First neural/README.md

luma/neural/README.md

@@ -1 +1,150 @@
-# luma.neural
+# Luma Neural Package
+
+Deep learning models and neural network utilities of Luma
+
+---
+
+## Neural Layers
+
+*luma.neural.layer 🔗*
+
+### Convolution
+
+| 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 | $(N,C_{in},D,H,W)$ | $(N,C_{out},D,H,W)$ |
+
+### Pooling
+
+| 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 | $(N,C,D_{in},H_{in},W_{in})$ | $(N,C,D_{out},H_{out},W_{out})$ |
+
+### Dropout
+
+| Class | Input Shape | Output Shape |
+| --- | --- | --- |
+| Dropout | $(*)$ | $(*)$ |
+| Dropout1D | $(N,C,W)$ | $(N,C,W)$ |
+| Dropout2D | $(N,C,H,W)$ | $(N,C,H,W)$ |
+| Dropout3D | $(N,C,D,H,W)$ | $(N,C,D,H,W)$ |
+
+### Linear
+
+| Class | Input Shape | Output Shape |
+| --- | --- | --- |
+| Flatten | $(N, *)$ | $(N, -1)$ |
+| Dense | $(N,L_{in})$ | $(N,L_{out})$ |
+| Identity | $(*)$ | $(*)$ |
+
+### Normalization
+
+| 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 | $(N,*)$ | $(N,*)$ |
+
+---
+
+## Neural Blocks
+
+*luma.neural.block 🔗*
+
+| 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~ | $(N,C_{in},H_{in},W_{in})$ | $(N,C_{out},H_{out},W_{out})$ |
+
+---
+
+## Neural Models
+
+*luma.neural.model 🔗*
+
+### LeNet Series
+
+> 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 |
+
+### AlexNet Series
+
+> 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 |
+
+### VGGNet Series
+
+> 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 |
+
+### InceptionNet Series
+
+*InceptionNet-v1, v2, v3*
+
+> 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 |
+|  |  |  |  |  |  |