steffen/self-replicating-neural-networks
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Added pickle save() function for SpawnExperiment, updated README, set

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plot-pca-all false on default, just True for SpawnExp for now.
This commit is contained in:
Maximilian Zorn
2021-05-21 16:37:27 +02:00
parent 1e8ccd2b8b
commit bcfe5807a7
3 changed files with 51 additions and 41 deletions
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6
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=True):
exp_name="experiment", is_trained="", batch_size=1, plot_pca_together=False):
""" Plotting the the weights of the nets in a 3d form using principal component analysis (PCA) """
fig = plt.figure()
@ -168,7 +168,7 @@ def plot_3d(matrices_weights_history, directory: Union[str, Path], population_si
plt.show()
def plot_3d_self_train(nets_array: List, exp_name: str, directory: Union[str, Path], batch_size: int):
def plot_3d_self_train(nets_array: List, exp_name: str, directory: Union[str, Path], batch_size: int, plot_pca_together: bool):
""" Plotting the evolution of the weights in a 3D space when doing self training. """
matrices_weights_history = []
@ -181,7 +181,7 @@ def plot_3d_self_train(nets_array: List, exp_name: str, directory: Union[str, Pa
z_axis_legend = "epochs"
return plot_3d(matrices_weights_history, directory, len(nets_array), z_axis_legend, exp_name, "", batch_size)
return plot_3d(matrices_weights_history, directory, len(nets_array), z_axis_legend, exp_name, "", batch_size, plot_pca_together=plot_pca_together)
def plot_3d_self_application(nets_array: List, exp_name: str, directory_name: Union[str, Path], batch_size: int) -> None: