Note

Click here to download the full example code

# Capsule Network¶

**Author**: Jinjing Zhou, Jake Zhao, Zheng Zhang, Jinyang Li

In this tutorial, you learn how to describe one of the more classical models in terms of graphs. The approach offers a different perspective. The tutorial describes how to implement a Capsule model for the capsule network.

Warning

The tutorial aims at gaining insights into the paper, with code as a mean of explanation. The implementation thus is NOT optimized for running efficiency. For recommended implementation, please refer to the official examples.

## Key ideas of Capsule¶

The Capsule model offers two key ideas: Richer representation and dynamic routing.

**Richer representation** – In classic convolutional networks, a scalar
value represents the activation of a given feature. By contrast, a
capsule outputs a vector. The vector’s length represents the probability
of a feature being present. The vector’s orientation represents the
various properties of the feature (such as pose, deformation, texture
etc.).

**Dynamic routing** – The output of a capsule is sent to
certain parents in the layer above based on how well the capsule’s
prediction agrees with that of a parent. Such dynamic
routing-by-agreement generalizes the static routing of max-pooling.

During training, routing is accomplished iteratively. Each iteration adjusts routing weights between capsules based on their observed agreements. It’s a manner similar to a k-means algorithm or competitive learning.

In this tutorial, you see how a capsule’s dynamic routing algorithm can be naturally expressed as a graph algorithm. The implementation is adapted from Cedric Chee, replacing only the routing layer. This version achieves similar speed and accuracy.

## Model implementation¶

### Step 1: Setup and graph initialization¶

The connectivity between two layers of capsules form a directed, bipartite graph, as shown in the Figure below.

Each node \(j\) is associated with feature \(v_j\), representing its capsule’s output. Each edge is associated with features \(b_{ij}\) and \(\hat{u}_{j|i}\). \(b_{ij}\) determines routing weights, and \(\hat{u}_{j|i}\) represents the prediction of capsule \(i\) for \(j\).

Here’s how we set up the graph and initialize node and edge features.

```
import torch.nn as nn
import torch as th
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
import dgl
def init_graph(in_nodes, out_nodes, f_size):
u = np.repeat(np.arange(in_nodes), out_nodes)
v = np.tile(np.arange(in_nodes, in_nodes + out_nodes), in_nodes)
g = dgl.DGLGraph((u, v))
# init states
g.ndata['v'] = th.zeros(in_nodes + out_nodes, f_size)
g.edata['b'] = th.zeros(in_nodes * out_nodes, 1)
return g
```

### Step 2: Define message passing functions¶

This is the pseudocode for Capsule’s routing algorithm.

Implement pseudocode lines 4-7 in the class DGLRoutingLayer as the following steps:

Calculate coupling coefficients.

Coefficients are the softmax over all out-edge of in-capsules. \(\textbf{c}_{i,j} = \text{softmax}(\textbf{b}_{i,j})\).

Calculate weighted sum over all in-capsules.

Output of a capsule is equal to the weighted sum of its in-capsules \(s_j=\sum_i c_{ij}\hat{u}_{j|i}\)

Squash outputs.

Squash the length of a Capsule’s output vector to range (0,1), so it can represent the probability (of some feature being present).

\(v_j=\text{squash}(s_j)=\frac{||s_j||^2}{1+||s_j||^2}\frac{s_j}{||s_j||}\)

Update weights by the amount of agreement.

The scalar product \(\hat{u}_{j|i}\cdot v_j\) can be considered as how well capsule \(i\) agrees with \(j\). It is used to update \(b_{ij}=b_{ij}+\hat{u}_{j|i}\cdot v_j\)

```
import dgl.function as fn
class DGLRoutingLayer(nn.Module):
def __init__(self, in_nodes, out_nodes, f_size):
super(DGLRoutingLayer, self).__init__()
self.g = init_graph(in_nodes, out_nodes, f_size)
self.in_nodes = in_nodes
self.out_nodes = out_nodes
self.in_indx = list(range(in_nodes))
self.out_indx = list(range(in_nodes, in_nodes + out_nodes))
def forward(self, u_hat, routing_num=1):
self.g.edata['u_hat'] = u_hat
for r in range(routing_num):
# step 1 (line 4): normalize over out edges
edges_b = self.g.edata['b'].view(self.in_nodes, self.out_nodes)
self.g.edata['c'] = F.softmax(edges_b, dim=1).view(-1, 1)
self.g.edata['c u_hat'] = self.g.edata['c'] * self.g.edata['u_hat']
# Execute step 1 & 2
self.g.update_all(fn.copy_e('c u_hat', 'm'), fn.sum('m', 's'))
# step 3 (line 6)
self.g.nodes[self.out_indx].data['v'] = self.squash(self.g.nodes[self.out_indx].data['s'], dim=1)
# step 4 (line 7)
v = th.cat([self.g.nodes[self.out_indx].data['v']] * self.in_nodes, dim=0)
self.g.edata['b'] = self.g.edata['b'] + (self.g.edata['u_hat'] * v).sum(dim=1, keepdim=True)
@staticmethod
def squash(s, dim=1):
sq = th.sum(s ** 2, dim=dim, keepdim=True)
s_norm = th.sqrt(sq)
s = (sq / (1.0 + sq)) * (s / s_norm)
return s
```

### Step 3: Testing¶

Make a simple 20x10 capsule layer.

Out:

```
/home/ubuntu/prod-doc/readthedocs.org/user_builds/dgl/checkouts/0.9.x/python/dgl/heterograph.py:72: DGLWarning: Recommend creating graphs by `dgl.graph(data)` instead of `dgl.DGLGraph(data)`.
dgl_warning('Recommend creating graphs by `dgl.graph(data)`'
```

You can visualize a Capsule network’s behavior by monitoring the entropy of coupling coefficients. They should start high and then drop, as the weights gradually concentrate on fewer edges.

```
entropy_list = []
dist_list = []
for i in range(10):
routing(u_hat)
dist_matrix = routing.g.edata['c'].view(in_nodes, out_nodes)
entropy = (-dist_matrix * th.log(dist_matrix)).sum(dim=1)
entropy_list.append(entropy.data.numpy())
dist_list.append(dist_matrix.data.numpy())
stds = np.std(entropy_list, axis=1)
means = np.mean(entropy_list, axis=1)
plt.errorbar(np.arange(len(entropy_list)), means, stds, marker='o')
plt.ylabel("Entropy of Weight Distribution")
plt.xlabel("Number of Routing")
plt.xticks(np.arange(len(entropy_list)))
plt.close()
```

Alternatively, we can also watch the evolution of histograms.

```
import seaborn as sns
import matplotlib.animation as animation
fig = plt.figure(dpi=150)
fig.clf()
ax = fig.subplots()
def dist_animate(i):
ax.cla()
sns.distplot(dist_list[i].reshape(-1), kde=False, ax=ax)
ax.set_xlabel("Weight Distribution Histogram")
ax.set_title("Routing: %d" % (i))
ani = animation.FuncAnimation(fig, dist_animate, frames=len(entropy_list), interval=500)
plt.close()
```

You can monitor the how lower-level Capsules gradually attach to one of the higher level ones.

```
import networkx as nx
from networkx.algorithms import bipartite
g = routing.g.to_networkx()
X, Y = bipartite.sets(g)
height_in = 10
height_out = height_in * 0.8
height_in_y = np.linspace(0, height_in, in_nodes)
height_out_y = np.linspace((height_in - height_out) / 2, height_out, out_nodes)
pos = dict()
fig2 = plt.figure(figsize=(8, 3), dpi=150)
fig2.clf()
ax = fig2.subplots()
pos.update((n, (i, 1)) for i, n in zip(height_in_y, X)) # put nodes from X at x=1
pos.update((n, (i, 2)) for i, n in zip(height_out_y, Y)) # put nodes from Y at x=2
def weight_animate(i):
ax.cla()
ax.axis('off')
ax.set_title("Routing: %d " % i)
dm = dist_list[i]
nx.draw_networkx_nodes(g, pos, nodelist=range(in_nodes), node_color='r', node_size=100, ax=ax)
nx.draw_networkx_nodes(g, pos, nodelist=range(in_nodes, in_nodes + out_nodes), node_color='b', node_size=100, ax=ax)
for edge in g.edges():
nx.draw_networkx_edges(g, pos, edgelist=[edge], width=dm[edge[0], edge[1] - in_nodes] * 1.5, ax=ax)
ani2 = animation.FuncAnimation(fig2, weight_animate, frames=len(dist_list), interval=500)
plt.close()
```

The full code of this visualization is provided on GitHub. The complete code that trains on MNIST is also on GitHub.

**Total running time of the script:** ( 0 minutes 0.476 seconds)