| Safe Haskell | None |
|---|---|
| Language | Haskell2010 |
TensorSafe.Examples.ResNet50Example
Description
This module implements the ResNet50 model using Convs with BatchNormalization. This implementation is based on the Keras application implementation: https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet50.py
Documentation
type IdentityBlock channels kernel_size filters1 filters2 filters3 = '[Conv2D channels filters1 1 1 1 1, BatchNormalization 3 99 1, Relu, Conv2D filters1 filters2 kernel_size kernel_size 1 1, BatchNormalization 3 99 1, ZeroPadding2D 1 1, Relu, Conv2D filters2 filters3 1 1 1 1, BatchNormalization 3 99 1] Source #
type Shortcut channels stride_size filters3 = '[Conv2D channels filters3 1 1 stride_size stride_size, BatchNormalization 3 99 1] Source #
type ConvBlock channels kernel_size stride_size filters1 filters2 filters3 = '[Conv2D channels filters1 1 1 stride_size stride_size, BatchNormalization 3 99 1, Relu, Conv2D filters1 filters2 kernel_size kernel_size 1 1, ZeroPadding2D 1 1, BatchNormalization 3 99 1, Relu, Conv2D filters2 filters3 1 1 1 1, BatchNormalization 3 99 1] Source #
type ResNet50 img_size channels = MkINetwork '[Input, ZeroPadding2D 3 3, Conv2D channels 64 7 7 2 2, BatchNormalization 3 99 1, Relu, ZeroPadding2D 1 1, MaxPooling 3 3 2 2, Add (ConvBlock 64 3 1 64 64 256) (Shortcut 64 1 256), Relu, Add (IdentityBlock 256 3 64 64 256) '[Input], Relu, Add (IdentityBlock 256 3 64 64 256) '[Input], Relu, Add (ConvBlock 256 3 1 128 128 512) (Shortcut 256 1 512), Relu, Add (IdentityBlock 512 3 128 128 512) '[Input], Relu, Add (IdentityBlock 512 3 128 128 512) '[Input], Relu, Add (IdentityBlock 512 3 128 128 512) '[Input], Relu, Add (ConvBlock 512 3 1 256 256 1024) (Shortcut 512 1 1024), Relu, Add (IdentityBlock 1024 3 256 256 1024) '[Input], Relu, Add (IdentityBlock 1024 3 256 256 1024) '[Input], Relu, Add (IdentityBlock 1024 3 256 256 1024) '[Input], Relu, Add (IdentityBlock 1024 3 256 256 1024) '[Input], Relu, Add (IdentityBlock 1024 3 256 256 1024) '[Input], Relu, Add (ConvBlock 1024 3 1 512 512 2048) (Shortcut 1024 1 2048), Relu, Add (IdentityBlock 2048 3 512 512 2048) '[Input], Relu, Add (IdentityBlock 2048 3 512 512 2048) '[Input], Relu, GlobalAvgPooling2D, Dense 2048 1000] (D3 img_size img_size channels) (D1 1000) Source #