Adam(self.parameters(), lr=self.lr) return optimizer # init the autoencoder autoencoder = LitAutoEncoder(lr=1e-3, inp_size=28) # setup data batch_size = 32 dataset = MNIST(os.getcwd(), download=True, transform=ToTensor()) train_loader = utils.data.DataLoader(dataset, shuffle=True) ...
array(dataset) if __name__ == '__main__': ANNOTATIONS_PATH = "F:\Annotations" #xml文件所在文件夹 CLUSTERS = 9 #聚类数量,anchor数量 INPUTDIM = 416 #输入网络大小 data = load_dataset(ANNOTATIONS_PATH) out = kmeans(data, k=CLUSTERS) print('Boxes:') print(np.array(out)*INPUTDIM) ...
import torch from torch.utils.data import Dataset class PreferenceDataset(Dataset): def __init__(self, data, tokenizer): self.data = data # Pre-tokenize texts self.encoded_texts = [] for entry in data: prompt = format_input(entry) rejected_response = entry["rejected"] chosen_response =...
import json import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F from tqdm import tqdm from typing import List from einops import rearrange from datasets import load_dataset from torch.utils.data import Dataset, DataLoader from transformers import AutoConfi...
['train','valid']}#用datasets.ImageFolder读数据,传入路径和预处理方法,构建完数据集dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True)forxin['train','valid']}#一批一批取数据dataset_sizes = {x:len(image_datasets[x])forxin['train','valid...
import paddle import pickle as pk import numpy as np import matplotlib.pyplot as plt from model import LatentModulatedSiren # 这个就是我们的模型F_theta from utils import get_coordinate_grid %matplotlib inline /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init_...
The main parameter include test_interval, base_lr, max_iter, lr_policy, gamma, and solver_mode, and their settings are shown in Table 3. Each model is trained for 100,000 iterations based on the training set and test set images. During this process, the network parameters will be fine-...
The accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of semiarid regions. The objective of this study was to achieve the best estimation of electrical conductivity variables fro
# 需要导入模块: from utils import AttributeDict [as 别名]# 或者: from utils.AttributeDict importload_from[as 别名]p.n_iterations =3p.n_groups =4p.lr =0.001p.labeled_samples =1000p.save_freq =50p.seed =1p.num_epochs =150p.batch_size =100p.valid_batch_size =100p.objects_per_sample...
import numpy as np from keras_squeezenet import SqueezeNet from keras.applications.imagenet_utils import preprocess_input, decode_predictions from keras.preprocessing import imagemodel = SqueezeNet()img = image.load_img('pexels-photo-280207.jpeg', target_size=(227, 227))...