Caffe学习3——Forward and Backward
Forward和Backward是Net中的计算本质。 让我们考虑最简单的逻辑回归分类器。 前向传播的部分是根据input来计算output，从而进行inference。在前向中，Caffe通过模型中每个layer的计算组合来计算“function”的值。这个传播从bottom到top进行。 数据x通过inner product layer来得到 g(x) g ( x ) ，在通过一个softma
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相关文章
- 1. Caffe学习：Forward and Backward
- 2. 【Caffe笔记】二.Forward and Backward(前传/反传)
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- 4. 路径规划之Backward search and forward search
- 5. saliency detection by forward and backward cues in deep-cnn
- 6. Forward and Backward Information Retention for Accurate Binary Neural Networks
- 7. Caffe学习3：Layer
- 8. Highly Efficient Forward and Backward Propagation of Convolutional Neural Networks for Pixelwise Cla
- 9. 【Paper Reading】Forward and Backward Information Retentionfor Accurate Binary Neural Networks
- 10. Feed Forward and Backward Run in Deep Convolution Neural Network 论文阅读笔记