experimet and viz init

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
+    pass

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))

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')