Vision-Systems-Lab:MLP,DCNN,深度卷积自动编码器,LSTM,GRU,ResNets,DCGAN-波恩大学的CudaVision实验室(SS19)

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视觉系统实验室:在GPU上学习计算机视觉[自述文件未定期更新] 作者:Saikat Roy, 波恩大学CudaVision实验室(SS19)的存储库(主要)在PyTorch,Python3和Jupyter笔记本电脑上实现。 该项目从神经网络的基础开始,并延伸到更深层次的模型。 以下项目包含在相应的文件夹中: 项目1:Softmax回归(无autograd / Pytorch张量) 涉及使用softmax回归和手动梯度计算对MNIST数据集进行分类。 经过5次简单的迭代运行后,训练和测试集的准确度分别为0.8931和0.8866 。 项目2:多层神经网络 涉及在PyTorch上使用香草SGD进行简单的多层神经网络训练,并通过k倍蒙特卡洛交叉验证进行超参数(学习率和批量大小)搜索。 分类是在CIFAR-10数据集上完成的。 下面给出了在3072-128-128-10体系结构上进行50次

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