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plotting

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Si11ium
2019-03-06 19:21:19 +01:00
parent 6ced18c2d7
commit bae997feab
3 changed files with 69 additions and 43 deletions
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7
code/network.py
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@ -1,7 +1,6 @@
import math
import copy
import numpy as np
from tqdm import tqdm
from keras.models import Sequential
from keras.layers import SimpleRNN, Dense
@ -43,6 +42,7 @@ class NeuralNetwork(PrintingObject):
for layer_id, layer in enumerate(network_weights):
for cell_id, cell in enumerate(layer):
for weight_id, weight in enumerate(cell):
# could be a chain comparission "lower_bound <= weight <= upper_bound"
if not (lower_bound <= weight and weight <= upper_bound):
return False
return True
@ -538,6 +538,7 @@ class LearningNeuralNetwork(NeuralNetwork):
self.depth = depth
self.features = features
self.compile_params = dict(loss='mse', optimizer='sgd')
self.model = Sequential()
self.model.add(Dense(units=self.width, input_dim=self.features, **self.keras_params))
for _ in range(self.depth-1):
self.model.add(Dense(units=self.width, **self.keras_params))
@ -591,7 +592,7 @@ class TrainingNeuralNetworkDecorator():
def compile_model(self, **kwargs):
compile_params = copy.deepcopy(self.compile_params)
compile_params.update(kwargs)
return self.get_model().compile(**compile_params)
return self.net.model.compile(**compile_params)
def compiled(self, **kwargs):
if not self.model_compiled:
@ -617,7 +618,7 @@ if __name__ == '__main__':
if False:
with FixpointExperiment() as exp:
for run_id in tqdm(range(100)):
# net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')
net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')
# net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)\
# net = FFTNeuralNetwork(aggregates=4, width=2, depth=2) \
# .with_params(print_all_weight_updates=False, use_bias=False)

27
code/soup.py
View File

@ -1,9 +1,5 @@
import random
import copy
from tqdm import tqdm
from experiment import *
from network import *
@ -21,7 +17,19 @@ class Soup:
self.params = dict(attacking_rate=0.1, train_other_rate=0.1, train=0)
self.params.update(kwargs)
self.time = 0
def __copy__(self):
copy_ = Soup(self.size, self.generator, **self.params)
copy_.__dict__ = {attr: self.__dict__[attr] for attr in self.__dict__ if
attr not in ['particles', 'historical_particles']}
return copy_
def without_particles(self):
self_copy = copy.copy(self)
# self_copy.particles = [particle.states for particle in self.particles]
self_copy.historical_particles = {key: val.states for key, val in self.historical_particles.items()}
return self_copy
def with_params(self, **kwargs):
self.params.update(kwargs)
return self
@ -94,6 +102,7 @@ class Soup:
particle.print_weights()
print(particle.is_fixpoint())
class ParticleDecorator:
next_uid = 0
@ -131,7 +140,6 @@ class ParticleDecorator:
return self.states
if __name__ == '__main__':
if False:
with SoupExperiment() as exp:
@ -155,12 +163,11 @@ if __name__ == '__main__':
# net_generator = lambda: AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='sigmoid')\
# .with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
# net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
soup = Soup(10, net_generator).with_params(remove_divergent=True, remove_zero=True, train=200)
soup = Soup(10, net_generator).with_params(remove_divergent=True, remove_zero=True, train=10)
soup.seed()
for _ in tqdm(range(10)):
for _ in tqdm(range(100)):
soup.evolve()
soup.print_all()
exp.log(soup.count())
exp.save(soup=soup) # you can access soup.historical_particles[particle_uid].states[time_step]['loss']
exp.save(soup=soup.without_particles()) # you can access soup.historical_particles[particle_uid].states[time_step]['loss']
# or soup.historical_particles[particle_uid].states[time_step]['weights'] from soup.dill

78
code/visualization.py
View File

@ -1,7 +1,8 @@
import os
from typing import Union
from experiment import Experiment, SoupExperiment
from experiment import Experiment
# noinspection PyUnresolvedReferences
from soup import Soup
from argparse import ArgumentParser
import numpy as np
@ -23,29 +24,42 @@ def build_args():
return arg_parser.parse_args()
def build_from_soup(soup):
particles = soup.historical_particles
particle_dict = [dict(trajectory=[timestamp['weights'] for timestamp in particle],
fitted=[timestamp['fitted'] for timestamp in particle],
loss=[timestamp['loss'] for timestamp in particle],
time=[timestamp['time'] for timestamp in particle]) for particle in particles.values()]
return particle_dict
def plot_latent_trajectories(soup_or_experiment, filename='latent_trajectory_plot'):
assert isinstance(soup_or_experiment, Union[Experiment, SoupExperiment])
bupu = cl.scales['9']['seq']['BuPu']
data_dict = soup_or_experiment.data_storage
assert isinstance(soup_or_experiment, (Experiment, Soup))
bupu = cl.scales['11']['div']['RdYlGn']
data_dict = soup_or_experiment.data_storage if isinstance(soup_or_experiment, Experiment) \
else build_from_soup(soup_or_experiment)
scale = cl.interp(bupu, len(data_dict)+1) # Map color scale to N bins
# Fit the mebedding space
transformer = TSNE()
for trajectory_id in data_dict:
transformer.fit(np.asarray(data_dict[trajectory_id]))
for particle_dict in data_dict:
array = np.asarray([np.hstack([x.flatten() for x in timestamp]).flatten()
for timestamp in particle_dict['trajectory']])
particle_dict['trajectory'] = array
transformer.fit(array)
# Transform data accordingly and plot it
data = []
for trajectory_id in data_dict:
transformed = transformer._fit(np.asarray(data_dict[trajectory_id]))
for p_id, particle_dict in enumerate(data_dict):
transformed = transformer._fit(np.asarray(particle_dict['trajectory']))
line_trace = go.Scatter(
x=transformed[:, 0],
y=transformed[:, 1],
text='Hovertext goes here'.format(),
line=dict(color=scale[trajectory_id]),
line=dict(color=scale[p_id]),
# legendgroup='Position -{}'.format(pos),
# name='Position -{}'.format(pos),
showlegend=False,
name='Particle - {}'.format(p_id),
showlegend=True,
# hoverinfo='text',
mode='lines')
line_start = go.Scatter(mode='markers', x=[transformed[0, 0]], y=[transformed[0, 1]],
@ -73,34 +87,38 @@ def plot_latent_trajectories(soup_or_experiment, filename='latent_trajectory_plo
pass
def plot_latent_trajectories_3D(data_dict, filename='plot'):
def plot_latent_trajectories_3D(soup_or_experiment, filename='plot'):
def norm(val, a=0, b=0.25):
return (val - a) / (b - a)
bupu = cl.scales['9']['seq']['BuPu']
data_dict = soup_or_experiment.data_storage if isinstance(soup_or_experiment, Experiment) \
else build_from_soup(soup_or_experiment)
bupu = cl.scales['11']['div']['RdYlGn']
scale = cl.interp(bupu, len(data_dict)+1) # Map color scale to N bins
max_len = max([len(trajectory) for trajectory in data_dict.values()])
# Fit the mebedding space
# Fit the embedding space
transformer = TSNE()
for trajectory_id in data_dict:
transformer.fit(data_dict[trajectory_id])
for particle_dict in data_dict:
array = np.asarray([np.hstack([x.flatten() for x in timestamp]).flatten()
for timestamp in particle_dict['trajectory']])
particle_dict['trajectory'] = array
transformer.fit(array)
# Transform data accordingly and plot it
data = []
for trajectory_id in data_dict:
transformed = transformer._fit(np.asarray(data_dict[trajectory_id]))
for p_id, particle_dict in enumerate(data_dict):
transformed = transformer._fit(particle_dict['trajectory'])
trace = go.Scatter3d(
x=transformed[:, 0],
y=transformed[:, 1],
z=np.arange(transformed.shape[0]),
text='Hovertext goes here'.format(),
line=dict(color=scale[trajectory_id]),
# legendgroup='Position -{}'.format(pos),
# name='Position -{}'.format(pos),
showlegend=False,
# hoverinfo='text',
z=np.asarray(particle_dict['time']),
text='Particle: {}<br> It had {} lifes.'.format(p_id, len(particle_dict['trajectory'])),
line=dict(color=scale[p_id]),
# legendgroup='Particle - {}'.format(p_id),
name='Particle -{}'.format(p_id),
# showlegend=True,
hoverinfo='text',
mode='lines')
data.append(trace)
@ -109,7 +127,7 @@ def plot_latent_trajectories_3D(data_dict, filename='plot'):
yaxis=dict(tickwidth=1, title='transformed Y'),
zaxis=dict(tickwidth=1, title='Epoch')),
title='{} - Latent Trajectory Movement'.format('Penis'),
width=800, height=800,
# width=0, height=0,
margin=dict(l=0, r=0, b=0, t=0))
fig = go.Figure(data=data, layout=layout)
@ -213,4 +231,4 @@ if __name__ == '__main__':
in_file = args.in_file[0]
out_file = args.out_file
search_and_apply(in_file, plot_latent_trajectories)
search_and_apply(in_file, plot_latent_trajectories_3D)