steffen/self-replicating-neural-networks
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fixed soup_basin experiment

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ru43zex
2021-06-05 17:44:37 +03:00
parent 0320957b85
commit 2077d800ae
6 changed files with 121 additions and 91 deletions
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13
visualization.py
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@ -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, plot_pca_together=False):
exp_name="experiment", is_trained="", batch_size=1, plot_pca_together=False, nets_array=None):
""" Plotting the the weights of the nets in a 3d form using principal component analysis (PCA) """
fig = plt.figure()
@ -134,7 +134,10 @@ def plot_3d(matrices_weights_history, directory: Union[str, Path], population_si
zdata = np.arange(start_time, len(ydata)*batch_size+start_time, batch_size)
ax.plot3D(xdata, ydata, zdata, label=f"net {i}")
ax.scatter(np.asarray(xdata), np.asarray(ydata), zdata, s=7)
if "parent" in nets_array[i].name:
ax.scatter(np.asarray(xdata), np.asarray(ydata), zdata, s=3, c="b")
else:
ax.scatter(np.asarray(xdata), np.asarray(ydata), zdata, s=3)
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")
@ -165,7 +168,7 @@ def plot_3d(matrices_weights_history, directory: Union[str, Path], population_si
else:
plt.savefig(str(filepath))
plt.show()
# plt.show()
def plot_3d_self_train(nets_array: List, exp_name: str, directory: Union[str, Path], batch_size: int, plot_pca_together: bool):
@ -177,12 +180,12 @@ def plot_3d_self_train(nets_array: List, exp_name: str, directory: Union[str, Pa
for i in loop_nets_array:
loop_nets_array.set_description("Creating ST weights history %s" % i)
matrices_weights_history.append( (nets_array[i].s_train_weights_history, nets_array[i].start_time) )
matrices_weights_history.append((nets_array[i].s_train_weights_history, nets_array[i].start_time))
z_axis_legend = "epochs"
return plot_3d(matrices_weights_history, directory, len(nets_array), z_axis_legend, exp_name, "", batch_size,
plot_pca_together=plot_pca_together)
plot_pca_together=plot_pca_together, nets_array=nets_array)
def plot_3d_self_application(nets_array: List, exp_name: str, directory_name: Union[str, Path], batch_size: int) -> None: