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# luma.neural | ||
# Luma Neural Package | ||
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Deep learning models and neural network utilities of Luma | ||
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--- | ||
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## Neural Layers | ||
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*luma.neural.layer 🔗* | ||
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### Convolution | ||
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| Class | Input Shape | Output Shape | | ||
| --- | --- | --- | | ||
| Convolution1D | $(N,C_{in},W)$ | $(N,C_{out},W)$ | | ||
| Convolution2D | $(N,C_{in},H,W)$ | $(N,C_{out},H,W)$ | | ||
| Convolution3D | $(N,C_{in},D,H,W)$ | $(N,C_{out},D,H,W)$ | | ||
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### Pooling | ||
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| Class | Input Shape | Output Shape | | ||
| --- | --- | --- | | ||
| Pooling1D | $(N,C,W_{in})$ | $(N,C,W_{in})$ | | ||
| Pooling2D | $(N,C,H_{in},W_{in})$ | $(N,C,H_{out},W_{out})$ | | ||
| Pooling3D | $(N,C,D_{in},H_{in},W_{in})$ | $(N,C,D_{out},H_{out},W_{out})$ | | ||
| GlobalAvgPooling1D | $(N,C,W)$ | $(N,C,1)$ | | ||
| GlobalAvgPooling2D | $(N,C,H,W)$ | $(N,C,1,1)$ | | ||
| GlovalAvgPooling3D | $(N,C,D,H,W)$ | $(N,C,1,1,1)$ | | ||
| AdaptiveAvgPooling1D | $(N,C,W_{in})$ | $(N,C,W_{out})$ | | ||
| AdaptiveAvgPooling2D | $(N,C,H_{in},W_{in})$ | $(N,C,H_{out},W_{out})$ | | ||
| AdaptiveAvgPooling3D | $(N,C,D_{in},H_{in},W_{in})$ | $(N,C,D_{out},H_{out},W_{out})$ | | ||
| LpPooling1D | $(N,C,W_{in})$ | $(N,C,W_{out})$ | | ||
| LpPooling2D | $(N,C,H_{in}, W_{in})$ | $(N,C,H_{out},W_{out})$ | | ||
| LpPooling3D | $(N,C,D_{in},H_{in},W_{in})$ | $(N,C,D_{out},H_{out},W_{out})$ | | ||
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### Dropout | ||
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| Class | Input Shape | Output Shape | | ||
| --- | --- | --- | | ||
| Dropout | $(*)$ | $(*)$ | | ||
| Dropout1D | $(N,C,W)$ | $(N,C,W)$ | | ||
| Dropout2D | $(N,C,H,W)$ | $(N,C,H,W)$ | | ||
| Dropout3D | $(N,C,D,H,W)$ | $(N,C,D,H,W)$ | | ||
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### Linear | ||
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| Class | Input Shape | Output Shape | | ||
| --- | --- | --- | | ||
| Flatten | $(N, *)$ | $(N, -1)$ | | ||
| Dense | $(N,L_{in})$ | $(N,L_{out})$ | | ||
| Identity | $(*)$ | $(*)$ | | ||
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### Normalization | ||
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| Class | Input Shape | Output Shape | | ||
| --- | --- | --- | | ||
| BatchNorm1D | $(N,C,W)$ | $(N,C,W)$ | | ||
| BatchNorm2D | $(N,C,H,W)$ | $(N,C,H,W)$ | | ||
| BatchNorm3D | $(N,C,D,H,W)$ | $(N,C,D,H,W)$ | | ||
| LocalResponseNorm | $(N,C,*)$ | $(N,C,*)$ | | ||
| LayerNorm | $(N,*)$ | $(N,*)$ | | ||
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--- | ||
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## Neural Blocks | ||
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*luma.neural.block 🔗* | ||
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| Class | # of Layers | Input Shape | Output Shape | | ||
| --- | --- | --- | --- | | ||
| ConvBlock1D | 2~3 | $(N,C,W_{in})$ | $(N,C,W_{out})$ | | ||
| ConvBlock2D | 2~3 | $(N,C,H_{in}, W_{in})$ | $(N,C,H_{out}, W_{out})$ | | ||
| ConvBlock3D | 2~3 | $(N,C,D_{in},H_{in},W_{in})$ | $(N,C,D_{out},H_{out},W_{out})$ | | ||
| DenseBlock | 2~3 | $(N,L_{in})$ | $(N,L_{out})$ | | ||
| IncepBlock.V1 | 19 | $(N,C_{in},H_{in},W_{in})$ | $(N,C_{out},H_{out},W_{out})$ | | ||
| IncepBlock.V2_TypeA | 22 | $(N,C_{in},H_{in},W_{in})$ | $(N,C_{out},H_{out},W_{out})$ | | ||
| IncepBlock.V2_TypeB | 31 | $(N,C_{in},H_{in},W_{in})$ | $(N,C_{out},H_{out},W_{out})$ | | ||
| IncepBlock.V2_TypeC | 28 | $(N,C_{in},H_{in},W_{in})$ | $(N,C_{out},H_{out},W_{out})$ | | ||
| IncepBlock.V2_Redux | 16 | $(N,C_{in},H_{in},W_{in})$ | $(N,C_{out},H_{out},W_{out})$ | | ||
| IncepBlock.V4_Stem | 38 | $(N,3,299,299)$ | $(N,384,35,35)$ | | ||
| IncepBlock.V4_TypeA | 24 | $(N,384,35,35)$ | $(N,384,35,35)$ | | ||
| IncepBlock.V4_TypeB | 33 | $(N,1024,17,17)$ | $(N,1024,17,17)$ | | ||
| IncepBlock.V4_TypeC | 33 | $(N,1536,8,8)$ | $(N,1536,8,8)$ | | ||
| IncepBlock.V4_ReduxA | 15 | $(N,384,35,35)$ | $(N,1024,17,17)$ | | ||
| IncepBlock.V4_ReduxB | 21 | $(N,1024,17,17)$ | $(N,1536,8,8)$ | | ||
| IncepResBlock.V1_Stem | 17 | $(N,3,299,299)$ | $(N,256,35,35)$ | | ||
| IncepResBlock.V1_TypeA | 22 | $(N,256,35,35)$ | $(N,256,35,35)$ | | ||
| IncepResBlock.V1_TypeB | 16 | $(N,896,17,17)$ | $(N,896,17,17)$ | | ||
| IncepResBlock.V1_TypeC | 16 | $(N,1792,8,8)$ | $(N,1792,8,8)$ | | ||
| IncepResBlock.V1_Redux | 24 | $(N,896,17,17)$ | $(N,1792,8,8)$ | | ||
| IncepResBlock.V2_TypeA | 22 | $(N,384,35,35)$ | $(N,384,35,35)$ | | ||
| IncepResBlock.V2_TypeB | 16 | $(N,1280,17,17)$ | $(N,1280,17,17)$ | | ||
| IncepResBlock.V2_TypeC | 16 | $(N,2272,8,8)$ | $(N,2272,8,8)$ | | ||
| IncepResBlock.V2_Redux | 24 | $(N,1280,17,17)$ | $(N,2272,8,8)$ | | ||
| ResNetBlock.Basic | 7~ | $(N,C_{in},H_{in},W_{in})$ | $(N,C_{out},H_{out},W_{out})$ | | ||
| ResNetBlock.Bottleneck | 10~ | $(N,C_{in},H_{in},W_{in})$ | $(N,C_{out},H_{out},W_{out})$ | | ||
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--- | ||
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## Neural Models | ||
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*luma.neural.model 🔗* | ||
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### LeNet Series | ||
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> LeCun, Yann, et al. "Backpropagation Applied to Handwritten Zip Code Recognition." Neural Computation, vol. 1, no. 4, 1989, pp. 541-551. | ||
> | ||
| Class | # of Layers | Input Shape | Weights | Biases | Total Param. | | ||
| --- | --- | --- | --- | --- | --- | | ||
| LeNet_1 | 6 | $(N,1,28,28)$ | 2,180 | 22 | 2,202 | | ||
| LeNet_4 | 8 | $(N,1,32,32)$ | 50,902 | 150 | 51,052 | | ||
| LeNet_5 | 10 | $(N,1,32,32)$ | 61,474 | 236 | 61,170 | | ||
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### AlexNet Series | ||
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> Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "ImageNet Classification with Deep Convolutional Neural Networks." Advances in Neural | ||
Information Processing Systems, 2012. | ||
> | ||
| Class | # of Layers | Input Shape | Weights | Biases | Total Param. | | ||
| --- | --- | --- | --- | --- | --- | | ||
| AlexNet | 21 | $(N,3,227,227)$ | 62,367,776 | 10,568 | 62,378,344 | | ||
| ZFNet | 21 | $(N,3,227,227)$ | 58,292,000 | 9,578 | 58,301,578 | | ||
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### VGGNet Series | ||
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> Simonyan, Karen, and Andrew Zisserman. "Very Deep Convolutional Networks for Large-Scale Image Recognition." arXiv preprint arXiv:1409.1556, 2014. | ||
> | ||
| Class | # of Layers | Input Shape | Weights | Biases | Total Param. | | ||
| --- | --- | --- | --- | --- | --- | | ||
| VGGNet_11 | 27 | $(N,3,224,224)$ | 132,851,392 | 11,944 | 132,863,336 | | ||
| VGGNet_13 | 31 | $(N,3,224,224)$ | 133,035,712 | 12,136 | 133,047,848 | | ||
| VGGNet_16 | 37 | $(N,3,224,224)$ | 138,344,128 | 13,416 | 138,357,544 | | ||
| VGGNet_19 | 43 | $(N,3,224,224)$ | 143,652,544 | 14,696 | 143,667,240 | | ||
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### InceptionNet Series | ||
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*InceptionNet-v1, v2, v3* | ||
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> Szegedy, Christian, et al. “Going Deeper with Convolutions.” Proceedings | ||
of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), | ||
2015, pp. 1-9. | ||
> | ||
| Class | # of Layers | Input Shape | Weights | Biases | Total Param. | | ||
| --- | --- | --- | --- | --- | --- | | ||
| InceptionNet_V1 | 182 | $(N,3,224,224)$ | 6,990,272 | 8,280 | 6,998,552 | | ||
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