deleted all filed from the old code

code/setups/applying-fixpoints.py

@@ -1,70 +0,0 @@
-import sys
-import os
-
-# Concat top Level dir to system environmental variables
-sys.path += os.path.join('..', '.')
-
-from util import *
-from experiment import *
-from network import *
-
-import keras.backend as K
-
-def generate_counters():
-    return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
-
-def count(counters, net, notable_nets=[]):
-    if net.is_diverged():
-        counters['divergent'] += 1
-    elif net.is_fixpoint():
-        if net.is_zero():
-            counters['fix_zero'] += 1
-        else:
-            counters['fix_other'] += 1
-            notable_nets += [net]
-    elif net.is_fixpoint(2):
-        counters['fix_sec'] += 1
-        notable_nets += [net]
-    else:
-        counters['other'] += 1
-    return counters, notable_nets
-
-
-if __name__ == '__main__':
-
-    with Experiment('applying_fixpoint') as exp:
-        exp.trials = 50
-        exp.run_count = 100
-        exp.epsilon = 1e-4
-        net_generators = []
-        for activation in ['linear']:  # , 'sigmoid', 'relu']:
-            for use_bias in [False]:
-                net_generators += [lambda activation=activation, use_bias=use_bias: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-                net_generators += [lambda activation=activation, use_bias=use_bias: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-                net_generators += [lambda activation=activation, use_bias=use_bias: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-        all_counters = []
-        all_notable_nets = []
-        all_names = []
-        for net_generator_id, net_generator in enumerate(net_generators):
-            counters = generate_counters()
-            notable_nets = []
-            for _ in tqdm(range(exp.trials)):
-                net = ParticleDecorator(net_generator())
-                net.with_params(epsilon=exp.epsilon)
-                name = str(net.net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias=" + str(net.get_keras_params().get('use_bias'))
-                for run_id in range(exp.run_count):
-                    loss = net.self_attack()
-                count(counters, net, notable_nets)
-            all_counters += [counters]
-            all_notable_nets += [notable_nets]
-            all_names += [name]
-            K.clear_session()
-        exp.save(all_counters=all_counters)
-        exp.save(trajectorys=exp.without_particles())
-        # net types reached in the end
-        # exp.save(all_notable_nets=all_notable_nets)
-        exp.save(all_names=all_names) #experiment setups
-        for exp_id, counter in enumerate(all_counters):
-            exp.log(all_names[exp_id])
-            exp.log(all_counters[exp_id])
-            exp.log('\n')

code/setups/experiments/exp-learn-from-soup-_1552658566.5572753-0/log.txt

@@ -1,4 +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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'zs': [0.0, 1.2, 5.2, 7.4, 8.1, 9.1, 9.6, 9.8, 10.0, 9.9, 9.9]}
-
-

code/setups/experiments/exp-training_fixpoint-_1552658296.0913951-0/log.txt

@@ -1,12 +0,0 @@
-WeightwiseNeuralNetwork activiation='linear' use_bias=False
-{'divergent': 0, 'fix_zero': 0, 'fix_other': 50, 'fix_sec': 0, 'other': 0}
-
-
-AggregatingNeuralNetwork activiation='linear' use_bias=False
-{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 50}
-
-
-RecurrentNeuralNetwork activiation='linear' use_bias=False
-{'divergent': 38, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 12}
-
-

code/setups/experiments/exp-weightwise_self_application-_1552664922.4501734-0/log.txt

@@ -1 +0,0 @@
-{'divergent': 11, 'fix_zero': 9, 'fix_other': 0, 'fix_sec': 0, 'other': 0}

code/setups/fixpoint-density.py

@@ -1,67 +0,0 @@
-import sys
-import os
-# Concat top Level dir to system environmental variables
-sys.path += os.path.join('..', '.')
-
-from util import *
-from experiment import *
-from network import *
-
-import keras.backend
-
-def generate_counters():
-    return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
-
-def count(counters, net, notable_nets=[]):
-    if net.is_diverged():
-        counters['divergent'] += 1
-    elif net.is_fixpoint():
-        if net.is_zero():
-            counters['fix_zero'] += 1
-        else:
-            counters['fix_other'] += 1
-            notable_nets += [net]
-    elif net.is_fixpoint(2):
-        counters['fix_sec'] += 1
-        notable_nets += [net]
-    else:
-        counters['other'] += 1
-    return counters, notable_nets
-
-
-if __name__ == '__main__':
-    with Experiment('fixpoint-density') as exp:
-        #NOTE: settings could/should stay this way
-        #FFT doesn't work though
-        exp.trials = 100000
-        exp.epsilon = 1e-4
-        net_generators = []
-        for activation in ['linear']:
-            net_generators += [lambda activation=activation: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
-            net_generators += [lambda activation=activation: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
-            # net_generators += [lambda activation=activation: FFTNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
-            # net_generators += [lambda activation=activation: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=False)]
-        all_counters = []
-        all_notable_nets = []
-        all_names = []
-        for net_generator_id, net_generator in enumerate(net_generators):
-            counters = generate_counters()
-            notable_nets = []
-            for _ in tqdm(range(exp.trials)):
-                net = net_generator().with_params(epsilon=exp.epsilon)
-                net = ParticleDecorator(net)
-                name = str(net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias='" + str(net.get_keras_params().get('use_bias')) + "'"
-                count(counters, net, notable_nets)
-                keras.backend.clear_session()
-            all_counters += [counters]
-            # all_notable_nets += [notable_nets]
-            all_names += [name]
-        exp.save(all_counters=all_counters)
-        exp.save(all_notable_nets=all_notable_nets)
-        exp.save(all_names=all_names)
-        for exp_id, counter in enumerate(all_counters):
-            exp.log(all_names[exp_id])
-            exp.log(all_counters[exp_id])
-            exp.log('\n')
-
-    print('Done')

code/setups/known-fixpoint-variation.py

@@ -1,93 +0,0 @@
-import sys
-
-import os
-
-# Concat top Level dir to system environmental variables
-sys.path += os.path.join('..', '.')
-
-from util import *
-from experiment import *
-from network import *
-from soup import prng
-
-import keras.backend
-
-
-from statistics import mean
-avg = mean
-
-
-def generate_fixpoint_weights():
-    return [
-        np.array([[1.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]], dtype=np.float32),
-        np.array([[1.0, 0.0], [0.0, 0.0]], dtype=np.float32),
-        np.array([[1.0], [0.0]], dtype=np.float32)
-    ]
-
-
-def generate_fixpoint_net():
-    #NOTE: Weightwise only is all we can do right now IMO
-    net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='sigmoid')
-    # I don't know if this work for aggregaeting. We don't actually need it, though.
-    # net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation='sigmoid')
-    net.set_weights(generate_fixpoint_weights())
-    return net
-
-
-def vary(old_weights, e=1.0):
-    new_weights = copy.deepcopy(old_weights)
-    for layer_id, layer in enumerate(new_weights):
-        for cell_id, cell in enumerate(layer):
-            for weight_id, weight in enumerate(cell):
-                if prng() < 0.5:
-                    new_weights[layer_id][cell_id][weight_id] = weight + prng() * e
-                else:
-                    new_weights[layer_id][cell_id][weight_id] = weight - prng() * e
-    return new_weights
-
-
-if __name__ == '__main__':
-    with Experiment('known-fixpoint-variation') as exp:
-        exp.depth = 10
-        exp.trials = 100
-        exp.max_steps = 100
-        exp.epsilon = 1e-4
-        exp.xs = []
-        exp.ys = []
-        exp.zs = []
-        exp.notable_nets = []
-        current_scale = 1.0
-        for _ in range(exp.depth):
-            print('variation scale ' + str(current_scale))
-            for _ in tqdm(range(exp.trials)):
-                net = generate_fixpoint_net().with_params(epsilon=exp.epsilon)
-                net = ParticleDecorator(net)
-                net.set_weights(vary(net.get_weights(), current_scale))
-                time_to_something = 0
-                time_as_fixpoint = 0
-                still_fixpoint = True
-                for _ in range(exp.max_steps):
-                    net.self_attack()
-                    if net.is_zero() or net.is_diverged():
-                        break
-                    if net.is_fixpoint():
-                        if still_fixpoint:
-                            time_as_fixpoint += 1
-                        else:
-                            print('remarkable')
-                            exp.notable_nets += [net.get_weights()]
-                            still_fixpoint = True
-                    else:
-                        still_fixpoint = False
-                    time_to_something += 1
-                exp.xs += [current_scale]
-                # time steps taken to reach divergence or zero (reaching another fix-point is basically never happening)
-                exp.ys += [time_to_something]
-                # time steps still regarded as sthe initial fix-point
-                exp.zs += [time_as_fixpoint]
-                keras.backend.clear_session()
-            current_scale /= 10.0
-        for d in range(exp.depth):
-            exp.log('variation 10e-' + str(d))
-            exp.log('avg time to vergence ' + str(avg(exp.ys[d*exp.trials:(d+1) * exp.trials])))
-            exp.log('avg time as fixpoint ' + str(avg(exp.zs[d*exp.trials:(d+1) * exp.trials])))

code/setups/learn_from_soup.py

@@ -1,110 +0,0 @@
-import sys
-import os
-
-# Concat top Level dir to system environmental variables
-sys.path += os.path.join('..', '.')
-
-from typing import Tuple
-
-from util import *
-from experiment import *
-from network import *
-from soup import *
-
-
-import keras.backend
-
-from statistics import mean
-avg = mean
-
-
-def generate_counters():
-    """
-    Initial build of the counter dict, to store counts.
-
-    :rtype: dict
-    :return: dictionary holding counter for: 'divergent', 'fix_zero', 'fix_sec', 'other'
-    """
-    return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
-
-
-def count(counters, soup, notable_nets=[]):
-    """
-    Count the occurences ot the types of weight trajectories.
-
-    :param counters:      A counter dictionary.
-    :param soup:          A Soup
-    :param notable_nets:  A list to store and save intersting candidates
-
-    :rtype      Tuple[dict, list]
-    :return:    Both the counter dictionary and the list of interessting nets.
-    """
-
-    for net in soup.particles:
-        if net.is_diverged():
-            counters['divergent'] += 1
-        elif net.is_fixpoint():
-            if net.is_zero():
-                counters['fix_zero'] += 1
-            else:
-                counters['fix_other'] += 1
-                # notable_nets += [net]
-        # elif net.is_fixpoint(2):
-        #     counters['fix_sec'] += 1
-        #     notable_nets += [net]
-        else:
-            counters['other'] += 1
-    return counters, notable_nets
-
-
-if __name__ == '__main__':
-
-    with SoupExperiment('learn-from-soup') as exp:
-        exp.soup_size = 10
-        exp.soup_life = 100
-        exp.trials = 10
-        exp.learn_from_severity_values = [10 * i for i in range(11)]
-        exp.epsilon = 1e-4
-        net_generators = []
-        for activation in ['linear']:  # ['sigmoid', 'linear', 'relu']:
-            for use_bias in [False]:
-                net_generators += [lambda activation=activation, use_bias=use_bias: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-                # net_generators += [lambda activation=activation, use_bias=use_bias: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-                # net_generators += [lambda activation=activation, use_bias=use_bias: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-
-        all_names = []
-        all_data = []
-        for net_generator_id, net_generator in enumerate(net_generators):
-            xs = []
-            ys = []
-            zs = []
-            notable_nets = []
-            for learn_from_severity in exp.learn_from_severity_values:
-                counters = generate_counters()
-                results = []
-                for _ in tqdm(range(exp.trials)):
-                    soup = Soup(exp.soup_size, lambda net_generator=net_generator,exp=exp: TrainingNeuralNetworkDecorator(net_generator()).with_params(epsilon=exp.epsilon))
-                    soup.with_params(attacking_rate=-1, learn_from_rate=0.1, train=0, learn_from_severity=learn_from_severity)
-                    soup.seed()
-                    name = str(soup.particles[0].net.__class__.__name__) + " activiation='" + str(soup.particles[0].get_keras_params().get('activation')) + "' use_bias=" + str(soup.particles[0].get_keras_params().get('use_bias'))
-                    for time in range(exp.soup_life):
-                        soup.evolve()
-                    count(counters, soup, notable_nets)
-                    keras.backend.clear_session()
-
-                xs += [learn_from_severity]
-                ys += [float(counters['fix_zero']) / float(exp.trials)]
-                zs += [float(counters['fix_other']) / float(exp.trials)]
-            all_names += [name]
-            # xs: learn_from_intensity according to exp.learn_from_intensity_values
-            # ys: zero-fixpoints after life time
-            # zs: non-zero-fixpoints after life time
-            all_data += [{'xs':xs, 'ys':ys, 'zs':zs}]
-
-        exp.save(all_names=all_names)
-        exp.save(all_data=all_data)
-        exp.save(soup=soup.without_particles())
-        for exp_id, name in enumerate(all_names):
-            exp.log(all_names[exp_id])
-            exp.log(all_data[exp_id])
-            exp.log('\n')

code/setups/mixed-self-fixpoints.py

@@ -1,101 +0,0 @@
-import sys
-import os
-
-from typing import Tuple
-
-# Concat top Level dir to system environmental variables
-sys.path += os.path.join('..', '.')
-
-from util import *
-from experiment import *
-from network import *
-
-import keras.backend
-
-
-def generate_counters():
-    """
-    Initial build of the counter dict, to store counts.
-
-    :rtype: dict
-    :return: dictionary holding counter for: 'divergent', 'fix_zero', 'fix_sec', 'other'
-    """
-    return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
-
-
-def count(counters, net, notable_nets=[]):
-    """
-    Count the occurences ot the types of weight trajectories.
-
-    :param counters:      A counter dictionary.
-    :param net:           A Neural Network
-    :param notable_nets:  A list to store and save intersting candidates
-
-    :rtype      Tuple[dict, list]
-    :return:    Both the counter dictionary and the list of interessting nets.
-    """
-
-    if net.is_diverged():
-        counters['divergent'] += 1
-    elif net.is_fixpoint():
-        if net.is_zero():
-            counters['fix_zero'] += 1
-        else:
-            counters['fix_other'] += 1
-            notable_nets += [net]
-    elif net.is_fixpoint(2):
-        counters['fix_sec'] += 1
-        notable_nets += [net]
-    else:
-        counters['other'] += 1
-    return counters, notable_nets
-
-if __name__ == '__main__':
-
-    with Experiment('mixed-self-fixpoints') as exp:
-        exp.trials = 20
-        exp.selfattacks = 4
-        exp.trains_per_selfattack_values = [50 * i for i in range(11)]
-        exp.epsilon = 1e-4
-        net_generators = []
-        for activation in ['linear']:  # , 'sigmoid', 'relu']:
-            for use_bias in [False]:
-                net_generators += [lambda activation=activation, use_bias=use_bias: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-                net_generators += [lambda activation=activation, use_bias=use_bias: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-                # net_generators += [lambda activation=activation, use_bias=use_bias: FFTNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-                net_generators += [lambda activation=activation, use_bias=use_bias: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-
-        all_names = []
-        all_data = []
-
-        for net_generator_id, net_generator in enumerate(net_generators):
-            xs = []
-            ys = []
-            for trains_per_selfattack in exp.trains_per_selfattack_values:
-                counters = generate_counters()
-                notable_nets = []
-                for _ in tqdm(range(exp.trials)):
-                    net = ParticleDecorator(net_generator())
-                    net = TrainingNeuralNetworkDecorator(net).with_params(epsilon=exp.epsilon)
-                    name = str(net.net.net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias=" + str(net.get_keras_params().get('use_bias'))
-                    for selfattack_id in range(exp.selfattacks):
-                        net.self_attack()
-                        for train_id in range(trains_per_selfattack):
-                            loss = net.compiled().train(epoch=selfattack_id*trains_per_selfattack+train_id)
-                        if net.is_diverged() or net.is_fixpoint():
-                            break
-                    count(counters, net, notable_nets)
-                    keras.backend.clear_session()
-                xs += [trains_per_selfattack]
-                ys += [float(counters['fix_zero'] + counters['fix_other']) / float(exp.trials)]
-            all_names += [name]
-            # xs: how many trains per self-attack from exp.trains_per_selfattack_values
-            # ys: average amount of fixpoints found
-            all_data += [{'xs': xs, 'ys': ys}]
-
-        exp.save(all_names=all_names)
-        exp.save(all_data=all_data)
-        for exp_id, name in enumerate(all_names):
-            exp.log(all_names[exp_id])
-            exp.log(all_data[exp_id])
-            exp.log('\n')

code/setups/mixed-soup.py

@@ -1,108 +0,0 @@
-import sys
-import os
-
-# Concat top Level dir to system environmental variables
-sys.path += os.path.join('..', '.')
-
-from typing import Tuple
-
-from util import *
-from experiment import *
-from network import *
-from soup import *
-
-import keras.backend
-
-
-def generate_counters():
-    """
-    Initial build of the counter dict, to store counts.
-
-    :rtype: dict
-    :return: dictionary holding counter for: 'divergent', 'fix_zero', 'fix_sec', 'other'
-    """
-    return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
-
-
-def count(counters, soup, notable_nets=[]):
-    """
-    Count the occurences ot the types of weight trajectories.
-
-    :param counters:      A counter dictionary.
-    :param soup:          A Soup
-    :param notable_nets:  A list to store and save intersting candidates
-
-    :rtype      Tuple[dict, list]
-    :return:    Both the counter dictionary and the list of interessting nets.
-    """
-
-    for net in soup.particles:
-        if net.is_diverged():
-            counters['divergent'] += 1
-        elif net.is_fixpoint():
-            if net.is_zero():
-                counters['fix_zero'] += 1
-            else:
-                counters['fix_other'] += 1
-                # notable_nets += [net]
-        # elif net.is_fixpoint(2):
-        #     counters['fix_sec'] += 1
-        #     notable_nets += [net]
-        else:
-            counters['other'] += 1
-    return counters, notable_nets
-
-if __name__ == '__main__':
-
-    with Experiment('mixed-soup') as exp:
-        exp.trials = 10
-        exp.soup_size = 10
-        exp.soup_life = 5
-        exp.trains_per_selfattack_values = [10 * i for i in range(11)]
-        exp.epsilon = 1e-4
-        net_generators = []
-        for activation in ['linear']:  # ['linear', 'sigmoid', 'relu']:
-            for use_bias in [False]:
-                net_generators += [lambda activation=activation, use_bias=use_bias: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-                net_generators += [lambda activation=activation, use_bias=use_bias: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-                # net_generators += [lambda activation=activation, use_bias=use_bias: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-
-        all_names = []
-        all_data = []
-        for net_generator_id, net_generator in enumerate(net_generators):
-            xs = []
-            ys = []
-            zs = []
-            for trains_per_selfattack in exp.trains_per_selfattack_values:
-                counters = generate_counters()
-                notable_nets = []
-                for soup_idx in tqdm(range(exp.trials)):
-                    soup = Soup(exp.soup_size,
-                                lambda net_generator=net_generator, exp=exp: TrainingNeuralNetworkDecorator(
-                                    net_generator()).with_params(epsilon=exp.epsilon))
-                    soup.with_params(attacking_rate=0.1, learn_from_rate=-1, train=trains_per_selfattack,
-                                     learn_from_severity=-1)
-                    soup.seed()
-                    name = str(soup.particles[0].net.__class__.__name__) + " activiation='" + str(
-                        soup.particles[0].get_keras_params().get('activation')) + "' use_bias=" + str(
-                        soup.particles[0].get_keras_params().get('use_bias'))
-                    for _ in range(exp.soup_life):
-                        soup.evolve()
-                    count(counters, soup, notable_nets)
-                    keras.backend.clear_session()
-
-                xs += [trains_per_selfattack]
-                ys += [float(counters['fix_zero']) / float(exp.trials)]
-                zs += [float(counters['fix_other']) / float(exp.trials)]
-            all_names += [name]
-            # xs: how many trains per self-attack from exp.trains_per_selfattack_values
-            # ys: average amount of zero-fixpoints found
-            # zs: average amount of non-zero fixpoints
-            all_data += [{'xs': xs, 'ys': ys, 'zs': zs}]
-
-        exp.save(all_names=all_names)
-        exp.save(all_data=all_data)
-        for exp_id, name in enumerate(all_names):
-            exp.log(all_names[exp_id])
-            exp.log(all_data[exp_id])
-            exp.log('\n')

code/setups/network_trajectorys.py

@@ -1,112 +0,0 @@
-import sys
-import os
-
-# Concat top Level dir to system environmental variables
-sys.path += os.path.join('..', '.')
-
-from soup import *
-from experiment import *
-
-
-if __name__ == '__main__':
-    def run_exp(net, prints=False):
-        # INFO Run_ID needs to be more than 0, so that exp stores the trajectories!
-        exp.run_net(net, 100, run_id=run_id + 1)
-        exp.historical_particles[run_id] = net
-        if prints:
-            print("Fixpoint? " + str(net.is_fixpoint()))
-            print("Loss " + str(loss))
-
-    if True:
-        # WeightWise Neural Network
-        with FixpointExperiment(name="weightwise_self_application") as exp:
-            for run_id in tqdm(range(20)):
-                net = ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)
-                                        .with_keras_params(activation='linear'))
-                run_exp(net)
-                K.clear_session()
-            exp.log(exp.counters)
-            exp.save(trajectorys=exp.without_particles())
-
-    if False:
-        # Aggregating Neural Network
-        with FixpointExperiment(name="aggregating_self_application") as exp:
-            for run_id in tqdm(range(10)):
-                net = ParticleDecorator(AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)
-                                        .with_keras_params(activation='linear'))
-                run_exp(net)
-                K.clear_session()
-            exp.log(exp.counters)
-            exp.save(trajectorys=exp.without_particles())
-
-    if False:
-        #FFT Neural Network
-        with FixpointExperiment() as exp:
-            for run_id in tqdm(range(10)):
-                net = ParticleDecorator(FFTNeuralNetwork(aggregates=4, width=2, depth=2)
-                                        .with_keras_params(activation='linear'))
-                run_exp(net)
-                K.clear_session()
-            exp.log(exp.counters)
-            exp.save(trajectorys=exp.without_particles())
-
-    if False:
-        # ok so this works quite realiably
-        with FixpointExperiment(name="weightwise_learning") as exp:
-            for i in range(10):
-                run_count = 100
-                net = TrainingNeuralNetworkDecorator(ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)))
-                net.with_params(epsilon=0.0001).with_keras_params(activation='linear')
-                exp.historical_particles[net.get_uid()] = net
-                for run_id in tqdm(range(run_count+1)):
-                    net.compiled()
-                    loss = net.train(epoch=run_id)
-                    # run_exp(net)
-                    # net.save_state(time=run_id)
-                K.clear_session()
-            exp.save(trajectorys=exp.without_particles())
-
-    if False:
-        # ok so this works quite realiably
-        with FixpointExperiment(name="aggregating_learning") as exp:
-            for i in range(10):
-                run_count = 100
-                net = TrainingNeuralNetworkDecorator(ParticleDecorator(AggregatingNeuralNetwork(4, width=2, depth=2)))
-                net.with_params(epsilon=0.0001).with_keras_params(activation='linear')
-                exp.historical_particles[net.get_uid()] = net
-                for run_id in tqdm(range(run_count+1)):
-                    net.compiled()
-                    loss = net.train(epoch=run_id)
-                    # run_exp(net)
-                    # net.save_state(time=run_id)
-                K.clear_session()
-            exp.save(trajectorys=exp.without_particles())
-
-    if False:
-        # this explodes in our faces completely... NAN everywhere
-        # TODO: Wtf is happening here?
-        with FixpointExperiment() as exp:
-            run_count = 10000
-            net = TrainingNeuralNetworkDecorator(RecurrentNeuralNetwork(width=2, depth=2))\
-                .with_params(epsilon=0.1e-2).with_keras_params(optimizer='sgd', activation='linear')
-            for run_id in tqdm(range(run_count+1)):
-                loss = net.compiled().train()
-                if run_id % 500 == 0:
-                    net.print_weights()
-                    # print(net.apply_to_network(net))
-                    print("Fixpoint? " + str(net.is_fixpoint()))
-                    print("Loss " + str(loss))
-                    print()
-    if False:
-        # and this gets somewhat interesting... we can still achieve non-trivial fixpoints
-        # over multiple applications when training enough in-between
-        with MixedFixpointExperiment() as exp:
-            for run_id in range(10):
-                net = TrainingNeuralNetworkDecorator(FFTNeuralNetwork(2, width=2, depth=2))\
-                    .with_params(epsilon=0.0001, activation='sigmoid')
-                exp.run_net(net, 500, 10)
-
-                net.print_weights()
-
-                print("Fixpoint? " + str(net.is_fixpoint()))
-            exp.log(exp.counters)

code/setups/soup_trajectorys.py

@@ -1,32 +0,0 @@
-import sys
-import os
-
-# Concat top Level dir to system environmental variables
-sys.path += os.path.join('..', '.')
-
-from soup import *
-from experiment import *
-
-
-if __name__ == '__main__':
-    if True:
-        with SoupExperiment("soup") as exp:
-            for run_id in range(1):
-                net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)) \
-                     .with_keras_params(activation='linear').with_params(epsilon=0.0001)
-                # net_generator = lambda: TrainingNeuralNetworkDecorator(AggregatingNeuralNetwork(4, 2, 2))\
-                #     .with_keras_params(activation='linear')
-                # net_generator = lambda: TrainingNeuralNetworkDecorator(FFTNeuralNetwork(4, 2, 2))\
-                #    .with_keras_params(activation='linear')
-                # net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
-                soup = Soup(20, net_generator).with_params(remove_divergent=True, remove_zero=True,
-                                                           train=30,
-                                                           learn_from_rate=-1)
-                soup.seed()
-                for _ in tqdm(range(100)):
-                    soup.evolve()
-                exp.log(soup.count())
-                # 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
-                exp.save(soup=soup.without_particles())

code/setups/training-fixpoints.py

@@ -1,70 +0,0 @@
-import sys
-import os
-
-# Concat top Level dir to system environmental variables
-sys.path += os.path.join('..', '.')
-
-from util import *
-from experiment import *
-from network import *
-
-import keras.backend as K
-
-def generate_counters():
-    return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
-
-def count(counters, net, notable_nets=[]):
-    if net.is_diverged():
-        counters['divergent'] += 1
-    elif net.is_fixpoint():
-        if net.is_zero():
-            counters['fix_zero'] += 1
-        else:
-            counters['fix_other'] += 1
-            notable_nets += [net]
-    elif net.is_fixpoint(2):
-        counters['fix_sec'] += 1
-        notable_nets += [net]
-    else:
-        counters['other'] += 1
-    return counters, notable_nets
-
-
-if __name__ == '__main__':
-
-    with Experiment('training_fixpoint') as exp:
-        exp.trials = 50
-        exp.run_count = 1000
-        exp.epsilon = 1e-4
-        net_generators = []
-        for activation in ['linear']:  # , 'sigmoid', 'relu']:
-            for use_bias in [False]:
-                net_generators += [lambda activation=activation, use_bias=use_bias: WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-                net_generators += [lambda activation=activation, use_bias=use_bias: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-                net_generators += [lambda activation=activation, use_bias=use_bias: RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation=activation, use_bias=use_bias)]
-        all_counters = []
-        all_notable_nets = []
-        all_names = []
-        for net_generator_id, net_generator in enumerate(net_generators):
-            counters = generate_counters()
-            notable_nets = []
-            for _ in tqdm(range(exp.trials)):
-                net = ParticleDecorator(net_generator())
-                net = TrainingNeuralNetworkDecorator(net).with_params(epsilon=exp.epsilon)
-                name = str(net.net.net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias=" + str(net.get_keras_params().get('use_bias'))
-                for run_id in range(exp.run_count):
-                    loss = net.compiled().train(epoch=run_id+1)
-                count(counters, net, notable_nets)
-            all_counters += [counters]
-            all_notable_nets += [notable_nets]
-            all_names += [name]
-            K.clear_session()
-        exp.save(all_counters=all_counters)
-        exp.save(trajectorys=exp.without_particles())
-        # net types reached in the end
-        # exp.save(all_notable_nets=all_notable_nets)
-        exp.save(all_names=all_names) #experiment setups
-        for exp_id, counter in enumerate(all_counters):
-            exp.log(all_names[exp_id])
-            exp.log(all_counters[exp_id])
-            exp.log('\n')