ficksuppe
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		| @@ -92,5 +92,5 @@ if __name__ == '__main__': | ||||
|     in_file = args.in_file[0] | ||||
|     out_file = args.out_file | ||||
|  | ||||
|     search_and_apply(in_file, plot_bars, files_to_look_for=['all_data.dill']) | ||||
|     search_and_apply(in_file, plot_bars, files_to_look_for=['all_counters.dill']) | ||||
|     # , 'all_names.dill', 'all_notable_nets.dill']) | ||||
|   | ||||
| @@ -28,26 +28,35 @@ def line_plot(names_exp_tuple, filename='lineplot'): | ||||
|  | ||||
|     names, line_dict_list = names_exp_tuple | ||||
|  | ||||
|     names = ['Weightwise', 'Aggregating', 'Recurrent'] | ||||
|     names = ['Weightwise', 'Aggregating'] | ||||
|  | ||||
|     data = [] | ||||
|     base_scale = cl.scales['10']['div']['RdYlGn'] | ||||
|     scale = cl.interp(base_scale, len(line_dict_list) + 1)  # Map color scale to N bins | ||||
|     for ld_id, line_dict in enumerate(line_dict_list): | ||||
|         trace = go.Scatter( | ||||
|             x=line_dict['xs'], | ||||
|             y=line_dict['ys'], | ||||
|             name=names[ld_id], | ||||
|             line=dict(color=scale[ld_id], | ||||
|                       width=5), | ||||
|         ) | ||||
|         for data_point in ['ys', 'zs']: | ||||
|             trace = go.Scatter( | ||||
|                 x=line_dict['xs'], | ||||
|                 y=line_dict[data_point], | ||||
|                 name='{} {}zero-fixpoints'.format(names[ld_id], 'non-' if data_point == 'zs' else ''), | ||||
|                 line=dict( | ||||
|                     # color=scale[ld_id], | ||||
|                     width=5, | ||||
|                     # dash='dash' if data_point == 'ys' else '' | ||||
|                 ), | ||||
|             ) | ||||
|  | ||||
|         data.append(trace) | ||||
|             data.append(trace) | ||||
|  | ||||
|     layout = dict(xaxis=dict(title='Trains per self-attack', titlefont=dict(size=20)), | ||||
|                   yaxis=dict(title='Average amount of fixpoints found', titlefont=dict(size=20)), | ||||
|                   legend=dict(orientation='h', x=0.2) | ||||
|                   # height=800, width=800, margin=dict(l=0, r=0, t=0, b=0) | ||||
|     layout = dict(xaxis=dict(title='Trains per self-application', titlefont=dict(size=20)), | ||||
|                   yaxis=dict(title='Average amount of fixpoints found', | ||||
|                              titlefont=dict(size=20), | ||||
|                              # type='log', | ||||
|                              # range=[0, 2] | ||||
|                              ), | ||||
|                   legend=dict(orientation='h', x=0.3, y=-0.3), | ||||
|                   # height=800, width=800, | ||||
|                   margin=dict(b=0) | ||||
|                   ) | ||||
|  | ||||
|     fig = go.Figure(data=data, layout=layout) | ||||
|   | ||||
| @@ -32,7 +32,7 @@ def count(counters, net, notable_nets=[]): | ||||
|  | ||||
| if __name__ == '__main__': | ||||
|  | ||||
|     with Experiment('training_fixpoint') as exp: | ||||
|     with Experiment('applying_fixpoint') as exp: | ||||
|         exp.trials = 50 | ||||
|         exp.run_count = 100 | ||||
|         exp.epsilon = 1e-4 | ||||
|   | ||||
										
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							| @@ -1,8 +0,0 @@ | ||||
| TrainingNeuralNetworkDecorator activiation='linear' use_bias=False | ||||
| {'xs': [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'ys': [0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0], 'zs': [0.0, 0.0, 0.7, 1.9, 3.6, 4.3, 6.0, 6.1, 8.3, 7.7, 8.8]} | ||||
|  | ||||
|  | ||||
| TrainingNeuralNetworkDecorator activiation='linear' use_bias=False | ||||
| {'xs': [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'ys': [0.8, 0.4, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3], 'zs': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]} | ||||
|  | ||||
|  | ||||
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