in between plots
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steffen-illium
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@ -6,6 +6,7 @@ import pickle
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import pandas as pd
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import numpy as np
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import torch
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from sklearn import preprocessing
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from functionalities_test import is_identity_function, test_status
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from journal_basins import SpawnExperiment, mean_invariate_manhattan_distance
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@ -21,8 +22,8 @@ class SpawnLinspaceExperiment(SpawnExperiment):
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number_clones = number_clones or self.nr_clones
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df = pd.DataFrame(
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columns=['parent', 'MAE_pre', 'MAE_post', 'MSE_pre', 'MSE_post', 'MIM_pre', 'MIM_post', 'noise',
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'status_post'])
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columns=['clone', 'parent', 'parent2', 'MAE_pre', 'MAE_post', 'MSE_pre', 'MSE_post', 'MIM_pre', 'MIM_post', 'noise',
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'status_pst'])
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# For every initial net {i} after populating (that is fixpoint after first epoch);
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# parent = self.parents[0]
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@ -31,7 +32,7 @@ class SpawnLinspaceExperiment(SpawnExperiment):
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# parent_clone.apply_weights(torch.as_tensor(parent.create_target_weights(parent.input_weight_matrix())))
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# parent_clone = parent_clone.apply_noise(self.noise)
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# self.parents.append(parent_clone)
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pairwise_net_list = itertools.combinations(self.parents, 2)
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pairwise_net_list = list(itertools.combinations(self.parents, 2))
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for net1, net2 in pairwise_net_list:
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# We set parent start_time to just before this epoch ended, so plotting is zoomed in. Comment out to
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# to see full trajectory (but the clones will be very hard to see).
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@ -50,12 +51,13 @@ class SpawnLinspaceExperiment(SpawnExperiment):
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# To plot clones starting after first epoch (z=ST_steps), set that as start_time!
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# To make sure PCA will plot the same trajectory up until this point, we clone the
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# parent-net's weight history as well.
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# in_between_weights = np.linspace(net1_target_data, net2_target_data, number_clones, endpoint=False)
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in_between_weights = np.logspace(net1_target_data, net2_target_data, number_clones, endpoint=False)
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in_between_weights = np.linspace(net1_target_data, net2_target_data, number_clones, endpoint=False)
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# in_between_weights = np.logspace(net1_target_data, net2_target_data, number_clones, endpoint=False)
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for j, in_between_weight in enumerate(in_between_weights):
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clone = Net(net1.input_size, net1.hidden_size, net1.out_size,
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name=f"{net1.name}_clone_{str(j)}", start_time=self.ST_steps + 100)
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name=f"{net1.name}_{net2.name}_clone_{str(j)}", start_time=self.ST_steps + 100)
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clone.apply_weights(torch.as_tensor(in_between_weight))
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clone.s_train_weights_history = copy.deepcopy(net1.s_train_weights_history)
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@ -67,10 +69,16 @@ class SpawnLinspaceExperiment(SpawnExperiment):
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MSE_pre = MSE(net1_target_data, clone_pre_weights)
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MIM_pre = mean_invariate_manhattan_distance(net1_target_data, clone_pre_weights)
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# Then finish training each clone {j} (for remaining epoch-1 * ST_steps) ..
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for _ in range(self.epochs - 1):
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for _ in range(self.ST_steps):
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clone.self_train(1, self.log_step_size, self.net_learning_rate)
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try:
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# Then finish training each clone {j} (for remaining epoch-1 * ST_steps) ..
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for _ in range(self.epochs - 1):
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for _ in range(self.ST_steps):
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clone.self_train(1, self.log_step_size, self.net_learning_rate)
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if any([torch.isnan(x).any() for x in clone.parameters()]):
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raise ValueError
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except ValueError:
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print("Ran into nan in 'in beetween weights' array.")
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continue
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# Post Training distances for comparison
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clone_post_weights = clone.create_target_weights(clone.input_weight_matrix())
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@ -81,15 +89,22 @@ class SpawnLinspaceExperiment(SpawnExperiment):
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# .. log to data-frame and add to nets for 3d plotting if they are fixpoints themselves.
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test_status(clone)
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if is_identity_function(clone):
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print(f"Clone {j} (of net_{net1.name}) is fixpoint."
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print(f"Clone {j} (between {net1.name} and {net2.name}) is fixpoint."
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f"\nMSE({net1.name},{j}): {MSE_post}"
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f"\nMAE({net1.name},{j}): {MAE_post}"
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f"\nMIM({net1.name},{j}): {MIM_post}\n")
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self.nets.append(clone)
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df.loc[clone.name] = [net1.name, MAE_pre, MAE_post, MSE_pre, MSE_post, MIM_pre, MIM_post,
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df.loc[len(df)] = [j, net1.name, net2.name, MAE_pre, MAE_post, MSE_pre, MSE_post, MIM_pre, MIM_post,
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self.noise, clone.is_fixpoint]
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for net1, net2 in pairwise_net_list:
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value = 'MAE'
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c_selector = [f'{value}_pre', f'{value}_post']
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values = df.loc[(df['parent'] == net1.name) & (df['parent2'] == net2.name)][c_selector]
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this_min, this_max = values.values.min(), values.values.max()
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df.loc[(df['parent'] == net1.name) &
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(df['parent2'] == net2.name), c_selector] = (values - this_min) / (this_max - this_min)
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for parent in self.parents:
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for _ in range(self.epochs - 1):
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for _ in range(self.ST_steps):
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@ -110,8 +125,8 @@ if __name__ == '__main__':
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ST_log_step_size = 10
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# Define number of networks & their architecture
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nr_clones = 100
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ST_population_size = 2
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nr_clones = 25
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ST_population_size = 10
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ST_net_hidden_size = 2
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ST_net_learning_rate = 0.04
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ST_name_hash = random.getrandbits(32)
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@ -147,26 +162,26 @@ if __name__ == '__main__':
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# Pointplot with pre and after parent Distances
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import seaborn as sns
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from matplotlib import pyplot as plt
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from matplotlib import pyplot as plt, ticker
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# ptplt = sns.pointplot(data=exp.df, x='MAE_pre', y='MAE_post', join=False)
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ptplt = sns.pointplot(data=exp.df, x='MIM_pre', y='MIM_post', join=False)
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ptplt.set(xscale='log', yscale='log')
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ptplt = sns.scatterplot(x=exp.df['MAE_pre'], y=exp.df['MAE_post'])
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# ptplt.set(xscale='log', yscale='log')
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x0, x1 = ptplt.axes.get_xlim()
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y0, y1 = ptplt.axes.get_ylim()
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lims = [max(x0, y0), min(x1, y1)]
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# This is the x=y line using transforms
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ptplt.plot(lims, lims, 'w', linestyle='dashdot', transform=ptplt.axes.transData)
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ptplt.plot([0, 1], [0, 1], ':k', transform=ptplt.axes.transAxes)
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ptplt.set(xlabel='Invariant Manhattan Distance befor Training',
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ylabel='Invariant Manhattan Distance after Training')
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plt.xticks(rotation=45)
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for ind, label in enumerate(ptplt.get_xticklabels()):
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if ind % 10 == 0: # every 10th label is kept
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label.set_visible(True)
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label.set_text(round(float(label.get_text()), 3))
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else:
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label.set_visible(False)
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ptplt.set(xlabel='Mean Absolute Distance before Self-Training',
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ylabel='Mean Absolute Distance after Self-Training')
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# ptplt.axes.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: round(float(x), 2)))
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# ptplt.xticks(rotation=45)
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#for ind, label in enumerate(ptplt.get_xticklabels()):
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# if ind % 10 == 0: # every 10th label is kept
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# label.set_visible(True)
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# else:
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# label.set_visible(False)
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filepath = exp.directory / 'mim_dist_plot.png'
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plt.tight_layout()
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