learning/annotated_deep_learning_paper_implementations
1
0
Fork 0
mirror of https://github.com/labmlai/annotated_deep_learning_paper_implementations.git synced 2025-11-03 22:09:18 +08:00
Code Issues Packages Projects Releases Wiki Activity

corrected some errors

Browse Source
This commit is contained in:
KeshSam
2021-02-12 11:04:41 +00:00
committed by Varuna Jayasiri
parent 56a460a243
commit 822174f6d0
1 changed files with 7 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
labml_nn/capsule_networks/__init__.py
View File

@ -3,7 +3,7 @@
title: Capsule Networks title: Capsule Networks
summary: > summary: >
PyTorch implementation and tutorial of Capsule Networks. PyTorch implementation and tutorial of Capsule Networks.
Capsule networks is neural network architecture that embeds features Capsule network is a neural network architecture that embeds features
as capsules and routes them with a voting mechanism to next layer of capsules. as capsules and routes them with a voting mechanism to next layer of capsules.
--- ---
@ -12,19 +12,19 @@ summary: >
This is a [PyTorch](https://pytorch.org) implementation/tutorial of This is a [PyTorch](https://pytorch.org) implementation/tutorial of
[Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829). [Dynamic Routing Between Capsules](https://arxiv.org/abs/1710.09829).
Capsule networks is neural network architecture that embeds features Capsule network is a neural network architecture that embeds features
as capsules and routes them with a voting mechanism to next layer of capsules. as capsules and routes them with a voting mechanism to next layer of capsules.
Unlike in other implementations of models, we've included a sample, because Unlike in other implementations of models, we've included a sample, because
it is difficult to understand some of the concepts with just the modules. it is difficult to understand some of the concepts with just the modules.
[This is the annotated code for a model that use capsules to classify MNIST dataset](mnist.html) [This is the annotated code for a model that uses capsules to classify MNIST dataset](mnist.html)
This file holds the implementations of the core modules of Capsule Networks. This file holds the implementations of the core modules of Capsule Networks.
I used [jindongwang/Pytorch-CapsuleNet](https://github.com/jindongwang/Pytorch-CapsuleNet) to clarify some I used [jindongwang/Pytorch-CapsuleNet](https://github.com/jindongwang/Pytorch-CapsuleNet) to clarify some
confusions I had with the paper. confusions I had with the paper.
Here's a notebook for training a Capsule Networks on MNIST dataset. Here's a notebook for training a Capsule Network on MNIST dataset.
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/capsule_networks/mnist.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lab-ml/nn/blob/master/labml_nn/capsule_networks/mnist.ipynb)
[![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://web.lab-ml.com/run?uuid=e7c08e08586711ebb3e30242ac1c0002) [![View Run](https://img.shields.io/badge/labml-experiment-brightgreen)](https://web.lab-ml.com/run?uuid=e7c08e08586711ebb3e30242ac1c0002)
@ -146,13 +146,13 @@ class MarginLoss(Module):
\lambda (1 - T_k) \max(0, \lVert\mathbf{v}_k\rVert - m^{-})^2$$ \lambda (1 - T_k) \max(0, \lVert\mathbf{v}_k\rVert - m^{-})^2$$
$T_k$ is $1$ if the class $k$ is present and $0$ otherwise. $T_k$ is $1$ if the class $k$ is present and $0$ otherwise.
The first component of the loss is $0$ when if the class is not present, The first component of the loss is $0$ when the class is not present,
and the second component is $0$ is the class is present. and the second component is $0$ if the class is present.
The $\max(0, x)$ is used to avoid predictions going to extremes. The $\max(0, x)$ is used to avoid predictions going to extremes.
$m^{+}$ is set to be $0.9$ and $m^{-}$ to be $0.1$ in the paper. $m^{+}$ is set to be $0.9$ and $m^{-}$ to be $0.1$ in the paper.
The $\lambda$ down-weighting is used to stop the length of all capsules from The $\lambda$ down-weighting is used to stop the length of all capsules from
fallind during the initial phase of training. falling during the initial phase of training.
""" """
def __init__(self, *, n_labels: int, lambda_: float = 0.5, m_positive: float = 0.9, m_negative: float = 0.1): def __init__(self, *, n_labels: int, lambda_: float = 0.5, m_positive: float = 0.9, m_negative: float = 0.1):
super().__init__() super().__init__()