From 7ea8287b0ea49756a2eeec4757eb9b9deb151d11 Mon Sep 17 00:00:00 2001
From: Si11ium <steffen.illium@ifi.lmu.de>
Date: Mon, 4 Mar 2019 17:58:17 +0100
Subject: [PATCH] experimet and viz init
---
code/experiment.py | 7 ++-
code/network.py | 9 ++-
code/visualization.py | 125 ++++++++++++++++++++++++++++++++++++++++++
3 files changed, 136 insertions(+), 5 deletions(-)
create mode 100644 code/visualization.py
diff --git a/code/experiment.py b/code/experiment.py
index 0b3ef3b..b77410f 100644
--- a/code/experiment.py
+++ b/code/experiment.py
@@ -13,8 +13,8 @@ class Experiment:
def __init__(self, name=None, ident=None):
self.experiment_id = ident or time.time()
- self.experiment_name = name or 'experiment'
- self.base_dir = os.path.join('experiments', self.experiment_name)
+ self.experiment_name = name or 'unnamed_experiment'
+ self.base_dir = self.experiment_name
self.next_iteration = 0
self.log_messages = []
@@ -82,5 +82,6 @@ class FixpointExperiment(Experiment):
class SoupExperiment(Experiment):
pass
+
class IdentLearningExperiment(Experiment):
- pass
\ No newline at end of file
+ pass
diff --git a/code/network.py b/code/network.py
index 8e6b707..982ba4b 100644
--- a/code/network.py
+++ b/code/network.py
@@ -394,6 +394,11 @@ if __name__ == '__main__':
if True:
with IdentLearningExperiment() as exp:
net = LearningNeuralNetwork(width=2, depth=2, features=2, )\
- .with_keras_params(activation='linear') \
+ .with_keras_params(activation='sigmoid', use_bias=False, ) \
.with_params(print_all_weight_updates=False)
- net.learn(1000, reduction=LearningNeuralNetwork.mean_reduction)
+ net.learn(1, reduction=LearningNeuralNetwork.fft_reduction)
+ import time
+ time.sleep(1)
+ net.print_weights()
+ time.sleep(1)
+ print(net.is_fixpoint(1, epsilon=0.9e-6))
diff --git a/code/visualization.py b/code/visualization.py
new file mode 100644
index 0000000..e689185
--- /dev/null
+++ b/code/visualization.py
@@ -0,0 +1,125 @@
+import os
+import re
+from collections import defaultdict
+from tqdm import tqdm
+from argparse import ArgumentParser
+from distutils.util import strtobool
+
+import numpy as np
+import tensorflow as tf
+
+import plotly as pl
+from plotly import tools
+import plotly.graph_objs as go
+
+import dill
+
+
+def build_args():
+ arg_parser = ArgumentParser()
+ arg_parser.add_argument('-i', '--in_file', nargs=1, type=str)
+ arg_parser.add_argument('-o', '--out_file', nargs='?', default='out', type=str)
+ return arg_parser.parse_args()
+
+
+def numberFromStrings(string) -> list:
+ numberfromstring = [int(x) for x in re.findall('\d+', string)]
+ return numberfromstring
+
+
+def visulize_as_tiled_subplot(plotting_tuple, filename='plot'):
+ def norm(val, a=0, b=0.25):
+ return (val - a) / (b - a)
+
+ data = np.asarray(plotting_tuple)
+
+ fig = tools.make_subplots(rows=1, cols=3,
+ subplot_titles=('Layers: 1', 'Layers: 2', 'Layers: 3'),
+ horizontal_spacing=0.05)
+
+ for x in range(1, 4):
+ # Only select Plots with x Layers
+ scatter_slice = data[np.where(data[:, 2] == x)]
+ # Only Select Plots with x Cells
+ scatter_slice = scatter_slice[np.where(scatter_slice[:, 1] <= 10)]
+ # Normalize colors
+ colors = scatter_slice[:, 4]
+ # colors = np.apply_along_axis(norm, 0, scatter_slice[:, 4])
+ scatter = go.Scatter(x=scatter_slice[:, 3],
+ y=scatter_slice[:, 1],
+ hoverinfo='text',
+ text=['Absolute Loss:<br>{}'.format(val) for val in colors],
+ mode='markers',
+ showlegend=False,
+ marker=dict(size=10, color=colors, colorscale='Jet',
+ # Only plot the colorscale once, use one for all
+ showscale=True if x == 1 else False,
+ cmax=0.25, cmin=0,
+ colorbar=dict(y=0.5, x=1, tickmode='array', ticks='outside',
+ tickvals=[0, 0.05, 0.10, 0.15, 0.20, 0.25],
+ ticktext=["0.00", "0.05", "0.10", "0.15", "0.20", "0.25"]
+ )
+ )
+ )
+ fig.append_trace(scatter, 1, x,)
+ # TODO: Layout Loop
+ if x == 1:
+ fig['layout']['yaxis{}'.format(x)].update(tickwidth=1, title='Number of Cells')
+ if x == 2:
+ fig['layout']['xaxis{}'.format(x)].update(tickwidth=1, title='Position -X')
+
+ fig['layout'].update(title='{} - Mean Absolute Loss'.format(os.path.split('DESTINATION_OR_EXPERIMENT_NAME')[-1].upper()),
+ height=300, width=800, margin=dict(l=50, r=0, t=60, b=50))
+ # import plotly.io as pio
+ # pio.write_image(fig, filename)
+ pl.offline.plot(fig, filename=filename)
+ pass
+
+
+def visulize_as_splatter3d(plotting_tuple, filename='plot'):
+ # timesteps, cells, layers, positions, val
+ _ , cells, layers, position, val = zip(*plotting_tuple)
+ text = ['Cells: {}<br>Layers: {}<br>Position: {}<br>Mean(Min()): {}'.format(cells, layers, position, val)
+ for _, cells, layers, position, val in plotting_tuple]
+
+ data = [go.Scatter3d(x=cells, y=layers, z=position, text=text, hoverinfo='text', mode='markers',
+ marker=dict(color=val, colorscale='Jet', opacity=0.8,
+ colorbar=dict(y=0.5, x=0.9, title="Mean(Min(Seeds))"))
+ )]
+ layout = go.Layout(scene=dict(aspectratio=dict(x=2, y=2, z=1),
+ xaxis=dict(tickwidth=1, title='Number of Cells'),
+ yaxis=dict(tickwidth=1, title='Number of Layers'),
+ zaxis=dict(tickwidth=1, title='Position -pX')),
+ margin=dict(l=0, r=0, b=0, t=0))
+ fig = go.Figure(data=data, layout=layout)
+ pl.offline.plot(fig, auto_open=True, filename=filename) # filename='3d-scatter_plot'
+
+
+def compile_run_name(path: str) -> dict:
+ """
+ Retrieve all names, extract index positions and group by seeds.
+
+ :param path: Path to the current TB folder of a sinle NN configuration
+ :return: List of foldernames to filter for.
+ """
+ config_keys = ['run_seed', 'timesteps', 'index_position', 'cell_count', 'layers', 'cell_type']
+ found_configurations = defaultdict(list)
+ for dname in os.listdir(path):
+ if os.path.isdir(os.path.join(path, dname)):
+ this_config = {key: value for key, value in zip(config_keys, dname.split("_"))}
+ found_configurations[this_config['index_position']].append(dname)
+
+ return found_configurations
+
+
+
+if __name__ == '__main__':
+ raise NotImplementedError()
+ args = build_args()
+ in_file = args.in_file[0]
+ out_file = args.out_file
+
+ with open(in_file, 'rb') as dill_file:
+ experiment = dill.load(dill_file)
+
+ print('hi')