steffen/self-replicating-neural-networks
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Added before-after-plot.

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This commit is contained in:
Maximilian Zorn
2021-06-17 15:53:48 +02:00
parent b40b534d5b
commit 1da5bd95d6
2 changed files with 11 additions and 6 deletions
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13
journal_basins.py
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@@ -146,7 +146,7 @@ class SpawnExperiment:
number_clones = number_clones or self.nr_clones
df = pd.DataFrame(
columns=['parent', 'MAE_pre', 'MAE_post', 'MSE_pre', 'MSE_post', 'MIM_pre', 'MIM_post', 'noise',
columns=['name', 'MAE_pre', 'MAE_post', 'MSE_pre', 'MSE_post', 'MIM_pre', 'MIM_post', 'noise',
'status_post'])
# For every initial net {i} after populating (that is fixpoint after first epoch);
@@ -201,8 +201,7 @@ class SpawnExperiment:
f"\nMIM({i},{j}): {MIM_post}\n")
self.nets.append(clone)
df.loc[clone.name] = [net.name, MAE_pre, MAE_post, MSE_pre, MSE_post, MIM_pre, MIM_post, self.noise,
clone.is_fixpoint]
df.loc[clone.name] = [clone.name, MAE_pre, MAE_post, MSE_pre, MSE_post, MIM_pre, MIM_post, self.noise, clone.is_fixpoint]
# Finally take parent net {i} and finish it's training for comparison to clone development.
for _ in range(self.epochs - 1):
@@ -252,7 +251,7 @@ if __name__ == "__main__":
print(f"Running the Spawn experiment:")
exp_list = []
for noise_factor in range(2, 5):
for noise_factor in range(2, 4):
exp = SpawnExperiment(
population_size=ST_population_size,
log_step_size=ST_log_step_size,
@@ -277,3 +276,9 @@ if __name__ == "__main__":
mlt = df[["MIM_pre", "MIM_post", "noise"]].melt("noise", var_name="time", value_name='Average Distance')
sns.catplot(data=mlt, x="time", y="Average Distance", col="noise", kind="point", col_wrap=5, sharey=False)
plt.savefig(f"output/spawn_basin/{ST_name_hash}/clone_distance_catplot.png")
mlt = df.melt(id_vars=["name", "noise"], value_vars=["MAE_pre", "MAE_post"], var_name="State", value_name="Distance")
ax = sns.catplot(data=mlt, x="State", y="Distance", col="noise", hue="name", kind="point", sharey=False, palette="Greens", legend=False)
ax.map(sns.boxplot, "State", "Distance", "noise", linewidth=0.8, order=["MAE_pre", "MAE_post"])
plt.savefig(f"output/spawn_basin/{ST_name_hash}/before_after_distance_catplot.png")

4
journal_soup_basins.py
View File

@@ -300,7 +300,7 @@ if __name__ == "__main__":
# Define number of runs & name:
ST_runs = 1
ST_runs_name = "test-27"
soup_ST_steps = 1500
soup_ST_steps = 2500
soup_epochs = 2
soup_log_step_size = 10
@@ -314,7 +314,7 @@ if __name__ == "__main__":
print(f"Running the Soup-Spawn experiment:")
exp_list = []
for noise_factor in range(2, 5):
for noise_factor in range(2, 3):
exp = SoupSpawnExperiment(
population_size=soup_population_size,
log_step_size=soup_log_step_size,