ComplexNN(Complex Neural Network Modules)是一个对基础神经网络模块进行复值化的GitHub开源项目。ComplexNN 在 PyTorch 框架下提供了复数形式的标准模块,无需任何额外的可训练参数。 该框架中各模块的参数和调用方式与PyTorch框架一致,无需额外的学习成本。 https://github.com/XinyuanLiao/ComplexNNgithub.com/Xin...
A high-level toolbox for using complex valued neural networks in PyTorch - GitHub - wavefrontshaping/complexPyTorch: A high-level toolbox for using complex valued neural networks in PyTorch
Library to help implement a complex-valued neural network (cvnn) using tensorflow as back-end pythonmachine-learningdeep-learningtensorflowmachineneural-networkscomplex-numberscomplex-networkscomplex-neural-networks UpdatedNov 21, 2024 Python Complex-valued neural networks for pytorch and Variational Dropout ...
卷积递归网络模型CRN来源文献《A convolutional recurrent neural network for real-time speech enhancement》,用于复数频谱映射(CSM),以同时估计混合语音的实部和虚部频谱图,该方法也采用与时域方法相似的CED结构,提取了高级特征,以便从嘈杂的语音频谱图中通过2维卷积更好地分离。 但是该模型的应用只是在实数神经网络。
Current Python frameworks for deep learning, such as TensorFlow, Keras, PyTorch, and scikit-learn, only solve real-domain problems, representing a considerable part of real-world applications but not all. For instance, complex-valued signals are essential for current and future technologies in ...
神经网络是在Pytorch 1.7.0平台上使用python 3.8实现的。 我们采用学习率为0.01的Adam优化器[12]来优化网络中的权值。 并在每个优化步骤中对固定输入I添加0 ~ 0.03的均匀分布噪声,以达到更好的收敛性。 在我们的网络中,图像大小设置为1024,图像传感器大小设置为1.34µm。 迭代次数为200次。 我们使用的计算机是...
The increasing interest in filter pruning of convolutional neural networks stems from its inherent ability to effectively compress and accelerate these networks. Currently, filter pruning is mainly divided into two schools: norm-based and relation-based. These methods aim to selectively remove the least...
Training settings: our network is constructed using the PyTorch framework. We employ the stochastic gradient descent (SGD) optimizer, incorporating a momentum term of 0.9 and a weight decay of 1e−4 for model training. Furthermore, we establish an initial learning rate of 0.01. The batch size...
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Our work is based on Pytorch, the model is built and trained with MMAction2. Number of blocks is set to 8. The output channels are 64-64-128-128-256-256-256-256 respectively. For Drive&Act, input is uniformly sampled into 450 frames. The training epoch is set to 90, the first five...