Trained Ternary Quantization ICLR 2017 https://github.com/TropComplique/trained-ternary-quantization...Train a model of your choice as usual (or take a trained model).
Our dream is creating a safe driving system working well under all circumstance,...
本文提出了一种训练有素、多尺度、可变形的目标检测零件模型。在2006年PASCAL人员检测挑战赛中，我们的系统在平均精度上比最佳性能提高了两倍。在2007年的挑...
参考链接 AMBERT: A Pre-trained Language Model with Multi-Grained Tokenization
GPT模型简介 GPT（Generative Pre-trained Transformer）是由OpenAI公司开发的一系列自然语言处理模型，采用多层Transformer结构来预测下一个单词的概率分布
), BertForMaskedLM - BERT Transformer with the pre-trained masked language modeling head on top (fully...pre-trained), BertForNextSentencePrediction - BERT Transformer with the pre-trained next sentence prediction...head and next sentence prediction classifier on top (fully pre-trained), BertForSequenceClassification...- BERT Transformer with a sequence classification head on top (BERT Transformer is pre-trained, the...sequence classification head is only initialized and has to be trained), BertForMultipleChoice - BERT
[PyTorch] VGG Convolutional Neural Network VGG-16 [TensorFlow 1] [PyTorch] VGG-16 Gender Classifier Trained...on MNIST [PyTorch] ResNet-18 Gender Classifier Trained on CelebA [PyTorch] ResNet-34 Digit Classifier...Trained on MNIST [PyTorch] ResNet-34 Gender Classifier Trained on CelebA [PyTorch] ResNet-50 Digit Classifier...Trained on MNIST [PyTorch] ResNet-50 Gender Classifier Trained on CelebA [PyTorch] ResNet-101 Gender...Classifier Trained on CelebA [PyTorch] ResNet-152 Gender Classifier Trained on CelebA [PyTorch] Network
on lower-cased English textbert-large-uncased24-layer, 1024-hidden, 16-heads, 340M parameters Trained...on lower-cased English textbert-base-cased12-layer, 768-hidden, 12-heads, 110M parameters Trained on...cased English textbert-large-cased24-layer, 1024-hidden, 16-heads, 340M parameters Trained on cased...the largest Wikipedias (see details)bert-base-chinese12-layer, 768-hidden, 12-heads, 110M parameters Trained...Simplified and Traditional textbert-base-german-cased12-layer, 768-hidden, 12-heads, 110M parameters Trained
on MNIST [PyTorch: GitHub | Nbviewer] ResNet-18 Gender Classifier Trained on CelebA [PyTorch:...GitHub | Nbviewer] ResNet-34 Digit Classifier Trained on MNIST [PyTorch: GitHub | Nbviewer] ResNet...Trained on CelebA [PyTorch: GitHub | Nbviewer] ResNet-50 Digit Classifier Trained on MNIST [PyTorch...-101 Gender Classifier Trained on CelebA [PyTorch: GitHub | Nbviewer] ResNet-101 Trained on CIFAR-...10 [PyTorch: GitHub | Nbviewer] ResNet-152 Gender Classifier Trained on CelebA [PyTorch: GitHub
的官方代码和预训练模型可以下载了，有没有同学准备一试： Github地址： https://github.com/google-research/bert TensorFlow code and pre-trained...If you already know what BERT is and you just want to get started, you can download the pre-trained models...Unsupervised means that BERT was trained using only a plain text corpus, which is important because an...Pre-trained representations can also either be context-free or contextual, and contextual representations...We are releasing a number of pre-trained models from the paper which were pre-trained at Google.
#Correct:1865 #Trained:2501 Training Accuracy:74.5% Progress:24.8% Speed(reviews/sec):1131....#Correct:3854 #Trained:5001 Training Accuracy:77.0% Progress:37.3% Speed(reviews/sec):1214....#Correct:5898 #Trained:7501 Training Accuracy:78.6% Progress:49.7% Speed(reviews/sec):1218....#Correct:7972 #Trained:10001 Training Accuracy:79.7% Progress:62.1% Speed(reviews/sec):1229....#Correct:16397 #Trained:20105 Training Accuracy:81.5% 进行到这，处理速度是快了，但是准确率不高。
数据的操作及下游分析 ###模型中的变量 slotNames(MOFAobject.trained) ###可视化factor结果 plot_factor_cor(MOFAobject.trained...(MOFAobject.trained, plot_total = T)[[2]] 从图中我们可以看到这些factor对数据的解释水平都超过了75%，应该很好的一个模型了。...###元数据中属性和因子之间的相关性分析，此处数据结构就是行为样本编号，列为属性值（如性别，年龄等），此处不做演示，函数如下 samples_metadata(MOFAobject.trained) <...","died","age"), plot="log_pval" ) ###绘制因子散点图 plot_factor(MOFAobject.trained, factors...####阐述单个因子对应的每个样本的贡献权重 plot_weights(MOFAobject.trained, view = "view_1",
可训练的tensor转numpy t = torch.ones(5) t_trained = t.clone().detach().requires_grad_(True) print(f"t_trained...: {t_trained}") n = t_trained.detach().numpy() print(f"n: {n}") 输出： t_trained: tensor([1., 1., 1., 1.
/datasets/download_dataset.sh ae_photos Download the pre-trained model style_cezanne: bash ....Please refer to Model Zoo for more pre-trained models. ....Model Zoo Download the pre-trained models with the following script. The model will be saved to ....on paintings and Flickr landscape photos. monet2photo (Monet paintings -> real landscape): trained on...on the Cityscapes dataset. map2sat (map -> aerial photo) and sat2map (aerial photo -> map): trained
= 2 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained...= 2 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained...= 2 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained...= 2 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained...= 1 [LightGBM] [Warning] No further splits with positive gain, best gain: -inf [LightGBM] [Debug] Trained
Matthias Müller, Alexey Dosovitskiy, Antonio López, Vladlen Koltun (Submitted on 6 Oct 2017) Deep networks trained...However, driving policies trained via imitation learning cannot be controlled at test time....A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming...in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained
= list (dict_trained.keys() )print("new_state_dict size: {} trained state_dict size: {}".format(len(...new_list),len(trained_list)) )print("New state_dict first 10th parameters names")print(new_list[:10])...print("trained state_dict first 10th parameters names")print(trained_list[:10])print(type(dict_new))print...(type(dict_trained))得到输出如下：我们截断一半之后，参数由137变成65了，前十个参数看出，名字变了但是顺序其实没变。...for i in range(65):dict_new[ new_list[i] ] = dict_trained[ trained_list[i] ]net.load_state_dict(dict_new
a high-level API to build and train models in TensorFlow, and tensorflow_hub, a library for loading trained...Use a pre-trained text embedding as the first layer, which will have three advantages: You don't have...There are many other pre-trained text embeddings from TFHub that can be used in this tutorial: google.../nnlm-en-dim128/2 - trained with the same NNLM architecture on the same data as google/nnlm-en-dim50/...This layer uses a pre-trained Saved Model to map a sentence into its embedding vector.
Second, once a model has been trained, if environment changes, which often happens in real tasks,the...Third, the trained models are usually black-boxes, whereas people usually want to know what have been...A learnware is a well-performed pre-trained machine learning model with a specification which explains...In particular, the pre-trained model should be able to be enhanced or adapted, by its new user through...Extracting symbolic rules from trained neural network ensembles.
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