cudnnConvolutionForward(cudnnHandle, &alpha, dataTensor, data, conv1filterDesc, pconv1, conv1Desc, conv1algo, workspace, m_workspaceSize, &beta, conv1Tensor, conv1); ... // 反向传播第一个卷积算子(仍需要写其他算子),如果用 AI 框架此步骤会省略 cudnnConvolutionBackwardBias(cudnnHandle, &...
cudnnConvolutionForward(cudnnHandle, &alpha, dataTensor, data, conv1filterDesc, pconv1, conv1Desc, conv1algo, workspace, m_workspaceSize, &beta, conv1Tensor, conv1); ... // 反向传播第一个卷积算子(仍需要写其他算子),如果用 AI 框架此步骤会省略 cudnnConvolutionBackwardBias(cudnnHandle, &...
cudnnConvolutionForward(cudnnHandle, &alpha, dataTensor, data, conv1filterDesc, pconv1, conv1Desc, conv1algo, workspace, m_workspaceSize, &beta, conv1Tensor, conv1); ... // 反向传播第一个卷积算子(仍需要写其他算子),如果用深度学习框架此步骤会省略 cudnnConvolutionBackwardBias(cudnnHandle, ...
...// 反向传播第一个卷积算子(仍需要写其他算子),如果用 AI 框架此步骤会省略cudnnConvolutionBackwardBias(cudnnHandle, α, conv1Tensor, dpool1, β, conv1BiasTensor, gconv1bias); cudnnConvolutionBackwardFilter(cudnnHandle, α, dataTensor, data, conv1Tensor, dpool1, conv1Desc, conv1bwfalgo, ...
conv1algo, workspace, m_workspaceSize, &beta, conv1Tensor, conv1); ... // 反向传播第一个卷积算子(仍需要写其他算子),如果用 AI 框架此步骤会省略 cudnnConvolutionBackwardBias(cudnnHandle, &alpha, conv1Tensor, dpool1, &beta, conv1BiasTensor, gconv1bias); ...
conv1algo,workspace,m_workspaceSize,β,conv1Tensor,conv1);...// 反向传播第一个卷积算子(仍需要写其他算子),如果用深度学习框架此步骤会省略,框架会通过自动求导方式补全反向传播计算逻辑cudnnConvolutionBackwardBias(cudnnHandle,α,conv1Tensor,dpool1,β,conv1BiasTensor,gconv1bias);cudnnConvolutionBackward...
in_oc_scale:输入张量的量化scale。 filter_pos:权值的量化position。 filter_scale:权值的量化scale。 compute_dtpe :输入张量的片上计算类型。 input_tensors:输入数据的描述符,个数必须是1。 ilter_tensors:权值数据的描述符,个数必须是1。 ias_tensors:bias数据的描述符,个数必须是1。
has bias:是否具有bias。 in_oc_pos:输入张量的量化position。 in_oc_scale:输入张量的量化scale。 filter_pos:权值的量化position。 filter_scale:权值的量化scale。 compute_dtpe :输入张量的片上计算类型。 input_tensors:输入数据的描述符,个数必须是1。
relevance of the input data can help identify model drift and ensure reproducibility. Both of these are are important factors when looking to implement explainable AI which is becoming increasingly important to address questions of fairness and bias, especially in financial services. Okay, this last ...
There are also ethical concerns about AI, such as privacy and security issues, as well as the potential for bias and discrimination in AI algo 有关人工智能利弊的英语作文(2) 有关人工智能利弊的英语作文(2) 人工智能利弊英语作文篇 4 The Impact of AI on Our Life In recent years, AI(...