plot-ann 1.0.8

Plot an Artificial Neural Network (ANN) model

The only import you need is the ann() function from the plot_ann module (which is the only module)

from plot_ann import ann

To draw a network with default settings just pass the dimensionality of layers as a List[int]

ax, *_ = ann([3, 5, 2])
ax.set_aspect('equal')

Figure customization

Subplotting

If no ax instance is passed, a new figure and axis will be instantiatied (fig, ax = plt.subplots()) and the ax is returned.

Alternatively, one can pass an ax:

import matplotlib.pyplot as plt
fig, [_, ax] = plt.subplots(1, 2, figsize=(8, 4))
ann([3, 5, 2], ax=ax);

Plot size

The plot proportions can be changed using the width and height paramters

fig, axes = plt.subplots(1, 2, figsize=(8, 8))
ax1, ax2 = axes
ann([3, 5, 2], ax=ax1, height=2)
ann([3, 5, 2], ax=ax2, width=3)
for ax in axes:
    ax.set_aspect('equal')

If you desire to preserve the aspect of the axis, remember to either set ax.set_aspect('equal') or manually set the right figsize. For example, default plt paramters will produce a non-square plot and affect the aspect of the network.

ann([3, 5, 2]);

Since in plt text is not affected by this distorsion, you can play with this to produce elliptical nodes with enough space to write long text inside of them

fig, ax = plt.subplots(figsize=(12,5))
ax, nodes, _ = ann([3, 5, 2], ax=ax, radius=2)
ax.text(*nodes[0][0].center, 'I\'m long text', zorder=10, va='center', ha='center');

Network Customization

The network is fairly customizable

Layers

Set the layer_labels

• layer_labels=True will set layer labels automatically
• layer_labels=List will set layer labels from the list from left to right

fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax1, ax2 = axes
ann([3, 5, 2], ax=ax1, layer_labels=True)
ann([3, 5, 2], ax=ax2, layer_labels=['Input', 'meow']);
for ax in axes:
    ax.set_aspect('equal')

Nodes

1. Change the radius parameter to customize the radius of nodes:

fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax1, ax2 = axes
ann([3, 5, 2], ax=ax1)
ann([3, 5, 2], ax=ax2, radius=3);
for ax in axes:
    ax.set_aspect('equal')

2. Change the node_lw (linewidth) of nodes:

fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax1, ax2 = axes
ann([3, 5, 2], ax=ax1)
ann([3, 5, 2], ax=ax2, node_lw=3);
for ax in axes:
    ax.set_aspect('equal')

3. Change the node_color:
   • node_colors=True will set colors automatically (C0 for input layer and C1 for everything else)
   • node_colors=List will set colors specified in the list starting from top-most input the first layer and prociding towards the bottom and then towards the right (if the list is shorter than the number of nodes default color is applied)

import matplotlib.pyplot as plt
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax1, ax2 = axes
ann([3, 5, 2], ax=ax1, node_colors=True, node_lw=2)
ann([3, 5, 2], ax=ax2, node_colors=['C0', 'C1', 'C2', 'C3', 'C4'], node_lw=2);
for ax in axes:
    ax.set_aspect('equal')

4. Set node_labels:
   • node_labels=True will set labels automatically
   • node_labels=List will set labels specified in the list starting from top-most input the first layer and prociding towards the bottom and then towards the right (if the list is shorter than the number of nodes no label is applied)

fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax1, ax2 = axes
architecture = [3, 5, 2]
ann(architecture, ax=ax1, radius=2, node_labels=True)
ann(architecture, ax=ax2, radius=2, node_labels=range(sum(architecture)-3));
for ax in axes:
    ax.set_aspect('equal')

Edges

1. Draw edges from the center of the node with edge_from_center=True or from the edge of the node with edge_from_center=False

fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax1, ax2 = axes
ann([3, 5, 2], ax=ax1, radius=3)
ann([3, 5, 2], ax=ax2, edge_from_center=False, radius=3);
for ax in axes:
    ax.set_aspect('equal')

2. Change the edge_lw (linewidth)

fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax1, ax2 = axes
ann([3, 5, 2], ax=ax1)
ann([3, 5, 2], ax=ax2, edge_lw=2);
for ax in axes:
    ax.set_aspect('equal')

3. Change the edge_colors:
   • edge_colors=True will set colors automatically (C0 for input layer and C1 for everything else)
   • edge_colors=List will set colors specified in the list starting from top-most input the first layer and prociding towards the bottom and then towards the right (if the list is shorter than the number of edges default color is applied)

import matplotlib.pyplot as plt
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax1, ax2 = axes
ann([3, 5, 2], ax=ax1, edge_colors=True, edge_lw=1)
ann([3, 5, 2], ax=ax2, edge_colors=['C0', 'C1', 'C2', 'C3', 'C4'], edge_lw=1);
for ax in axes:
    ax.set_aspect('equal')

4. Set edge_labels:
   • edge_labels=True will set labels automatically
   • edge_labels=List will set labels specified in the list starting from top-most input the first layer and prociding towards the bottom and then towards the right (if the list is shorter than the number of edges no label is applied)

import numpy as np
fig, axes = plt.subplots(1, 2, figsize=(12.5, 5))
ax1, ax2 = axes
architecture = [3, 5, 2]
ann(architecture, ax=ax1, edge_labels=True)
ann(architecture, ax=ax2, edge_labels=range(np.prod(architecture)-10));
for ax in axes:
    ax.set_aspect('equal')

5. Change edge_label_spacing:
   • edge_label_spacing=0 will have all edge labels of a layer in the center of the edge (if all labels are drawn some of them will overlap)
   • edge_label_spacing>0 will displace a label left if its rotation angle is > 0 else right

import numpy as np
fig, axes = plt.subplots(1, 2, figsize=(12.5, 5))
ax1, ax2 = axes
architecture = [3, 5, 2]
ann(architecture, ax=ax1, edge_labels=True, edge_label_spacing=0)
ann(architecture, ax=ax2, edge_labels=True, edge_label_spacing=1);
for ax in axes:
    ax.set_aspect('equal')

6. bias=True will assume that the topmost input in architecture is the bias for each layer (no edges reaching them from the previous layer) and will change the indexing for node_labels=True and edge_labels=True

fig, axes = plt.subplots(1, 2, figsize=(8, 4))
ax1, ax2 = axes
ann([3, 5, 2], ax=ax1, radius=2, bias=False, node_labels=True)
ann([3, 5, 2], ax=ax2, radius=2, bias=True, node_labels=True)
for ax in axes:
    ax.set_aspect('equal')