modules_to_save=modules_to_save, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) model.print_trainable_parameters() # Be more transparent about the % of trainable params. print(model.get_nb_trainable_parameters()) print(model.num_parameters(only_trainable=...
[64, 128, 256]] YOLOv5n summary: 214 layers, 1872157 parameters, 1872157 gradients Layer (type) Output Shape Param # === model.0.conv.weight [16, 3, 6, 6] 1728 model.0.bn.weight torch.Size([16]) 16 model.0.bn.bias torch.Size([16]) 16 model.1.conv.weight [32, 16, 3...
sess.graph, run_meta, tfprof_options=opts)withgfile.Open(outfile,'r')asf:# pylint: disable=line-too-longself.assertEqual('node name | # parameters | # float_ops | assigned devices | op types | op count (run|defined) | input shapes\n_TFProfRoot (...
It also outperforms other compact variants, where SpDenseNet-L-narrow could achieve an accuracy of 93.6% withiri: An On-device DNN-powere 9.27 K trainable parameters and 3.47 M FLOPs. Compared to the benchmark works, the accuracy on SpDenseNet-L-narrow is improved by...
I do not know if this process is still running or not. What should I do about it? Can I have my searched hyper-parameters back? (I already have the database file, but I do not know how to find the best hyperparameters based on sql operations). Do you all have any idea? Any sug...
In case any body else stumbles over this: the above code example from@vn09works good, but has a tiny bug in it. The two parameters for thelabelsandpredictionsare swapped in the method call: 'Confusion_matrix': _get_streaming_metrics(labels, predictions, [..]) ...
To resolve these problems, we propose a novel FCN model that can obtain rich and multi-scale contexts for finer segmentation and, at the same time, significantly reduce the number of trainable parameters and memory requirements. 2.1. Model Overview As illustrated in Figure 1, our proposed model...
Since the network contains many trainable parameters, the dataset containing FV and IKP images is relatively tiny. If the network parameters are randomly initialized, it is difficult for them to converge to the optimal value. Therefore, we use transfer learning to load the parameter values of the...