steffen/self-replicating-neural-networks
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

PCA now fit and transform over all trajectories. Networks now have

Browse Source 
start-time properties for that to control where plotting starts. Added distance from
parent table/matrix.
This commit is contained in:
Maximilian Zorn
2021-05-21 15:28:09 +02:00
parent f5ca3d1115
commit 1e8ccd2b8b
3 changed files with 110 additions and 35 deletions
Download Patch File Download Diff File Expand all files Collapse all files

68
journal_basins.py
View File

@ -8,7 +8,7 @@ from functionalities_test import is_identity_function
from network import Net
from visualization import plot_3d_self_train, plot_loss
import numpy as np
from tabulate import tabulate
from sklearn.metrics import mean_absolute_error as MAE
from sklearn.metrics import mean_squared_error as MSE
@ -44,11 +44,37 @@ def distance_matrix(nets, distance="MIM", print_it=True):
matrix[net][other_net] = mean_invariate_manhattan_distance(weights, other_weights)
if print_it:
print(f"\nDistance matrix [{distance}]:")
[print(row) for row in matrix]
print(f"\nDistance matrix (all to all) [{distance}]:")
headers = [i.name for i in nets]
print(tabulate(matrix, showindex=headers, headers=headers, tablefmt='orgtbl'))
return matrix
def distance_from_parent(nets, distance="MIM", print_it=True):
parents = list(filter(lambda x: "clone" not in x.name and is_identity_function(x), nets))
distance_range = range(10)
for parent in parents:
parent_weights = parent.create_target_weights(parent.input_weight_matrix())
clones = list(filter(lambda y: parent.name in y.name and parent.name != y.name, nets))
matrix = [[0 for _ in distance_range] for _ in range(len(clones))]
for dist in distance_range:
for idx, clone in enumerate(clones):
clone_weights = clone.create_target_weights(clone.input_weight_matrix())
if distance in ["MSE"]:
matrix[idx][dist] = MSE(parent_weights, clone_weights) < pow(10, -dist)
elif distance in ["MAE"]:
matrix[idx][dist] = MAE(parent_weights, clone_weights) < pow(10, -dist)
elif distance in ["MIM"]:
matrix[idx][dist] = mean_invariate_manhattan_distance(parent_weights, clone_weights) < pow(10, -dist)
if print_it:
print(f"\nDistances from parent {parent.name} [{distance}]:")
col_headers = [str(f"10e-{d}") for d in distance_range]
row_headers = [str(f"clone_{i}") for i in range(len(clones))]
print(tabulate(matrix, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
return matrix
class SpawnExperiment:
@staticmethod
@ -58,7 +84,7 @@ class SpawnExperiment:
for layer_id, layer_name in enumerate(network.state_dict()):
for line_id, line_values in enumerate(network.state_dict()[layer_name]):
for weight_id, weight_value in enumerate(network.state_dict()[layer_name][line_id]):
# network.state_dict()[layer_name][line_id][weight_id] = weight_value + noise
#network.state_dict()[layer_name][line_id][weight_id] = weight_value + noise
if prng() < 0.5:
network.state_dict()[layer_name][line_id][weight_id] = weight_value + noise
else:
@ -67,7 +93,7 @@ class SpawnExperiment:
return network
def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
epochs, st_steps, noise, directory) -> None:
epochs, st_steps, nr_clones, noise, directory) -> None:
self.population_size = population_size
self.log_step_size = log_step_size
self.net_input_size = net_input_size
@ -78,6 +104,7 @@ class SpawnExperiment:
self.ST_steps = st_steps
self.loss_history = []
self.nets = []
self.nr_clones = nr_clones
self.noise = noise or 10e-5
print("\nNOISE:", self.noise)
@ -89,6 +116,7 @@ class SpawnExperiment:
self.weights_evolution_3d_experiment()
# self.visualize_loss()
distance_matrix(self.nets)
distance_from_parent(self.nets)
def populate_environment(self):
loop_population_size = tqdm(range(self.population_size))
@ -105,33 +133,40 @@ class SpawnExperiment:
# {net.input_weight_matrix()}\nLossHistory: {net.loss_history[-10:]}")
self.nets.append(net)
def spawn_and_continue(self, number_spawns: int = 5):
def spawn_and_continue(self, number_clones: int = None):
number_clones = number_clones or self.nr_clones
# For every initial net {i} after populating (that is fixpoint after first epoch);
for i in range(self.population_size):
net = self.nets[i]
# We set parent start_time to just before this epoch ended, so plotting is zoomed in. Comment out to
# to see full trajectory (but the clones will be very hard to see).
# Make one target to compare distances to clones later when they have trained.
net.start_time = self.ST_steps - 150
net_input_data = net.input_weight_matrix()
net_target_data = net.create_target_weights(net_input_data)
if is_identity_function(net):
print(f"\nNet {i} is fixpoint")
# print("\nNet weights before training\n", target_data)
# Clone the fixpoint x times and add (+-)self.noise to weight-sets randomly;
# To plot clones starting after first epoch (z=ST_steps), set that as start_time!
for j in range(number_spawns):
# To make sure PCA will plot the same trajectory up until this point, we clone the
# parent-net's weight history as well.
for j in range(number_clones):
clone = Net(net.input_size, net.hidden_size, net.out_size,
f"ST_net_{str(i)}_clone_{str(j)}",
start_time=self.ST_steps)
f"ST_net_{str(i)}_clone_{str(j)}", start_time=self.ST_steps)
clone.load_state_dict(copy.deepcopy(net.state_dict()))
rand_noise = prng() * self.noise
clone = self.apply_noise(clone, rand_noise)
clone.s_train_weights_history = copy.deepcopy(net.s_train_weights_history)
clone.number_trained = copy.deepcopy(net.number_trained)
# Then finish training each clone {j} (for remaining epoch-1 * ST_steps)
# and add to nets for plotting;
# and add to nets for plotting if they are fixpoints themselves;
for _ in range(self.epochs - 1):
for _ in range(self.ST_steps):
clone.self_train(1, self.log_step_size, self.net_learning_rate)
# print(f"clone {j} last weights: {target_data}, noise {noise}")
if is_identity_function(clone):
input_data = clone.input_weight_matrix()
target_data = clone.create_target_weights(input_data)
@ -143,7 +178,6 @@ class SpawnExperiment:
for _ in range(self.epochs - 1):
for _ in range(self.ST_steps):
net.self_train(1, self.log_step_size, self.net_learning_rate)
# print("\nNet weights after training \n", target_data)
else:
print("No fixpoints found.")
@ -167,18 +201,19 @@ if __name__ == "__main__":
# Define number of runs & name:
ST_runs = 1
ST_runs_name = "test-27"
ST_steps = 1500
ST_steps = 1700
ST_epochs = 2
ST_log_step_size = 10
# Define number of networks & their architecture
nr_clones = 5
ST_population_size = 1
ST_net_hidden_size = 2
ST_net_learning_rate = 0.04
ST_name_hash = random.getrandbits(32)
print(f"Running the Spawn experiment:")
for noise_factor in range(3, 6):
for noise_factor in range(2,3):
SpawnExperiment(
population_size=ST_population_size,
log_step_size=ST_log_step_size,
@ -188,6 +223,7 @@ if __name__ == "__main__":
net_learning_rate=ST_net_learning_rate,
epochs=ST_epochs,
st_steps=ST_steps,
nr_clones=nr_clones,
noise=pow(10, -noise_factor),
directory=Path('output') / 'spawn_basin' / f'{ST_name_hash}_10e-{noise_factor}'
)

7
requirements.txt Normal file
View File

@ -0,0 +1,7 @@
torch
tqdm
numpy==1.19.0
matplotlib
sklearn
scipy
tabulate

66
visualization.py
View File

@ -73,7 +73,7 @@ def bar_chart_fixpoints(fixpoint_counter: Dict, population_size: int, directory:
def plot_3d(matrices_weights_history, directory: Union[str, Path], population_size, z_axis_legend,
exp_name="experiment", is_trained="", batch_size=1):
exp_name="experiment", is_trained="", batch_size=1, plot_pca_together=True):
""" Plotting the the weights of the nets in a 3d form using principal component analysis (PCA) """
fig = plt.figure()
@ -83,26 +83,58 @@ def plot_3d(matrices_weights_history, directory: Union[str, Path], population_si
pca = PCA(n_components=2, whiten=True)
ax = plt.axes(projection='3d')
loop_matrices_weights_history = tqdm(range(len(matrices_weights_history)))
for i in loop_matrices_weights_history:
loop_matrices_weights_history.set_description("Plotting weights 3D PCA %s" % i)
if plot_pca_together:
weight_histories = []
start_times = []
weight_matrix, start_time = matrices_weights_history[i]
weight_matrix = np.array(weight_matrix)
n, x, y = weight_matrix.shape
weight_matrix = weight_matrix.reshape(n, x * y)
for wh, st in matrices_weights_history:
start_times.append(st)
wm = np.array(wh)
n, x, y = wm.shape
wm = wm.reshape(n, x * y)
#print(wm.shape, wm)
weight_histories.append(wm)
pca.fit(weight_matrix)
weight_matrix_pca = pca.transform(weight_matrix)
weight_data = np.array(weight_histories)
n, x, y = weight_data.shape
weight_data = weight_data.reshape(n*x, y)
xdata, ydata = [], []
for j in range(len(weight_matrix_pca)):
xdata.append(weight_matrix_pca[j][0])
ydata.append(weight_matrix_pca[j][1])
zdata = np.arange(start_time, len(ydata)*batch_size+start_time, batch_size).tolist()
pca.fit(weight_data)
weight_data_pca = pca.transform(weight_data)
ax.plot3D(xdata, ydata, zdata)
ax.scatter(np.array(xdata), np.array(ydata), np.array(zdata), s=7)
for transformed_trajectory, start_time in zip(np.split(weight_data_pca, n), start_times):
start_log_time = int(start_time / batch_size)
#print(start_time, start_log_time)
xdata = transformed_trajectory[start_log_time:, 0]
ydata = transformed_trajectory[start_log_time:, 1]
zdata = np.arange(start_time, len(ydata)*batch_size+start_time, batch_size).tolist()
ax.plot3D(xdata, ydata, zdata, label=f"net")
ax.scatter(xdata, ydata, zdata, s=7)
else:
loop_matrices_weights_history = tqdm(range(len(matrices_weights_history)))
for i in loop_matrices_weights_history:
loop_matrices_weights_history.set_description("Plotting weights 3D PCA %s" % i)
weight_matrix, start_time = matrices_weights_history[i]
weight_matrix = np.array(weight_matrix)
n, x, y = weight_matrix.shape
weight_matrix = weight_matrix.reshape(n, x * y)
pca.fit(weight_matrix)
weight_matrix_pca = pca.transform(weight_matrix)
xdata, ydata = [], []
start_log_time = int(start_time / 10)
for j in range(start_log_time, len(weight_matrix_pca)):
xdata.append(weight_matrix_pca[j][0])
ydata.append(weight_matrix_pca[j][1])
zdata = np.arange(start_time, len(ydata)*batch_size+start_time, batch_size).tolist()
ax.plot3D(xdata, ydata, zdata, label=f"net {i}")
ax.scatter(np.array(xdata), np.array(ydata), np.array(zdata), s=7)
steps = mpatches.Patch(color="white", label=f"{z_axis_legend}: {len(matrices_weights_history)} steps")
population_size = mpatches.Patch(color="white", label=f"Population: {population_size} networks")