def train_with_random(name):
"""Trains network with random values. Assumes one input module of size 2."""
def train(self, epochs=100, iterations=50):
"""Trains the network for the given amount of epochs and iterations."""
input_mdl = six.next(six.itervalues(self.inputs))
patterns = np.random.uniform(0.0, 1.0, size=(epochs, 2))
for pat in patterns:
# reset activations
for mdl in self.modules:
mdl.reset()
# set pattern
input_mdl.r = pat
# activation flow and weight update
for _ in xrange(0, iterations):
# update activations
for mdl in self.modules:
mdl.activate()
# update weights
for mdl in self.modules:
mdl.change_weights()
# swap acts
for mdl in self.modules:
mdl.swap_activations()
network = load_network(name)
network.train = types.MethodType(train, network)
return network
def train_with_random(name):
"""Trains network with random values. Assumes one input module of size 2."""
def train(self, epochs=100, iterations=50):
"""Trains the network for the given amount of epochs and iterations."""
input_mdl = six.next(six.itervalues(self.inputs))
patterns = np.random.uniform(0.0, 1.0, size=(epochs, 2))
for pat in patterns:
# reset activations
for mdl in self.modules:
mdl.reset()
# set pattern
input_mdl.r = pat
# activation flow and weight update
for _ in xrange(0, iterations):
# update activations
for mdl in self.modules:
mdl.activate()
# update weights
for mdl in self.modules:
mdl.change_weights()
# swap acts
for mdl in self.modules:
mdl.swap_activations()
network = load_network(name)
network.train = types.MethodType(train, network)
return network
def test_training(network):
"""Trains a simple network with one input module of size 2 connected
to a CALM module of size 2. See fixture for details."""
network = load_network('simple')
network.original_calm = True
for module in network.modules:
module.random_func = get_fake_random
network.train(1, 10)
weights = network.modules[0].connections[0].weights
# we use pre-determined values instead of random numbers,
# so if the outcome is different, then something in the
# algorithm has changed.
w1 = str(np.round(weights[0, 0], 8))
assert w1 == '0.60004046'
w2 = str(np.round(weights[0, 1], 8))
assert w2 == '0.60003977'
w3 = str(np.round(weights[1, 0], 8))
assert w3 == '0.60004092'
w4 = str(np.round(weights[1, 1], 8))
assert w4 == '0.60003948'
def train_with_activation_display(network_name, mdl_name):
"""Trains network while displaying node activations of given module."""
network = load_network(network_name)
for cur_mdl in network.modules:
cur_mdl.reset()
mdl = network.module_with_name(mdl_name)
# pick first input module (this code won't work with multi-input modules)
input_mdl = six.next(six.itervalues(network.inputs))
num_frames = len(network.patterns) * num_iterations * num_presentations
# set up node display
fig = plt.figure()
num_nodes = max(len(input_mdl.r), len(mdl.r)) + 1
ax = plt.axes(xlim=(0, 0.5 + num_nodes), ylim=(0, 3.5), frameon=True)
plt.tick_params(
axis='both',
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
right='off',
left='off',
labelbottom='off',
labelleft='off')
input_nodes = []
x = 0.5
for node in input_mdl.r:
patch = plt.Rectangle((x, 0), 0.5, 0.0, fc='k')
ax.add_patch(patch)
input_nodes.append(patch)
x += 1.0
r_nodes = []
x = 0.5
for node in mdl.r:
patch = plt.Rectangle((x, 1), 0.5, 0.0, fc='r')
ax.add_patch(patch)
r_nodes.append(patch)
x += 1.0
e = plt.Rectangle((x, 1), 0.5, 0.0, fc='y')
ax.add_patch(e)
v_nodes = []
x = 0.5
for node in mdl.v:
patch = plt.Rectangle((x, 2.5), 0.5, 0.0, fc='b')
ax.add_patch(patch)
v_nodes.append(patch)
x += 1.0
a = plt.Rectangle((x, 2.5), 0.5, 0.0, fc='g')
ax.add_patch(a)
def learn_animate(i):
print("animation index: {0}".format(i))
global ignore_first_animation
if ignore_first_animation:
ignore_first_animation = False
return
global current_presentation, num_iterations
if i % num_iterations == 0:
for cur_mdl in network.modules:
cur_mdl.reset()
pat = network.patterns[current_presentation]
input_mdl.r = pat[input_mdl.name]
for idx, val in enumerate(input_mdl.r):
input_nodes[idx].set_height(val / 2.0)
current_presentation += 1
if current_presentation >= len(network.patterns):
current_presentation = 0
# update activations
for cur_mdl in network.modules:
cur_mdl.activate()
# swap acts
for cur_mdl in network.modules:
cur_mdl.swap_activations()
# update weights
for cur_mdl in network.modules:
cur_mdl.change_weights()
for idx, val in enumerate(mdl.r):
r_nodes[idx].set_height(val)
for idx, val in enumerate(mdl.v):
v_nodes[idx].set_height(val)
a.set_height(mdl.a[0])
e.set_height(mdl.e[0])
anim = animation.FuncAnimation(fig, learn_animate,
frames=num_frames,
interval=20,
blit=False,
repeat=False)
anim.save("/tmp/{0}_learning.mp4".format(network.name), fps=25, extra_args=['-vcodec', 'h264', '-pix_fmt', 'yuv420p'])
def test_animate(i):
print("animation index: {0}".format(i))
global ignore_first_animation
if ignore_first_animation:
ignore_first_animation = False
return
global current_presentation, num_iterations
if i % num_iterations == 0:
for cur_mdl in network.modules:
cur_mdl.reset()
pat = network.patterns[current_presentation]
input_mdl.r = pat[input_mdl.name]
for idx, val in enumerate(input_mdl.r):
input_nodes[idx].set_height(val / 2.0)
current_presentation += 1
if current_presentation >= len(network.patterns):
current_presentation = 0
# update activations
for cur_mdl in network.modules:
cur_mdl.activate(testing=True)
# swap acts
for cur_mdl in network.modules:
cur_mdl.swap_activations()
for idx, val in enumerate(mdl.r):
r_nodes[idx].set_height(val)
for idx, val in enumerate(mdl.v):
v_nodes[idx].set_height(val)
a.set_height(mdl.a[0])
e.set_height(mdl.e[0])
global current_presentation
global ignore_first_animation
current_presentation = 0
ignore_first_animation = True
num_frames = len(network.patterns) * num_iterations
anim = animation.FuncAnimation(fig, test_animate,
frames=num_frames,
interval=20,
blit=False)
anim.save("/tmp/{0}_testing.mp4".format(network.name), fps=25, extra_args=['-vcodec', 'h264', '-pix_fmt', 'yuv420p'])
# plt.show()
return network
def train_with_activation_display(network_name, mdl_name):
"""Trains network while displaying node activations of given module."""
network = load_network(network_name)
for cur_mdl in network.modules:
cur_mdl.reset()
mdl = network.module_with_name(mdl_name)
# pick first input module (this code won't work with multi-input modules)
input_mdl = six.next(six.itervalues(network.inputs))
num_frames = len(network.patterns) * num_iterations * num_presentations
# set up node display
fig = plt.figure()
num_nodes = max(len(input_mdl.r), len(mdl.r)) + 1
ax = plt.axes(xlim=(0, 0.5 + num_nodes), ylim=(0, 3.5), frameon=True)
plt.tick_params(
axis='both',
which='both', # both major and minor ticks are affected
bottom='off', # ticks along the bottom edge are off
top='off', # ticks along the top edge are off
right='off',
left='off',
labelbottom='off',
labelleft='off')
input_nodes = []
x = 0.5
for node in input_mdl.r:
patch = plt.Rectangle((x, 0), 0.5, 0.0, fc='k')
ax.add_patch(patch)
input_nodes.append(patch)
x += 1.0
r_nodes = []
x = 0.5
for node in mdl.r:
patch = plt.Rectangle((x, 1), 0.5, 0.0, fc='r')
ax.add_patch(patch)
r_nodes.append(patch)
x += 1.0
e = plt.Rectangle((x, 1), 0.5, 0.0, fc='y')
ax.add_patch(e)
v_nodes = []
x = 0.5
for node in mdl.v:
patch = plt.Rectangle((x, 2.5), 0.5, 0.0, fc='b')
ax.add_patch(patch)
v_nodes.append(patch)
x += 1.0
a = plt.Rectangle((x, 2.5), 0.5, 0.0, fc='g')
ax.add_patch(a)
def learn_animate(i):
print("animation index: {0}".format(i))
global ignore_first_animation
if ignore_first_animation:
ignore_first_animation = False
return
global current_presentation, num_iterations
if i % num_iterations == 0:
for cur_mdl in network.modules:
cur_mdl.reset()
pat = network.patterns[current_presentation]
input_mdl.r = pat[input_mdl.name]
for idx, val in enumerate(input_mdl.r):
input_nodes[idx].set_height(val / 2.0)
current_presentation += 1
if current_presentation >= len(network.patterns):
current_presentation = 0
# update activations
for cur_mdl in network.modules:
cur_mdl.activate()
# swap acts
for cur_mdl in network.modules:
cur_mdl.swap_activations()
# update weights
for cur_mdl in network.modules:
cur_mdl.change_weights()
for idx, val in enumerate(mdl.r):
r_nodes[idx].set_height(val)
for idx, val in enumerate(mdl.v):
v_nodes[idx].set_height(val)
a.set_height(mdl.a[0])
e.set_height(mdl.e[0])
anim = animation.FuncAnimation(fig,
learn_animate,
frames=num_frames,
interval=20,
blit=False,
repeat=False)
anim.save("/tmp/{0}_learning.mp4".format(network.name),
fps=25,
extra_args=['-vcodec', 'h264', '-pix_fmt', 'yuv420p'])
def test_animate(i):
print("animation index: {0}".format(i))
global ignore_first_animation
if ignore_first_animation:
ignore_first_animation = False
return
global current_presentation, num_iterations
if i % num_iterations == 0:
for cur_mdl in network.modules:
cur_mdl.reset()
pat = network.patterns[current_presentation]
input_mdl.r = pat[input_mdl.name]
for idx, val in enumerate(input_mdl.r):
input_nodes[idx].set_height(val / 2.0)
current_presentation += 1
if current_presentation >= len(network.patterns):
current_presentation = 0
# update activations
for cur_mdl in network.modules:
cur_mdl.activate(testing=True)
# swap acts
for cur_mdl in network.modules:
cur_mdl.swap_activations()
for idx, val in enumerate(mdl.r):
r_nodes[idx].set_height(val)
for idx, val in enumerate(mdl.v):
v_nodes[idx].set_height(val)
a.set_height(mdl.a[0])
e.set_height(mdl.e[0])
global current_presentation
global ignore_first_animation
current_presentation = 0
ignore_first_animation = True
num_frames = len(network.patterns) * num_iterations
anim = animation.FuncAnimation(fig,
test_animate,
frames=num_frames,
interval=20,
blit=False)
anim.save("/tmp/{0}_testing.mp4".format(network.name),
fps=25,
extra_args=['-vcodec', 'h264', '-pix_fmt', 'yuv420p'])
# plt.show()
return network
