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