") dim_set.add_argument("--image_dim",type=int,default=16,help="image size: [image_dim,image_dim,1]") args = parse.parse_args() def preprocess(x, y): x = tf.io.read_file(x) x = tf.image.decode_png(x, channels=1) x = tf.cast(x,dtype=tf.float32) /255. x1 = tf.concat([x, x], 0) x2 = tf.concat([x1, x1], 1) x = x - 0.5 y = tf.convert_to_tensor(y) y = tf.cast(y,dtype=tf.float32) return x2, y train_db = tf.data.Dataset.from_tensor_slices((train_image, train_label)) train_db = train_db.shuffle(100).map(preprocess).batch(args.batch_size) test_db = tf.data.Dataset.from_tensor_slices((test_image, test_label)) test_db = test_db.map(preprocess).batch(args.batch_size) model = ResNet18([2, 2, 2, 2]) model.build(input_shape=(args.batch_size, 32, 32, 1)) model.compile(optimizer = tf.keras.optimizers.Adam(lr = 1e-3), loss = tf.keras.losses.MSE, metrics = ['MSE']) checkpoint_save_path = "./checkpoint/InceptionNet_im_3/checkpoint.ckpt" if os.path.exists(checkpoint_save_path+'.index'): print('------------------load the model---------------------') model.load_weights(checkpoint_save_path) cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,save_weights_only=True,save_best_only=True) history = model.fit(train_db, epochs=500, validation_data=test_db, validation_freq=1, callbacks=[cp_callback]) model.summary() acc = history.history['loss'] val_acc = history.history['val_loss'] plt.plot(acc, label='Training MSE') plt.plot(val_acc, label='Validation MSE') plt.title('Training and Validation MSE') plt.legend() plt.show()

这段代码是一个使用 ResNet18 模型进行图像分类的程序，它使用了 TensorFlow 深度学习框架，其中包含了许多参数和超参数的设置。具体来说，它的输入数据是 train_image 和 train_label，输出结果是对 test_image ...

Traceback (most recent call last): File "e:/RetinexNet_Pytorch-main/RetinexNet_Pytorch-main/train.py", line 163, in <module> main(args.epochs, args.batch_size, args.patch_size, args.lr, args.data_dir, args.ckpt_dir, args.gpu_id, args.vis_dir) File "e:/RetinexNet_Pytorch-main/RetinexNet_Pytorch-main/train.py", line 37, in main assert len(train_low_data_names) != 0 AssertionError

这个错误是由一个断言（assert）语句引起的。在您的代码中，断言确保了一个条件的真实性。如果断言的条件为 False，则会引发 AssertionError 异常。在这个特定的例子中，断言 len(train_low_data_names) !...

TAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing 怎么训练数据，给出示例代码

per_device_train_batch_size=2, per_device_eval_batch_size=2, num_train_epochs=10, weight_decay=0.01, push_to_hub=False, ) trainer = Trainer( model=task, args=training_args, train_dataset=...

feature_pyramid: true loss_args: type: AutoBCE batch_size: 8 epochs: 100 learning_rate: 0.001

深度学习train中报错IndexError: list index out of range在train_net(net_type=net_type, net=net, epochs=args.epochs, batch_size=args.batchsize, warm_epochs=args.warm_epochs, lr=args.lr, weight_decay=args.weight_decay, device=device, img_scale=scale, apex=if_apex)是因为什么

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