def logo1():
with viznet.DynamicShow(figsize=(4, 4), filename='_logo1.png') as ds:
n1 = viznet.NodeBrush('tn.dia', color='#CC3333') >> (0, 0)
n2 = viznet.NodeBrush('qc.cross') >> (1, 0)
n3 = viznet.NodeBrush('tn.mpo', size=0.25) >> (1, 1)
n4 = viznet.NodeBrush('nn.memory') >> (0, 1)
viznet.EdgeBrush('<.>') >> (n1, n2)
viznet.EdgeBrush('<.>') >> (n2, n3)
viznet.EdgeBrush('-<=') >> (n3, n4)
viznet.EdgeBrush('=>-') >> (n4, n1)
def cgraph_mnist_full(output='png'):
'''computation graph for simple mnist network.'''
with viznet.DynamicShow(figsize=(3, 6),
filename='_cg_mnist_full.%s' % output) as ds:
output = viznet.NodeBrush('nn.output', ds.ax)
input = viznet.NodeBrush('nn.input', ds.ax)
op = viznet.NodeBrush('basic', ds.ax)
edge = viznet.EdgeBrush('->', ds.ax)
x = input >> (0, 0)
x.text('$x$')
dot = op >> (0, -1)
dot.text('dot')
W = input >> (-1, -1)
W.text('$W$')
edge >> (x, dot)
edge >> (W, dot)
b = input >> (-1, -2)
b.text('$b$')
plus = op >> (0, -2)
plus.text('$+$')
edge >> (dot, plus)
edge >> (b, plus)
softmax = op >> (0, -3)
softmax.text(r'Softmax', 'left')
edge >> (plus, softmax)
crossentropy = op >> (0, -4)
crossentropy.text(r'CrossEntropy', 'left')
y = input >> (1, -4)
y.text(r'$y$')
edge >> (softmax, crossentropy)
edge >> (y, crossentropy)
mean = output >> (0, -5)
mean.text(r'Mean')
edge >> (crossentropy, mean)
def cgraph_layer(output='png'):
'''computation graph for a typical single layer.'''
with viznet.DynamicShow(figsize=(5, 4),
filename='_cg_layer.%s' % output) as ds:
output = viznet.NodeBrush('nn.output', ds.ax)
input = viznet.NodeBrush('nn.input', ds.ax)
op = viznet.NodeBrush('basic', ds.ax)
edge = viznet.EdgeBrush('->', ds.ax)
x = input >> (0, 0)
x.text('$x$')
dot = op >> (1, 0)
dot.text('dot')
dot.text('$f(W,x)=Wx$', 'top')
W = input >> (1, -1)
W.text('$W$')
edge >> (x, dot)
edge >> (W, dot)
b = input >> (2, -1)
b.text('$b$')
plus = op >> (2, 0)
plus.text('$+$')
edge >> (dot, plus)
edge >> (b, plus)
sigmoid = output >> (3, 0)
sigmoid.text(r'$\sigma$')
edge >> (plus, sigmoid)
def logo3():
viznet.setting.node_setting['inner_lw'] = 0
viznet.setting.node_setting['lw'] = 0
npoint = 60
nedge = 50
angle = random(npoint) * 2 * np.pi
#r = np.exp(randn(npoint)*0.4)
r = np.sqrt(randn(npoint))
xy = np.array([r * np.cos(angle), r * np.sin(angle)]).T
#xy = randn(npoint, 2)*0.5
with viznet.DynamicShow(figsize=(4, 4), filename='_logo3.png') as ds:
#body = viznet.NodeBrush('tn.mps', size='huge', color='#AACCFF') >> (0, 0)
dot = viznet.NodeBrush('tn.mps', size='tiny')
node_list = []
for i, p in enumerate(xy):
dot.color = random(3) * 0.5 + 0.5
dot.zorder = 100 + i * 2
dot.size = 0.05 + 0.08 * random()
node_list.append(dot >> p)
dis_mat = np.linalg.norm(xy - xy[:, None, :], axis=-1)
tree = minimum_spanning_tree(dis_mat).tocoo()
for i, j in zip(tree.row, tree.col):
n1, n2 = node_list[i], node_list[j]
viznet.EdgeBrush(
choice(['.>.', '.>.']),
lw=1,
color=random([3]) * 0.4,
zorder=(n1.obj.zorder + n2.obj.zorder) / 2) >> (n1, n2)
def visualize_tree(pairs, geometry, engine='viznet', offsets=None):
if len(geometry)==2:
xs, ys = np.meshgrid(np.arange(geometry[0]), np.arange(geometry[1]), indexing='ij')
num_bit = np.prod(geometry)
else:
num_bit = geometry[0]
t = np.linspace(0,2*np.pi*(num_bit-1)/num_bit,num_bit)
xs, ys = np.sqrt(num_bit)/2.5*np.cos(t), np.sqrt(num_bit)/2.5*np.sin(t)
locs = np.concatenate([xs[...,None], ys[...,None]], axis=-1).reshape([-1,2])
wl = np.log(np.array([p[2] for p in pairs]))
if offsets is None:
offsets = [(0,0)]*num_bit
if engine == 'networkx':
import networkx as nx
G = nx.Graph()
for i, loc in enumerate(locs):
plt.text(loc[0], loc[1]-0.2, '%d'%i, fontsize=18, va='center', ha='center')
G.add_node(i, loc=loc)
G.add_edges_from([p[:2] for p in pairs])
vmin = wl.min()-0.3
vmax = wl.max()+0.3
print(vmin, vmax)
nx.draw(G, pos=locs, node_color='#A0CBE2', edge_color=np.log(wl), edge_vmin=vmin, edge_vmax=vmax,
width=4, edge_cmap=plt.cm.Blues, with_labels=False, alpha=1)
elif engine == 'matplotlib':
for i, loc in enumerate(locs):
plt.text(loc[0], loc[1]-0.2, '%d'%i, fontsize=18, va='center', ha='center')
plt.scatter(xs, ys, s=200, zorder=101)
w_interval = wl.max()-wl.min()
wl/=w_interval*1.2
wl-=wl.min()-0.01
for (i, j, _), w in zip(pairs, wl):
start, end = locs[i], locs[j]
cmap = plt.get_cmap('hot')
plt.plot([start[0], end[0]], [start[1], end[1]],color=cmap(w*10))
else:
import viznet
cmap = plt.get_cmap('binary')
w_interval = wl.max()-wl.min()
wl=(wl-wl.min())/(w_interval+0.1)
print(wl)
wl+=0.1
viznet.setting.node_setting['lw']=0
node = viznet.NodeBrush('basic', size='small', color='#6699CC')
nl = []
for i, pos in enumerate(locs):
ni = node >> np.array(pos)+offsets[i]
ni.text(i)
nl.append(ni)
for (i,j,_), w in zip(pairs, wl):
edge = viznet.EdgeBrush('-', color=cmap(w), lw=2)
edge >> (nl[i], nl[j])
def logo2():
viznet.setting.node_setting['inner_lw'] = 0
viznet.setting.node_setting['lw'] = 0
with viznet.DynamicShow(figsize=(4, 4), filename='_logo2.png') as ds:
body = viznet.NodeBrush('tn.dia', size=1, color='#44CCFF') >> (0, 0)
b1 = viznet.NodeBrush('tn.mpo', size='large',
color='#5588CC') >> (0, 0)
b2 = viznet.NodeBrush('tn.dia', size='small',
color='#331133') >> (0, 0)
viznet.setting.node_setting['inner_lw'] = 2
l = 1.0
brush = viznet.NodeBrush('qc.cross', rotate=np.pi / 4.)
n1 = brush >> (l, l)
n2 = brush >> (-l, l)
n3 = brush >> (-l, -l)
n4 = brush >> (l, -l)
viznet.EdgeBrush('.>-', lw=2) >> (body, n1)
viznet.EdgeBrush('.>-', lw=2) >> (body, n2)
viznet.EdgeBrush('.>-', lw=2) >> (body, n3)
viznet.EdgeBrush('.>-', lw=2) >> (body, n4)
def cgraph_illu(output='png'):
'''computation graph for simple mnist network.'''
dx = 1
dy = 1
with viznet.DynamicShow(figsize=(3, 2),
filename='_cg_illu.%s' % output) as ds:
op = viznet.NodeBrush('basic', ds.ax)
edge = viznet.EdgeBrush('->', ds.ax)
func1 = op >> (dx, 0)
func1.text('$f$')
inv1 = viznet.edgenode.Pin([0, 0])
e1 = edge >> (inv1, func1)
e1.text('input', 'top')
func2 = op >> (0, -dy)
func2.text('$f$')
inv2 = viznet.edgenode.Pin([dx, -dy])
e2 = edge >> (func2, inv2)
e2.text('output', 'top')
def simple():
'''(x1+x2)*x3'''
num_node_visible = 6
node = viznet.NodeBrush('nn.input', size='normal')
op = viznet.NodeBrush('basic', size='small')
edge = viznet.EdgeBrush('->-')
with viznet.DynamicShow((4, 3), '_simple_cg.png') as d:
# define brushes
x1 = node >> (0, 0)
x2 = node >> (0, 1)
add = op >> (1, 0.5)
edge >> (x1, add)
edge >> (x2, add)
x3 = node >> (1, 1.5)
prod = op >> (2, 1.0)
edge >> (x3, prod)
edge >> (add, prod)
prod.text(r'$\times$')
add.text(r'$+$')
x1.text('$x_1$')
x2.text('$x_2$')
x3.text('$x_3$')
import numpy as np
import viznet
facecolor = '#BB99FF'
leg_length = 0.2
mpo_offset = 0.4
ldots_span = 1.7
dy = 0.9
edge = viznet.EdgeBrush('-')
dot = viznet.NodeBrush('pin')
hbar = viznet.NodeBrush('tn.mpo', color=facecolor); hbar.size = (0.7, 0.3)
box = viznet.NodeBrush('box', color=facecolor, size=(0.8,0.4))
mpo = viznet.NodeBrush('tn.mpo', color=facecolor)
invis = viznet.NodeBrush('invisible')
invis_mini = viznet.NodeBrush('invisible', size='small')
def leg(obj, loc, offset=(0,0)):
target = obj.pin(loc) + offset
pin = obj.pin(loc, align=target)
edge >> (pin, target)
def connect_ldots(node1, node2):
center = 0.5*(node1.center + node2.center)
inter = invis >> center.position
inter.text('$\ldots$', fontsize=18)
edge >> (node1, inter)
edge >> (inter, node2)
class Gradient():
def fig0(self, ext='pdf'):
# the matric of positions