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code/experiment.py

@@ -2,13 +2,17 @@ import os
 import time
 import dill
 from tqdm import tqdm
-import copy
+from copy import copy
 
 from tensorflow.python.keras import backend as K
 
 from abc import ABC, abstractmethod
 
 
+class IllegalArgumentError(ValueError):
+    pass
+
+
 class Experiment(ABC):
 
     @staticmethod
@@ -20,15 +24,24 @@ class Experiment(ABC):
     def reset_model():
         K.clear_session()
 
-    def __init__(self, name=None, ident=None):
+    def __init__(self, name=None, ident=None, **kwargs):
         self.experiment_id = f'{ident or ""}_{time.time()}'
         self.experiment_name = name or 'unnamed_experiment'
-        self.next_iteration = 0
+        self.iteration = 0
         self.log_messages = list()
         self.historical_particles = dict()
+        self.params = dict(exp_iterations=100, application_steps=100, prints=True, trains_per_application=100)
+        self.with_params(**kwargs)
+
+    def __copy__(self, *args, **kwargs):
+        params = self.params
+        params.update(name=self.experiment_name)
+        params.update(**kwargs)
+        self_copy = self.__class__(*args, **params)
+        return self_copy
 
     def __enter__(self):
-        self.dir = os.path.join('experiments', f'exp-{self.experiment_name}-{self.experiment_id}-{self.next_iteration}')
+        self.dir = os.path.join('experiments', f'exp-{self.experiment_name}-{self.experiment_id}-{self.iteration}')
         os.makedirs(self.dir)
         print(f'** created {self.dir} **')
         return self
@@ -36,7 +49,14 @@ class Experiment(ABC):
     def __exit__(self, exc_type, exc_value, traceback):
         self.save(experiment=self.without_particles())
         self.save_log()
-        self.next_iteration += 1
+        # Clean Exit
+        self.reset_all()
+        # self.iteration += 1  Taken From here!
+
+    def with_params(self, **kwargs):
+        # Make them your own
+        self.params.update(kwargs)
+        return self
 
     def log(self, message, **kwargs):
         self.log_messages.append(message)
@@ -47,16 +67,15 @@ class Experiment(ABC):
             for log_message in self.log_messages:
                 print(str(log_message), file=log_file)
 
-    def __copy__(self):
-        self_copy = self.__class__(name=self.experiment_name,)
-        self_copy.__dict__ = {attr: self.__dict__[attr] for attr in self.__dict__ if
-                              attr not in ['particles', 'historical_particles']}
-        return self_copy
-
     def without_particles(self):
-        self_copy = copy.copy(self)
+        self_copy = copy(self)
-        # self_copy.particles = [particle.states for particle in self.particles]
+        # Check if attribute exists
-        self_copy.historical_particles = {key: val.states for key, val in self.historical_particles.items()}
+        if hasattr(self, 'historical_particles'):
+            # Check if it is empty.
+            if self.historical_particles:
+                # Do the Update
+                # self_copy.particles = [particle.states for particle in self.particles]
+                self_copy.historical_particles = {key: val.states for key, val in self.historical_particles.items()}
         return self_copy
 
     def save(self, **kwargs):
@@ -64,28 +83,32 @@ class Experiment(ABC):
             with open(os.path.join(self.dir, f"{name}.dill"), "wb") as dill_file:
                 dill.dump(value, dill_file)
 
+    def reset_log(self):
+        self.log_messages = list()
+
     @abstractmethod
-    def run_net(self, net, trains_per_application=100, step_limit=100, run_id=0, **kwargs):
+    def run_net(self, net, **kwargs):
         raise NotImplementedError
         pass
 
-    def run_exp(self, network_generator, exp_iterations, prints=False, **kwargs):
+    def run_exp(self, network_generator, reset_model=False, **kwargs):
         # INFO Run_ID needs to be more than 0, so that exp stores the trajectories!
-        for run_id in range(exp_iterations):
+        for run_id in range(self.params.get('exp_iterations')):
             network = network_generator()
-            self.run_net(network, 100, run_id=run_id + 1, **kwargs)
+            self.run_net(network, **kwargs)
             self.historical_particles[run_id] = network
-            if prints:
+            if self.params.get('prints'):
                 print("Fixpoint? " + str(network.is_fixpoint()))
-        self.reset_model()
+            self.iteration += 1
+            if reset_model:
+                self.reset_model()
 
     def reset_all(self):
+        self.reset_log()
         self.reset_model()
 
 
 class FixpointExperiment(Experiment):
-    if kwargs.get('logging', False):
-        self.log(self.counters)
 
     def __init__(self, **kwargs):
         kwargs['name'] = self.__class__.__name__ if 'name' not in kwargs else kwargs['name']
@@ -93,12 +116,22 @@ class FixpointExperiment(Experiment):
         self.counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
         self.interesting_fixpoints = []
 
-    def run_net(self, net, step_limit=100, run_id=0, **kwargs):
+    def run_exp(self, network_generator, logging=True, reset_model=False, **kwargs):
-        i = 0
+        kwargs.update(reset_model=False)
-        while i < step_limit and not net.is_diverged() and not net.is_fixpoint():
+        super(FixpointExperiment, self).run_exp(network_generator, **kwargs)
-            net.self_attack()
+        if logging:
-            i += 1
+            self.log(self.counters)
-            if run_id:
+        if reset_model:
+            self.reset_model()
+
+    def run_net(self, net, **kwargs):
+        if len(kwargs):
+            raise IllegalArgumentError
+        for i in range(self.params.get('application_steps')):
+            if net.is_diverged() or net.is_fixpoint():
+                break
+            net.set_weights(net.apply_to_weights(net.get_weights()))
+            if self.iteration and hasattr(self, 'save_state'):
                 net.save_state(time=i)
         self.count(net)
 
@@ -128,44 +161,117 @@ class FixpointExperiment(Experiment):
 
 class MixedFixpointExperiment(FixpointExperiment):
 
-    def run_net(self, net, trains_per_application=100, step_limit=100, run_id=0, **kwargs):
+    def __init__(self, **kwargs):
-        for i in range(step_limit):
+        kwargs['name'] = self.__class__.__name__ if 'name' not in kwargs else kwargs['name']
+        super(MixedFixpointExperiment, self).__init__(**kwargs)
+
+    def run_net(self, net, **kwargs):
+        assert hasattr(net, 'train'), 'This Network must be trainable, i.e. use the "TrainingNeuralNetworkDecorator"!'
+
+        for application in range(self.params.get('application_steps')):
+            epoch_num = self.params.get('trains_per_application') * application
+            net.set_weights(net.apply_to_weights(net.get_weights()))
             if net.is_diverged() or net.is_fixpoint():
                 break
-            net.self_attack()
+            barformat = "Experiment Iteration: {postfix[iteration]} | "
-            with tqdm(postfix=["Loss", dict(value=0)]) as bar:
+            barformat += "Evolution Step:{postfix[step]}| "
-                for _ in range(trains_per_application):
+            barformat += "Training Epoch:{postfix[epoch]}| "
-                    loss = net.compiled().train()
+            barformat += "Loss: {postfix[loss]} | {bar}"
-                    bar.postfix[1]["value"] = loss
+            with tqdm(total=self.params.get('trains_per_application'),
+                      postfix={'step': 0, 'loss': 0, 'iteration': self.iteration, 'epoch': 0, None: None},
+                      bar_format=barformat) as bar:
+                # This iterates for self.trains_per_application times, the addition is just for  epoch enumeration
+                for epoch in range(epoch_num, epoch_num + self.params.get('trains_per_application')):
+                    if net.is_diverged():
+                        print('Network diverged to either inf or nan... breaking')
+                        break
+                    loss = net.train(epoch=epoch)
+                    if epoch % 10 == 0:
+                        bar.postfix.update(step=application, epoch=epoch, loss=loss, iteration=self.iteration)
                     bar.update()
-            if run_id:
+                    epoch_num += 1
+            if self.iteration and hasattr(net, 'save_sate'):
                 net.save_state()
         self.count(net)
 
 
-class SoupExperiment(Experiment):
+class TaskExperiment(MixedFixpointExperiment):
 
     def __init__(self, **kwargs):
-        super(SoupExperiment, self).__init__(name=kwargs.get('name', self.__class__.__name__))
+        kwargs['name'] = self.__class__.__name__ if 'name' not in kwargs else kwargs['name']
+        super(TaskExperiment, self).__init__(**kwargs)
 
-    def run_exp(self, network_generator, exp_iterations, soup_generator=None, soup_iterations=0, prints=False):
+    def run_exp(self, network_generator, reset_model=False, logging=True, **kwargs):
-        for i in range(soup_iterations):
+        kwargs.update(reset_model=False, logging=logging)
-            soup = soup_generator()
+        super(FixpointExperiment, self).run_exp(network_generator, **kwargs)
+        if reset_model:
+            self.reset_model()
+        pass
+
+    def run_net(self, net, **kwargs):
+        assert hasattr(net, 'evaluate')
+        super(TaskExperiment, self).run_net(net, **kwargs)
+
+        # Get Performance without Training
+        task_performance = net.evaluate(*net.get_samples(task_samples=True),
+                                        batchsize=net.get_amount_of_weights())
+        self_performance = net.evaluate(*net.get_samples(self_samples=True),
+                                        batchsize=net.get_amount_of_weights())
+
+        current_performance = dict(task_performance=task_performance,
+                                   self_performance=self_performance,
+                                   counters=self.counters, id=self.iteration)
+
+        self.log(current_performance)
+        pass
+
+
+class SoupExperiment(Experiment):
+
+    def __init__(self, soup_generator, **kwargs):
+        kwargs['name'] = self.__class__.__name__ if 'name' not in kwargs else kwargs['name']
+        self.soup_generator = soup_generator
+        super(SoupExperiment, self).__init__(**kwargs)
+
+    def run_exp(self, network_generator, **kwargs):
+        for i in range(self.params.get('exp_iterations')):
+            soup = self.soup_generator()
             soup.seed()
-            for _ in tqdm(exp_iterations):
+            for _ in tqdm(range(self.params.get('application_steps'))):
                 soup.evolve()
             self.log(soup.count())
             self.save(soup=soup.without_particles())
+            K.clear_session()
 
-    def run_net(self, net, trains_per_application=100, step_limit=100, run_id=0, **kwargs):
+    def run_net(self, net, **kwargs):
         raise NotImplementedError
         pass
 
 
-class IdentLearningExperiment(Experiment):
+class TaskingSoupExperiment(Experiment):
 
-    def __init__(self):
+    def __init__(self, soup_generator, **kwargs):
-        super(IdentLearningExperiment, self).__init__(name=self.__class__.__name__)
+        kwargs['name'] = self.__class__.__name__ if 'name' not in kwargs else kwargs['name']
+        super(TaskingSoupExperiment, self).__init__(**kwargs)
+        self.soup_generator = soup_generator
 
-    def run_net(self, net, trains_per_application=100, step_limit=100, run_id=0, **kwargs):
+    def __copy__(self):
+        super(TaskingSoupExperiment, self).__copy__(self.soup_generator)
+
+    def run_exp(self, **kwargs):
+        for i in range(self.params.get('exp_iterations')):
+            soup = self.soup_generator()
+            soup.seed()
+            for _ in tqdm(range(self.params.get('application_steps'))):
+                soup.evolve()
+            self.log(soup.count())
+            self.save(soup=soup.without_particles())
+            K.clear_session()
+
+    def run_net(self, net, **kwargs):
+        raise NotImplementedError()
         pass
+
+
+if __name__ == '__main__':
+    pass

code/network.py

@@ -1,14 +1,26 @@
+# Librarys
 import numpy as np
 from abc import abstractmethod, ABC
-from typing import List, Union
+from typing import List, Tuple
 from types import FunctionType
+import warnings
 
+import os
+
+# Functions and Operators
+from operator import mul
+from functools import reduce
+from itertools import accumulate
+from copy import deepcopy
+
+# Deep learning Framework
 from tensorflow.python.keras.models import Sequential
 from tensorflow.python.keras.callbacks import Callback
 from tensorflow.python.keras.layers import SimpleRNN, Dense
-from tensorflow.python.keras import backend as K
 
-from experiment import *
+# Experiment Class
+from task import TaskAdditionOfN
+from experiment import TaskExperiment
 
 # Supress warnings and info messages
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
@@ -28,104 +40,23 @@ class SaveStateCallback(Callback):
         return
 
 
-class Weights:
+class EarlyStoppingByInfNanLoss(Callback):
+    def __init__(self, monitor='loss', verbose=0):
+        super(Callback, self).__init__()
+        self.monitor = monitor
+        self.verbose = verbose
 
-    @staticmethod
+    def on_epoch_end(self, epoch, logs: dict = None):
-    def __reshape_flat_array__(array, shapes):
+        logs = logs or dict()
-        sizes: List[int] = [int(np.prod(shape)) for shape in shapes]
+        current = logs.get(self.monitor)
-        # Split the incoming array into slices for layers
+        if current is None:
-        slices = [array[x: y] for x, y in zip(np.cumsum([0]+sizes), np.cumsum([0]+sizes)[1:])]
+            warnings.warn(f'Early stopping requires {self.monitor} available!', RuntimeWarning)
-        # reshape them in accordance to the given shapes
+            pass
-        weights = [np.reshape(weight_slice, shape) for weight_slice, shape in zip(slices, shapes)]
-        return weights
 
-    def __init__(self, weight_vector: Union[List[np.ndarray], np.ndarray], flat_array_shape=None):
+        if np.isnan(current) or np.isinf(current):
-        """
+            if self.verbose > 0:
-        Weight class, for easy manipulation of weight vectors from Keras models
+                print(f'Epoch {epoch}: early stopping THR')
-
+            self.model.stop_training = True
-        :param weight_vector: A numpy array holding weights
-        :type weight_vector: List[np.ndarray]
-        """
-        self.__iter_idx = [0, 0]
-        if flat_array_shape:
-            weight_vector = self.__reshape_flat_array__(weight_vector, flat_array_shape)
-
-        self.layers = weight_vector
-
-        # TODO: implement a way to access the cells directly
-        # self.cells = len(self)
-        # TODO: implement a way to access the weights directly
-        # self.weights = self.to_flat_array() ?
-
-    def __iter__(self):
-        self.__iter_idx = [0, 0]
-        return self
-
-    def __getitem__(self, item):
-        return self.layers[item]
-
-    def max(self):
-        np.max(self.layers)
-
-    def avg(self):
-        return np.average(self.layers)
-
-    def __len__(self):
-        return sum([x.size for x in self.layers])
-
-    def shapes(self):
-        return [x.shape for x in self.layers]
-
-    def num_layers(self):
-        return len(self.layers)
-
-    def __copy__(self):
-        return copy.deepcopy(self)
-
-    def __next__(self):
-        # ToDo: Check iteration progress over layers
-        # ToDo: There is still a problem interation, currently only cell level is the last loop stage.
-        # Do we need this?
-        if self.__iter_idx[0] >= len(self.layers):
-            if self.__iter_idx[1] >= len(self.layers[self.__iter_idx[0]]):
-                raise StopIteration
-        result = self.layers[self.__iter_idx[0]][self.__iter_idx[1]]
-
-        if self.__iter_idx[1] >= len(self.layers[self.__iter_idx[0]]):
-            self.__iter_idx[0] += 1
-            self.__iter_idx[1] = 0
-        else:
-            self.__iter_idx[1] += 1
-        return result
-
-    def __repr__(self):
-        return f'Weights({self.to_flat_array().tolist()})'
-
-    def to_flat_array(self) -> np.ndarray:
-        return np.hstack([weight.flatten() for weight in self.layers])
-
-    def from_flat_array(self, array):
-        new_weights = self.__reshape_flat_array__(array, self.shapes())
-        return new_weights
-
-    def shuffle(self):
-        flat = self.to_flat_array()
-        np.random.shuffle(flat)
-        self.from_flat_array(flat)
-        return True
-
-    def are_diverged(self):
-        return any([np.isnan(x).any() for x in self.layers]) or any([np.isinf(x).any() for x in self.layers])
-
-    def are_within_bounds(self, lower_bound: float, upper_bound: float):
-        return bool(sum([((lower_bound < x) & (x > upper_bound)).size for x in self.layers]))
-
-    def aggregate_by(self, func: FunctionType, num_aggregates):
-        collection_sizes = len(self) // num_aggregates
-        weights = self.to_flat_array()[:collection_sizes * num_aggregates].reshape((num_aggregates, -1))
-        aggregated_weights = func(weights, num_aggregates)
-        left_overs = self.to_flat_array()[collection_sizes * num_aggregates:]
-        return aggregated_weights, left_overs
 
 
 class NeuralNetwork(ABC):
@@ -133,10 +64,50 @@ class NeuralNetwork(ABC):
     This is the Base Network Class, including abstract functions that must be implemented.
     """
 
+    @staticmethod
+    def are_weights_diverged(weights: List[np.ndarray]) -> bool:
+        return any([any((np.isnan(x).any(), np.isinf(x).any())) for x in weights])
+
+    @staticmethod
+    def are_weights_within_bounds(weights: List[np.ndarray], lower_bound: float, upper_bound: float) -> bool:
+        return any([((lower_bound < x) & (x < upper_bound)).any() for x in weights])
+
+    @staticmethod
+    def get_weight_amount(weights: List[np.ndarray]):
+        return sum([x.size for x in weights])
+
+    @staticmethod
+    def shapes(weights: List[np.ndarray]):
+        return [x.shape for x in weights]
+
+    @staticmethod
+    def num_layers(weights: List[np.ndarray]):
+        return len(weights)
+
+    def repr(self, weights: List[np.ndarray]):
+        return f'Weights({self.weights_to_flat_array(weights).tolist()})'
+
+    @staticmethod
+    def weights_to_flat_array(weights: List[np.ndarray]) -> np.ndarray:
+        return np.concatenate([d.ravel() for d in weights])
+
+    @staticmethod
+    def reshape_flat_array(array, shapes: List[Tuple[int]]) -> List[np.ndarray]:
+        # Same thing, but with an additional np call
+        # sizes: List[int] = [int(np.prod(shape)) for shape in shapes]
+
+        sizes = [reduce(mul, shape) for shape in shapes]
+        # Split the incoming array into slices for layers
+        slices = [array[x: y] for x, y in zip(accumulate([0] + sizes), accumulate(sizes))]
+        # reshape them in accordance to the given shapes
+        weights = [np.reshape(weight_slice, shape) for weight_slice, shape in zip(slices, shapes)]
+        return weights
+
     def __init__(self, **params):
         super().__init__()
-        self.params = dict(epsilon=0.00000000000001)
+        self.params = dict(epsilon=0.00000000000001, early_nan_stopping=True, store_states=False)
         self.params.update(params)
+        self.name = params.get('name', self.__class__.__name__)
         self.keras_params = dict(activation='linear', use_bias=False)
         self.states = []
         self.model: Sequential
@@ -155,76 +126,89 @@ class NeuralNetwork(ABC):
         self.keras_params.update(kwargs)
         return self
 
-    def get_weights(self) -> Weights:
+    def print_weights(self, weights=None):
-        return Weights(self.model.get_weights())
+        print(self.repr(weights or self.get_weights()))
+
+    def get_amount_of_weights(self):
+        return self.get_weight_amount(self.get_weights())
+
+    def get_model(self):
+        return self.model
+
+    def get_weights(self) -> List[np.ndarray]:
+        return self.get_model().get_weights()
 
     def get_weights_flat(self) -> np.ndarray:
-        return self.get_weights().to_flat_array()
+        return self.weights_to_flat_array(self.get_weights())
 
-    def set_weights(self, new_weights: Weights):
+    def reshape_flat_array_like(self, array, weights: List[np.ndarray]) -> List[np.ndarray]:
-        return self.model.set_weights(new_weights.layers)
+        return self.reshape_flat_array(array, self.shapes(weights))
 
-    @abstractmethod
+    def set_weights(self, new_weights: List[np.ndarray]):
-    def get_samples(self):
+        return self.model.set_weights(new_weights)
-        # TODO: add a dogstring, telling the user what this does, e.g. what is a sample?
-        raise NotImplementedError
 
-    @abstractmethod
+    def apply_to_network(self, other_network) -> List[np.ndarray]:
-    def apply_to_weights(self, old_weights) -> Weights:
+        """
-        # TODO: add a dogstring, telling the user what this does, e.g. what is applied?
+        Take a networks weights and apply _this_ networks function.
-        raise NotImplementedError
+        :param other_network:
-
+        :return:
-    def apply_to_network(self, other_network) -> Weights:
+        """
-        # TODO: add a dogstring, telling the user what this does, e.g. what is applied?
         new_weights = self.apply_to_weights(other_network.get_weights())
         return new_weights
 
-    def attack(self, other_network):
-        # TODO: add a dogstring, telling the user what this does, e.g. what is an attack?
-        other_network.set_weights(self.apply_to_network(other_network))
-        return self
-
-    def fuck(self, other_network):
-        # TODO: add a dogstring, telling the user what this does, e.g. what is fucking?
-        self.set_weights(self.apply_to_network(other_network))
-        return self
-
-    def self_attack(self, iterations=1):
-        # TODO: add a dogstring, telling the user what this does, e.g. what is self attack?
-        for _ in range(iterations):
-            self.attack(self)
-        return self
-
-    def meet(self, other_network):
-        # TODO: add a dogstring, telling the user what this does, e.g. what is meeting?
-        new_other_network = copy.deepcopy(other_network)
-        return self.attack(new_other_network)
-
     def is_diverged(self):
-        return self.get_weights().are_diverged()
+        return self.are_weights_diverged(self.get_weights())
 
     def is_zero(self, epsilon=None):
         epsilon = epsilon or self.get_params().get('epsilon')
-        return self.get_weights().are_within_bounds(-epsilon, epsilon)
+        return self.are_weights_within_bounds(self.get_weights(), -epsilon, epsilon)
 
     def is_fixpoint(self, degree: int = 1, epsilon: float = None) -> bool:
         assert degree >= 1, "degree must be >= 1"
         epsilon = epsilon or self.get_params().get('epsilon')
 
-        new_weights = copy.deepcopy(self.get_weights())
+        new_weights = deepcopy(self.get_weights())
 
         for _ in range(degree):
             new_weights = self.apply_to_weights(new_weights)
-            if new_weights.are_diverged():
+            if self.are_weights_diverged(new_weights):
                 return False
 
-        biggerEpsilon = (np.abs(new_weights.to_flat_array() - self.get_weights().to_flat_array()) >= epsilon).any()
+        flat_new = self.weights_to_flat_array(new_weights)
+        flat_old = self.weights_to_flat_array(self.get_weights())
+        biggerEpsilon = (np.abs(flat_new - flat_old) >= epsilon).any()
 
         # Boolean Value needs to be flipped to answer "is_fixpoint"
         return not biggerEpsilon
 
-    def print_weights(self, weights=None):
+    def aggregate_weights_by(self, weights: List[np.ndarray], func: FunctionType, num_aggregates: int):
-        print(weights or self.get_weights())
+        collection_sizes = self.get_weight_amount(weights) // num_aggregates
+        flat = self.weights_to_flat_array(weights)
+        array_for_aggregation = flat[:collection_sizes * num_aggregates].reshape((num_aggregates, -1))
+        left_overs = flat[collection_sizes * num_aggregates:]
+        aggregated_weights = func(array_for_aggregation, num_aggregates)
+        return aggregated_weights, left_overs
+
+    def shuffle_weights(self, weights: List[np.ndarray]):
+        flat = self.weights_to_flat_array(weights)
+        np.random.shuffle(flat)
+        return self.reshape_flat_array_like(flat, weights)
+
+    @abstractmethod
+    def get_samples(self, **kwargs):
+        # TODO: add a dogstring, telling the user what this does, e.g. what is a sample?
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply_to_weights(self, old_weights) -> List[np.ndarray]:
+        """
+        Take weights as inputs; retunr the evaluation of _this_ network.
+        "Apply this function".
+
+        :param old_weights:
+        :return:
+        """
+        raise NotImplementedError
 
 
 class ParticleDecorator:
@@ -273,6 +257,63 @@ class ParticleDecorator:
     def get_states(self):
         return self.states
 
+    def attack(self, other_network, iterations: int = 1):
+        """
+        Set a networks weights based on the output of the application of my function to its weights.
+        "Alter a networks weights based on my evaluation"
+        :param other_network:
+        :param iterations:
+        :return:
+        """
+        for _ in range(iterations):
+            other_network.set_weights(self.apply_to_network(other_network))
+        return self
+
+    def self_attack(self, iterations: int = 1):
+        """
+        Set my weights based on the output of the application of my function to its weights.
+        "Alter my network weights based on my evaluation"
+        :param iterations:
+        :return:
+        """
+        for _ in range(iterations):
+            self.attack(self)
+        return self
+
+
+class TaskDecorator(TaskAdditionOfN):
+
+    def __init__(self, network, **kwargs):
+        super(TaskDecorator, self).__init__(**kwargs)
+        self.network = network
+        self.batchsize = self.network.get_amount_of_weights()
+
+    def __getattr__(self, name):
+        return getattr(self.network, name)
+
+    def get_samples(self, task_samples=False, self_samples=False, **kwargs):
+        # XOR, cannot be true at the same time
+        assert not all([task_samples, self_samples])
+
+        if task_samples:
+            return super(TaskDecorator, self).get_samples()
+
+        elif self_samples:
+            return self.network.get_samples()
+
+        else:
+            self_x, self_y = self.network.get_samples()
+            # Super class = Task
+            task_x, task_y = super(TaskDecorator, self).get_samples()
+
+            amount_of_weights = self.network.get_amount_of_weights()
+            random_idx = np.random.choice(np.arange(amount_of_weights), amount_of_weights//2)
+
+            x = self_x[random_idx] = task_x[random_idx]
+            y = self_y[random_idx] = task_y[random_idx]
+
+            return x, y
+
 
 class WeightwiseNeuralNetwork(NeuralNetwork):
 
@@ -291,23 +332,23 @@ class WeightwiseNeuralNetwork(NeuralNetwork):
         # TODO: Write about it... What does it do?
         return self.model.predict(inputs)
 
-    def get_samples(self):
+    def get_samples(self, **kwargs: List[np.ndarray]):
-        weights = self.get_weights()
+        weights = kwargs.get('weights', self.get_weights())
         sample = np.asarray([
-            [weight, idx, *x] for idx, layer in enumerate(weights.layers) for x, weight in np.ndenumerate(layer)
+            [weight, idx, *x] for idx, layer in enumerate(weights) for x, weight in np.ndenumerate(layer)
         ])
         # normalize [layer, cell, position]
         for idx in range(1, sample.shape[1]):
             sample[:, idx] = sample[:, idx] / np.max(sample[:, idx])
-        return sample, sample
+        return sample, sample[:, 0]
 
-    def apply_to_weights(self, weights) -> Weights:
+    def apply_to_weights(self, weights) -> List[np.ndarray]:
         # ToDo: Insert DocString
         # Transform the weight matrix in an horizontal stack as: array([[weight, layer, cell, position], ...])
-        transformed_weights = self.get_samples()[0]
+        transformed_weights, _ = self.get_samples(weights=weights)
-        new_weights = self.apply(transformed_weights)
+        new_flat_weights = self.apply(transformed_weights)
         # use the original weight shape to transform the new tensor
-        return Weights(new_weights, flat_array_shape=weights.shapes())
+        return self.reshape_flat_array_like(new_flat_weights, weights)
 
 
 class AggregatingNeuralNetwork(NeuralNetwork):
@@ -329,11 +370,10 @@ class AggregatingNeuralNetwork(NeuralNetwork):
 
     @staticmethod
     def deaggregate_identically(aggregate, amount):
-        # ToDo: Find a better way than using the a hardcoded [0]
+        return np.repeat(aggregate, amount, axis=0)
-        return np.hstack([aggregate for _ in range(amount)])[0]
 
     @staticmethod
-    def shuffle_not(weights: Weights):
+    def shuffle_not(weights: List[np.ndarray]):
         """
         Doesn't do a thing. f(x)
 
@@ -344,9 +384,8 @@ class AggregatingNeuralNetwork(NeuralNetwork):
         """
         return weights
 
-    @staticmethod
+    def shuffle_random(self, weights: List[np.ndarray]):
-    def shuffle_random(weights: Weights):
+        weights = self.shuffle_weights(weights)
-        assert weights.shuffle()
         return weights
 
     def __init__(self, aggregates, width, depth, **kwargs):
@@ -369,17 +408,14 @@ class AggregatingNeuralNetwork(NeuralNetwork):
     def get_shuffler(self):
         return self.params.get('shuffler', self.shuffle_not)
 
-    def get_amount_of_weights(self):
-        return len(self.get_weights())
-
     def apply(self, inputs):
         # You need to add an dimension here... "..." copies array values
         return self.model.predict(inputs[None, ...])
 
     def get_aggregated_weights(self):
-        return self.get_weights().aggregate_by(self.get_aggregator(), self.aggregates)
+        return self.aggregate_weights_by(self.get_weights(), self.get_aggregator(), self.aggregates)
 
-    def apply_to_weights(self, old_weights) -> Weights:
+    def apply_to_weights(self, old_weights) -> List[np.ndarray]:
 
         # build aggregations of old_weights
         old_aggregations, leftovers = self.get_aggregated_weights()
@@ -390,14 +426,14 @@ class AggregatingNeuralNetwork(NeuralNetwork):
         new_aggregations = self.deaggregate_identically(new_aggregations, collection_sizes)
         # generate new weights
         # only include leftovers if there are some then coonvert them to Weight on base of th old shape
-        new_weights = Weights(new_aggregations if not leftovers.shape[0] else np.hstack((new_aggregations, leftovers)),
+        complete_weights = new_aggregations if not leftovers.shape[0] else np.hstack((new_aggregations, leftovers))
-                              flat_array_shape=old_weights.shapes())
+        new_weights = self.reshape_flat_array_like(complete_weights, old_weights)
 
         # maybe shuffle
         new_weights = self.get_shuffler()(new_weights)
         return new_weights
 
-    def get_samples(self):
+    def get_samples(self, **kwargs):
         aggregations, _ = self.get_aggregated_weights()
         # What did that do?
         # sample = np.transpose(np.array([[aggregations[i]] for i in range(self.aggregates)]))
@@ -408,11 +444,11 @@ class AggregatingNeuralNetwork(NeuralNetwork):
         epsilon = epsilon or self.get_params().get('epsilon')
 
         old_aggregations, _ = self.get_aggregated_weights()
-        new_weights = copy.deepcopy(self.get_weights())
+        new_weights = deepcopy(self.get_weights())
 
         for _ in range(degree):
             new_weights = self.apply_to_weights(new_weights)
-            if new_weights.are_diverged():
+            if self.are_weights_diverged(new_weights):
                 return False
 
         new_aggregations, leftovers = self.get_aggregated_weights()
@@ -427,7 +463,8 @@ class AggregatingNeuralNetwork(NeuralNetwork):
 class RecurrentNeuralNetwork(NeuralNetwork):
 
     def __init__(self, width, depth, **kwargs):
-        super().__init__(**kwargs)
+        raise NotImplementedError
+        super(RecurrentNeuralNetwork, self).__init__()
         self.features = 1
         self.width = width
         self.depth = depth
@@ -443,7 +480,7 @@ class RecurrentNeuralNetwork(NeuralNetwork):
 
     def apply_to_weights(self, old_weights):
         # build list from old weights
-        new_weights = copy.deepcopy(old_weights)
+        new_weights = deepcopy(old_weights)
         old_weights_list = []
         for layer_id, layer in enumerate(old_weights):
             for cell_id, cell in enumerate(layer):
@@ -500,7 +537,7 @@ class TrainingNeuralNetworkDecorator:
         return self
 
     def compile_model(self, **kwargs):
-        compile_params = copy.deepcopy(self.compile_params)
+        compile_params = deepcopy(self.compile_params)
         compile_params.update(kwargs)
         return self.network.model.compile(**compile_params)
 
@@ -510,13 +547,30 @@ class TrainingNeuralNetworkDecorator:
             self.model_compiled = True
         return self
 
-    def train(self, batchsize=1, store_states=True, epoch=0):
+    def train(self, batchsize=1, epoch=0):
         self.compiled()
         x, y = self.network.get_samples()
-        savestatecallback = [SaveStateCallback(network=self, epoch=epoch)] if store_states else None
+        callbacks = []
-        history = self.network.model.fit(x=x, y=y, epochs=epoch+1, verbose=0,
+        if self.get_params().get('store_states'):
-                                         batch_size=batchsize, callbacks=savestatecallback,
+            callbacks.append(SaveStateCallback(network=self, epoch=epoch))
-                                         initial_epoch=epoch)
+        if self.get_params().get('early_nan_stopping'):
+            callbacks.append(EarlyStoppingByInfNanLoss())
+
+        # 'or' does not work on empty lists
+        callbacks = callbacks if callbacks else None
+        """
+        Please Note:
+        
+        epochs: Integer. Number of epochs to train the model.
+            An epoch is an iteration over the entire `x` and `y`
+            data provided.
+            Note that in conjunction with `initial_epoch`,
+            `epochs` is to be understood as "final epoch".
+            The model is not trained for a number of iterations
+            given by `epochs`, but merely until the epoch
+            of index `epochs` is reached."""
+        history = self.network.model.fit(x=x, y=y, initial_epoch=epoch, epochs=epoch+1, verbose=0,
+                                         batch_size=batchsize, callbacks=callbacks)
         return history.history['loss'][-1]
 
     def learn_from(self, other_network, batchsize=1):
@@ -524,68 +578,81 @@ class TrainingNeuralNetworkDecorator:
         other_network.compiled()
         x, y = other_network.network.get_samples()
         history = self.network.model.fit(x=x, y=y, verbose=0, batch_size=batchsize)
-
         return history.history['loss'][-1]
 
+    def evaluate(self, x=None, y=None, batchsize=1):
+        self.compiled()
+        x, y = x, y if x is not None and y is not None else self.network.get_samples()
+        """
+        Please Note:
+
+        epochs: Integer. Number of epochs to train the model.
+            An epoch is an iteration over the entire `x` and `y`
+            data provided.
+            Note that in conjunction with `initial_epoch`,
+            `epochs` is to be understood as "final epoch".
+            The model is not trained for a number of iterations
+            given by `epochs`, but merely until the epoch
+            of index `epochs` is reached."""
+        loss = self.network.model.evaluate(x=x, y=y, verbose=0, batch_size=batchsize)
+        return loss
+
 
 if __name__ == '__main__':
 
     if True:
         # WeightWise Neural Network
-        net_generator = ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear'))
+        with TaskExperiment().with_params(application_steps=10, trains_per_application=1000, exp_iterations=30) as exp:
-        with FixpointExperiment() as exp:
+            net_generator = lambda: TrainingNeuralNetworkDecorator(TaskDecorator(
-            exp.run_exp(net_generator, 10, logging=True)
+                WeightwiseNeuralNetwork(width=2, depth=2))
-            exp.reset_all()
+            ).with_keras_params(activation='linear')
+            exp.run_exp(net_generator, reset_model=True)
 
     if False:
         # Aggregating Neural Network
-        net_generator = ParticleDecorator(AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params())
+        net_generator = lambda: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)
-        with FixpointExperiment() as exp:
+        with MixedFixpointExperiment() as exp:
-            exp.run_exp(net_generator, 10, logging=True)
+            exp.run_exp(net_generator, 10)
-
             exp.reset_all()
 
     if False:
         # FFT Aggregation
-        net_generator = lambda: ParticleDecorator(
+        net_generator = lambda: AggregatingNeuralNetwork(
-            AggregatingNeuralNetwork(
+            aggregates=4, width=2, depth=2, aggregator=AggregatingNeuralNetwork.aggregate_fft)
-                aggregates=4, width=2, depth=2, aggregator=AggregatingNeuralNetwork.aggregate_fft
-            ).with_keras_params(activation='linear'))
         with FixpointExperiment() as exp:
-            for run_id in tqdm(range(10)):
+            exp.run_exp(net_generator, 10)
-                exp.run_exp(net_generator, 1)
+            exp.log(exp.counters)
-                exp.log(exp.counters)
+            exp.reset_model()
-                exp.reset_model()
             exp.reset_all()
 
-    if True:
+    if False:
         # ok so this works quite realiably
-        run_count = 10000
+        run_count = 1000
-        net_generator = TrainingNeuralNetworkDecorator(
+        net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(
-            ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2))
+            width=2, depth=2).with_params(epsilon=0.0001)).with_keras_params(optimizer='sgd')
-        ).with_params(epsilon=0.0001).with_keras_params(optimizer='sgd')
         with MixedFixpointExperiment() as exp:
             for run_id in tqdm(range(run_count+1)):
                 exp.run_exp(net_generator, 1)
                 if run_id % 100 == 0:
-                    exp.run_net(net_generator, 1)
+                    exp.run_exp(net_generator, 1)
             K.clear_session()
 
     if False:
         with FixpointExperiment() as exp:
-            run_count = 1000
+            run_count = 100
-            net = TrainingNeuralNetworkDecorator(AggregatingNeuralNetwork(4, width=2, depth=2)).with_params(epsilon=0.1e-6)
+            net = TrainingNeuralNetworkDecorator(
+                AggregatingNeuralNetwork(4, width=2, depth=2).with_params(epsilon=0.1e-6))
             for run_id in tqdm(range(run_count+1)):
-                loss = net.compiled().train()
+                current_loss = net.compiled().train()
                 if run_id % 100 == 0:
                     net.print_weights()
-                    old_aggs, _ = net.net.get_aggregated_weights()
+                    old_aggs, _ = net.get_aggregated_weights()
                     print("old weights agg: " + str(old_aggs))
-                    fp, new_aggs = net.net.is_fixpoint_after_aggregation(epsilon=0.0001)
+                    fp, new_aggs = net.is_fixpoint_after_aggregation(epsilon=0.0001)
                     print("new weights agg: " + str(new_aggs))
                     print("Fixpoint? " + str(net.is_fixpoint()))
                     print("Fixpoint after Agg? " + str(fp))
-                    print("Loss " + str(loss))
+                    print("Loss " + str(current_loss))
                     print()
 
     if False:
@@ -593,26 +660,13 @@ if __name__ == '__main__':
         # TODO: Wtf is happening here?
         with FixpointExperiment() as exp:
             run_count = 10000
-            net = TrainingNeuralNetworkDecorator(RecurrentNeuralNetwork(width=2, depth=2))\
+            net = TrainingNeuralNetworkDecorator(RecurrentNeuralNetwork(width=2, depth=2)
-                .with_params(epsilon=0.1e-2).with_keras_params(optimizer='sgd', activation='linear')
+                                                 ).with_keras_params(optimizer='sgd', activation='linear')
             for run_id in tqdm(range(run_count+1)):
-                loss = net.compiled().train()
+                current_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("Loss " + str(current_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/plotting/plotting_class.py

@@ -0,0 +1,66 @@
+import os
+
+from experiment import Experiment
+# noinspection PyUnresolvedReferences
+from soup import Soup
+
+from argparse import ArgumentParser
+import numpy as np
+
+import plotly as pl
+import plotly.graph_objs as go
+
+import colorlover as cl
+
+import dill
+
+from sklearn.manifold.t_sne import TSNE, PCA
+
+
+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()
+
+
+class DataPlotter:
+
+    def __init__(self, path=None):
+        self.path = path or os.getcwd()
+        pass
+
+    def search_and_apply(self, plotting_function, files_to_look_for=None, absolut_file_or_folder=None):
+        absolut_file_or_folder, files_to_look_for = self.path or absolut_file_or_folder, list() or files_to_look_for
+        if os.path.isdir(absolut_file_or_folder):
+            for sub_file_or_folder in os.scandir(absolut_file_or_folder):
+                self.search_and_apply(plotting_function, files_to_look_for=files_to_look_for,
+                                      absolut_file_or_folder=sub_file_or_folder.path)
+        elif absolut_file_or_folder.endswith('.dill'):
+            file_or_folder = os.path.split(absolut_file_or_folder)[-1]
+            if file_or_folder in files_to_look_for and not os.path.exists(
+                    '{}.html'.format(absolut_file_or_folder[:-5])):
+                print('Apply Plotting function "{func}" on file "{file}"'.format(func=plotting_function.__name__,
+                                                                                 file=absolut_file_or_folder)
+                      )
+                with open(absolut_file_or_folder, 'rb') as in_f:
+                    exp = dill.load(in_f)
+
+                names_dill_location = os.path.join(*os.path.split(absolut_file_or_folder)[:-1], 'all_names.dill')
+                with open(names_dill_location, 'rb') as in_f:
+                    names = dill.load(in_f)
+
+                try:
+                    plotting_function((names, exp), filename='{}.html'.format(absolut_file_or_folder[:-5]))
+                except ValueError:
+                    pass
+                except AttributeError:
+                    pass
+            else:
+                # This was either another FilyType or Plot.html already exists.
+                pass
+
+
+if __name__ == '__main__':
+    plotter = DataPlotter
+    pass

code/plotting/task_learning_curves.py

@@ -0,0 +1,109 @@
+import os
+from collections import defaultdict
+
+# noinspection PyUnresolvedReferences
+from soup import Soup
+from experiment import TaskExperiment
+
+from argparse import ArgumentParser
+
+import plotly as pl
+import plotly.graph_objs as go
+
+import colorlover as cl
+
+import dill
+import numpy as np
+
+
+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 line_plot(exp: TaskExperiment, filename='lineplot'):
+    assert isinstance(exp, TaskExperiment), ' This has to be a TaskExperiment!'
+    traces, data = [], defaultdict(list)
+
+    color_scale = cl.scales['3']['div']['RdYlBu']
+
+    # Sort data per Key
+    for message in exp.log_messages:
+        for key in message.keys():
+            try:
+                data[key].append(-0.1 if np.isnan(message[key]) or np.isinf(message[key]) else message[key])
+            except:
+                data[key].append(message[key])
+
+    for line_id, key in enumerate(data.keys()):
+        if key not in ['counters', 'id']:
+            trace = go.Scatter(
+                x=[x for x in range(len(data[key]))],
+                y=data[key],
+                name=key,
+                line=dict(
+                    color=color_scale[line_id],
+                    width=5
+                ),
+            )
+
+            traces.append(trace)
+        else:
+            continue
+
+    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=traces, layout=layout)
+    pl.offline.plot(fig, auto_open=True, filename=filename)
+    pass
+
+
+def search_and_apply(absolut_file_or_folder, plotting_function, files_to_look_for=None, override=False):
+    # ToDo: Clean this Mess
+    assert os.path.exists(absolut_file_or_folder), f'The given path does not exist! Given: {absolut_file_or_folder}'
+    files_to_look_for = files_to_look_for or list()
+    if os.path.isdir(absolut_file_or_folder):
+        for sub_file_or_folder in os.scandir(absolut_file_or_folder):
+            search_and_apply(sub_file_or_folder.path, plotting_function,
+                             files_to_look_for=files_to_look_for, override=override)
+    elif absolut_file_or_folder.endswith('.dill'):
+        file_or_folder = os.path.split(absolut_file_or_folder)[-1]
+        if file_or_folder in files_to_look_for or not files_to_look_for:
+            if not os.path.exists('{}.html'.format(absolut_file_or_folder[:-5])) or override:
+                print('Apply Plotting function "{func}" on file "{file}"'.format(func=plotting_function.__name__,
+                                                                                 file=absolut_file_or_folder)
+                      )
+                with open(absolut_file_or_folder, 'rb') as in_f:
+                    exp = dill.load(in_f)
+
+                try:
+                    plotting_function(exp, filename='{}.html'.format(absolut_file_or_folder[:-5]))
+                except ValueError:
+                    pass
+                except AttributeError:
+                    pass
+            else:
+                # Plot.html already exists.
+                pass
+        else:
+            # This was a wrong FilyType.
+            pass
+
+
+if __name__ == '__main__':
+    args = build_args()
+    in_file = args.in_file[0]
+    out_file = args.out_file
+
+    search_and_apply(in_file, line_plot, override=True)

code/setups/applying-fixpoints.py

@@ -7,12 +7,13 @@ sys.path += os.path.join('..', '.')
 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=[]):
+
+def count(counters, net, notable_nets: list=None):
+    notable_nets = notable_nets or list()
     if net.is_diverged():
         counters['divergent'] += 1
     elif net.is_fixpoint():
@@ -31,7 +32,7 @@ def count(counters, net, notable_nets=[]):
 
 if __name__ == '__main__':
 
-    with Experiment('applying_fixpoint') as exp:
+    with FixpointExperiment(name='applying_fixpoint') as exp:
         exp.trials = 50
         exp.run_count = 100
         exp.epsilon = 1e-4
@@ -40,7 +41,7 @@ if __name__ == '__main__':
             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)]
+        #       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 = []
@@ -50,14 +51,14 @@ if __name__ == '__main__':
             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'))
+                name = str(net.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.reset_model()
         exp.save(all_counters=all_counters)
         exp.save(trajectorys=exp.without_particles())
         # net types reached in the end

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/learn_from_soup.py

@@ -11,7 +11,7 @@ from network import *
 from soup import *
 
 
-import tensorflow.python.keras.backend as K
+from tensorflow.python.keras import backend as K
 
 from statistics import mean
 avg = mean
@@ -59,7 +59,7 @@ def count(counters, soup, notable_nets=None):
 
 if __name__ == '__main__':
 
-    with SoupExperiment('learn-from-soup') as exp:
+    with SoupExperiment(name='learn-from-soup') as exp:
         exp.soup_size = 10
         exp.soup_life = 100
         exp.trials = 10
@@ -83,10 +83,10 @@ if __name__ == '__main__':
                 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 = 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'))
+                    name = str(soup.particles[0].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)

code/setups/mixed-self-fixpoints.py

@@ -9,8 +9,6 @@ sys.path += os.path.join('..', '.')
 from experiment import *
 from network import *
 
-import tensorflow.python.keras.backend as K
-
 
 def generate_counters():
     """
@@ -84,7 +82,7 @@ if __name__ == '__main__':
                         if net.is_diverged() or net.is_fixpoint():
                             break
                     count(counters, net, notable_nets)
-                    keras.backend.clear_session()
+                    exp.reset_model()
                 xs += [trains_per_selfattack]
                 ys += [float(counters['fix_zero'] + counters['fix_other']) / float(exp.trials)]
             all_names += [name]

code/setups/network_trajectorys.py

@@ -104,7 +104,7 @@ if __name__ == '__main__':
             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)
+                exp.run_net(net)
 
                 net.print_weights()
 

code/setups/soup_trajectorys.py

@@ -10,23 +10,23 @@ from experiment import *
 
 if __name__ == '__main__':
     if True:
-        with SoupExperiment("soup") as exp:
+        with SoupExperiment(namne="soup") as exp:
-            for run_id in range(1):
+            net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)) \
-                net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)) \
+                 .with_keras_params(activation='linear').with_params(epsilon=0.0001)
-                     .with_keras_params(activation='linear').with_params(epsilon=0.0001)
+            # net_generator = lambda: TrainingNeuralNetworkDecorator(AggregatingNeuralNetwork(4, 2, 2))\
-                # net_generator = lambda: TrainingNeuralNetworkDecorator(AggregatingNeuralNetwork(4, 2, 2))\
+            #     .with_keras_params(activation='linear')
-                #     .with_keras_params(activation='linear')
+            # net_generator = lambda: TrainingNeuralNetworkDecorator(FFTNeuralNetwork(4, 2, 2))\
-                # net_generator = lambda: TrainingNeuralNetworkDecorator(FFTNeuralNetwork(4, 2, 2))\
+            #    .with_keras_params(activation='linear')
-                #    .with_keras_params(activation='linear')
+            # net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
-                # 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,
-                soup = Soup(20, net_generator).with_params(remove_divergent=True, remove_zero=True,
+                                                       train=30,
-                                                           train=30,
+                                                       learn_from_rate=-1)
-                                                           learn_from_rate=-1)
+            soup.seed()
-                soup.seed()
+            for _ in tqdm(range(100)):
-                for _ in tqdm(range(100)):
+                soup.evolve()
-                    soup.evolve()
+            exp.log(soup.count())
-                exp.log(soup.count())
+            # you can access soup.historical_particles[particle_uid].states[time_step]['loss']
-                # you can access soup.historical_particles[particle_uid].states[time_step]['loss']
+            #             or soup.historical_particles[particle_uid].states[time_step]['weights']
-                #             or soup.historical_particles[particle_uid].states[time_step]['weights']
+            # from soup.dill
-                # from soup.dill
+            exp.save(soup=soup.without_particles())
-                exp.save(soup=soup.without_particles())
+            K.clear_session()

code/soup.py

@@ -1,85 +1,140 @@
 import random
+from tensorflow.python.keras.layers import Dense, Dropout, BatchNormalization
+from tensorflow.python.keras.layers import Input, Layer, Concatenate, RepeatVector, Reshape
+from tensorflow.python.keras.models import Sequential, Model
+from tensorflow.python.keras import backend as K
 
-from network import *
+from typing import List, Tuple
+
+# Functions and Operators
+from operator import mul
+from functools import reduce
+from itertools import accumulate
+
+import numpy as np
+
+from task import Task, TaskAdditionOfN
+
+from copy import copy, deepcopy
+
+from network import ParticleDecorator, WeightwiseNeuralNetwork, TrainingNeuralNetworkDecorator, \
+    EarlyStoppingByInfNanLoss
+
+from experiment import TaskingSoupExperiment
+
+from math import sqrt
 
 
 def prng():
     return random.random()
 
 
+class SlicingLayer(Layer):
+
+    def __init__(self):
+        self.kernel: None
+        self.inputs: int
+        super(SlicingLayer, self).__init__()
+
+    def build(self, input_shape):
+        # Create a trainable weight variable for this layer.
+        self.kernel = None
+        self.inputs = input_shape[-1]
+        super(SlicingLayer, self).build(input_shape)  # Be sure to call this at the end
+
+    def call(self, x, **kwargs):
+        concats = [Concatenate()([x[:, i][..., None]] * self.inputs) for i in range(x.shape[-1].value)]
+        return concats
+
+    def compute_output_shape(self, input_shape):
+        return [Concatenate()([(None, 1)] * 4) for _ in range(input_shape[-1])]
+
+
 class Soup(object):
-    
+
     def __init__(self, size, generator, **kwargs):
         self.size = size
         self.generator = generator
         self.particles = []
         self.historical_particles = {}
-        self.params = dict(attacking_rate=0.1, learn_from_rate=0.1, train=0, learn_from_severity=1)
+        self.soup_params = dict(attacking_rate=0.1, learn_from_rate=0.1, train=0, learn_from_severity=1)
-        self.params.update(kwargs)
+        self.soup_params.update(kwargs)
         self.time = 0
+        self.is_seeded = False
+        self.is_compiled = False
+
+    def __len__(self):
+        return len(self.particles)
 
     def __copy__(self):
-        copy_ = Soup(self.size, self.generator, **self.params)
+        copy_ = Soup(self.size, self.generator, **self.soup_params)
         copy_.__dict__ = {attr: self.__dict__[attr] for attr in self.__dict__ if
                           attr not in ['particles', 'historical_particles']}
         return copy_
 
     def without_particles(self):
-        self_copy = copy.copy(self)
+        self_copy = copy(self)
         # self_copy.particles = [particle.states for particle in self.particles]
         self_copy.historical_particles = {key: val.states for key, val in self.historical_particles.items()}
         return self_copy
 
-    def with_params(self, **kwargs):
+    def with_soup_params(self, **kwargs):
-        self.params.update(kwargs)
+        self.soup_params.update(kwargs)
         return self
-    
+
     def generate_particle(self):
         new_particle = ParticleDecorator(self.generator())
         self.historical_particles[new_particle.get_uid()] = new_particle
         return new_particle
-        
+
     def get_particle(self, uid, otherwise=None):
         return self.historical_particles.get(uid, otherwise)
-    
+
     def seed(self):
-        self.particles = []
+        if not self.is_seeded:
-        for _ in range(self.size):
+            self.particles = []
-            self.particles += [self.generate_particle()]
+            for _ in range(self.size):
-        return self       
+                self.particles += [self.generate_particle()]
-    
+        else:
+            print('already seeded!')
+        self.is_seeded = True
+        return self
+
     def evolve(self, iterations=1):
         for _ in range(iterations):
             self.time += 1
             for particle_id, particle in enumerate(self.particles):
                 description = {'time': self.time}
-                if prng() < self.params.get('attacking_rate'):
+                if prng() < self.soup_params.get('attacking_rate'):
                     other_particle_id = int(prng() * len(self.particles))
                     other_particle = self.particles[other_particle_id]
                     particle.attack(other_particle)
                     description['action'] = 'attacking'
                     description['counterpart'] = other_particle.get_uid()
-                if prng() < self.params.get('learn_from_rate'):
+
+                if prng() < self.soup_params.get('learn_from_rate'):
                     other_particle_id = int(prng() * len(self.particles))
                     other_particle = self.particles[other_particle_id]
-                    for _ in range(self.params.get('learn_from_severity', 1)):
+                    for _ in range(self.soup_params.get('learn_from_severity', 1)):
                         particle.learn_from(other_particle)
                     description['action'] = 'learn_from'
                     description['counterpart'] = other_particle.get_uid()
-                for _ in range(self.params.get('train', 0)):
+
-                    particle.compiled()
+                for _ in range(self.soup_params.get('train', 0)):
                     # callbacks on save_state are broken for TrainingNeuralNetwork
                     loss = particle.train(store_states=False)
-                    description['fitted'] = self.params.get('train', 0)
+                    description['fitted'] = self.soup_params.get('train', 0)
                     description['loss'] = loss
                     description['action'] = 'train_self'
                     description['counterpart'] = None
-                if self.params.get('remove_divergent') and particle.is_diverged():
+
+                if self.soup_params.get('remove_divergent') and particle.is_diverged():
                     new_particle = self.generate_particle()
                     self.particles[particle_id] = new_particle
                     description['action'] = 'divergent_dead'
                     description['counterpart'] = new_particle.get_uid()
-                if self.params.get('remove_zero') and particle.is_zero():
+
+                if self.soup_params.get('remove_zero') and particle.is_zero():
                     new_particle = self.generate_particle()
                     self.particles[particle_id] = new_particle
                     description['action'] = 'zweo_dead'
@@ -101,37 +156,221 @@ class Soup(object):
             else:
                 counters['other'] += 1
         return counters
-        
+
     def print_all(self):
         for particle in self.particles:
             particle.print_weights()
             print(particle.is_fixpoint())
 
 
+class TaskingSoup(Soup):
+
+    @staticmethod
+    def weights_to_flat_array(weights: List[np.ndarray]) -> np.ndarray:
+        return np.concatenate([d.ravel() for d in weights])
+
+    @staticmethod
+    def reshape_flat_array(array, shapes: List[Tuple[int]]) -> List[np.ndarray]:
+        # Same thing, but with an additional np call
+        # sizes: List[int] = [int(np.prod(shape)) for shape in shapes]
+
+        sizes = [reduce(mul, shape) for shape in shapes]
+        # Split the incoming array into slices for layers
+        slices = [array[x: y] for x, y in zip(accumulate([0] + sizes), accumulate(sizes))]
+        # reshape them in accordance to the given shapes
+        weights = [np.reshape(weight_slice, shape) for weight_slice, shape in zip(slices, shapes)]
+        return weights
+
+    def __init__(self, population_size: int, task: Task, particle_generator, sparsity_rate=0.1, use_bias=False,
+                 safe=True, **kwargs):
+
+        if safe:
+            input_shape_error_message = f'The population size must be devideable by {task.input_shape[-1]}'
+            assert population_size % task.input_shape[-1] == 0, input_shape_error_message
+            assert population_size % 2 == 0, 'The population size needs to be of even value'
+
+        super(TaskingSoup, self).__init__(population_size, particle_generator, **kwargs)
+        self.task = task
+        self.model: Sequential
+
+        self.network_params = dict(sparsity_rate=sparsity_rate, early_nan_stopping=True, use_bias=use_bias,
+                                   depth=population_size // task.input_shape[-1])
+        self.network_params.update(kwargs.get('network_params', {}))
+        self.compile_params = dict(loss='mse', optimizer='sgd')
+        self.compile_params.update(kwargs.get('compile_params', {}))
+
+    def with_network_params(self, **params):
+        self.network_params.update(params)
+
+    def _generate_model(self):
+        particle_idx_list = list(range(len(self)))
+        particles_per_layer = len(self) // self.network_params.get('depth')
+        task_input = Input(self.task.input_shape, name='Task_Input')
+        # First layer, which is conected to the input layer and independently trainable / not trainable at all.
+        input_neurons = particles_per_layer * self.task.output_shape
+        x = Dense(input_neurons, use_bias=self.network_params.get('use_bias'))(task_input)
+        x = SlicingLayer()(x)
+
+        for layer_num in range(self.network_params.get('depth')):
+            # This needs to be tensors, because particles come as keras models that applicable
+            x = [self.particles[layer_num*particles_per_layer + i].get_model()(x[i]) for
+                 i in range(particles_per_layer)]
+            x = [RepeatVector(particles_per_layer)(x[i]) for i in range(particles_per_layer)]
+            x = [Reshape((particles_per_layer,))(x[i]) for i in range(particles_per_layer)]
+        x = Concatenate()(x)
+        x = Dense(self.task.output_shape, use_bias=self.network_params.get('use_bias'), activation='linear')(x)
+
+        model = Model(inputs=task_input, outputs=x)
+        return model
+
+    def get_weights(self):
+        return self.model.get_weights()
+
+    def set_weights(self, weights: List[np.ndarray]):
+        self.model.set_weights(weights)
+
+    def set_intermediate_weights(self, weights: List[np.ndarray]):
+        all_weights = self.get_weights()
+        all_weights[1:-1] = weights
+        self.set_weights(all_weights)
+
+    def get_intermediate_weights(self):
+        return self.get_weights()[1:-1]
+
+    def seed(self):
+        K.clear_session()
+        self.is_compiled = False
+        super(TaskingSoup, self).seed()
+        self.model = self._generate_model()
+        pass
+
+    def compile_model(self, **kwargs):
+        if not self.is_compiled:
+            compile_params = deepcopy(self.compile_params)
+            compile_params.update(kwargs)
+            return self.model.compile(**compile_params)
+        else:
+            raise BrokenPipeError('This Model is not compiled yet! Something went wrong in the Pipeline!')
+
+    def get_total_weight_amount(self):
+        if self.is_seeded:
+            return sum([x.get_amount_of_weights() for x in self.particles])
+        else:
+            return 0
+
+    def get_shapes(self):
+        return [x.shape for x in self.get_weights()]
+
+    def get_intermediate_shapes(self):
+        weights = [x.shape for x in self.get_weights()]
+        return weights[2:-2] if self.network_params.get('use_bias') else weights[1:-1]
+
+    def predict(self, x):
+        return self.model.predict(x)
+
+    def evolve(self, iterations=1):
+        for iteration in range(iterations):
+            super(TaskingSoup, self).evolve(iterations=1)
+            self.train_particles()
+
+    def get_particle_weights(self):
+        return np.concatenate([x.get_weights_flat() for x in self.particles])
+
+    def get_particle_input_shape(self):
+        if self.is_seeded:
+            return tuple([x if x else -1 for x in self.particles[0].get_model().input_shape])
+        else:
+            return -1
+
+    def set_particle_weights(self, weights):
+        particle_weight_shape = self.particles[0].shapes(self.particles[0].get_weights())
+        sizes = [x.get_amount_of_weights() for x in self.particles]
+        flat_weights = self.weights_to_flat_array(weights)
+        slices = [flat_weights[x: y] for x, y in zip(accumulate([0] + sizes), accumulate(sizes))]
+        for particle, slice in zip(self.particles, slices):
+            new_weights = self.reshape_flat_array(slice, particle_weight_shape)
+            particle.set_weights(new_weights)
+        return True
+
+    def compiled(self, **kwargs):
+        if not self.is_compiled:
+            self.compile_model(**kwargs)
+            self.is_compiled = True
+        return self
+
+    def train(self, batchsize=1, epoch=0):
+        self.compiled()
+        x, y = self.task.get_samples()
+        callbacks = []
+        if self.network_params.get('early_nan_stopping'):
+            callbacks.append(EarlyStoppingByInfNanLoss())
+
+        # 'or' does not work on empty lists
+        callbacks = callbacks if callbacks else None
+        """
+        Please Note:
+
+        epochs: Integer. Number of epochs to train the model.
+            An epoch is an iteration over the entire `x` and `y`
+            data provided.
+            Note that in conjunction with `initial_epoch`,
+            `epochs` is to be understood as "final epoch".
+            The model is not trained for a number of iterations
+            given by `epochs`, but merely until the epoch
+            of index `epochs` is reached."""
+        history = self.model.fit(x=x, y=y, initial_epoch=epoch, epochs=epoch + 1, verbose=0,
+                                 batch_size=batchsize, callbacks=callbacks)
+        return history.history['loss'][-1]
+
+    def train_particles(self, **kwargs):
+        self.compiled()
+        weights = self.get_particle_weights()
+        shaped_weights = self.reshape_flat_array(weights, self.get_intermediate_shapes())
+        self.set_intermediate_weights(shaped_weights)
+        _ = self.train(**kwargs)  # This returns the loss values
+        new_weights = self.get_intermediate_weights()
+        self.set_particle_weights(new_weights)
+        return
+
+
 if __name__ == '__main__':
     if True:
-        net_generator = lambda: WeightwiseNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
+        from task import TaskAdditionOfN
-        soup_generator = Soup(100, net_generator).with_params(remove_divergent=True, remove_zero=True)
-        exp = SoupExperiment()
-        exp.run_exp(net_generator, 1000, soup_generator, 1, False)
 
-        # net_generator = lambda: FFTNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
+        net_generator = lambda: TrainingNeuralNetworkDecorator(
-        # net_generator = lambda: AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='sigmoid')\
+            WeightwiseNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
-        # .with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
+        )
-        # net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
+        soup_generator = lambda: TaskingSoup(20, TaskAdditionOfN(4), net_generator)
+        with TaskingSoupExperiment(soup_generator, name='solving_soup') as exp:
+            exp.run_exp(reset_model=False)
 
-    if True:
+    if False:
-        net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)) \
+        soup_generator = lambda: Soup(10, net_generator).with_soup_params(remove_divergent=True, remove_zero=True)
-            .with_keras_params(activation='linear').with_params(epsilon=0.0001)
+        with SoupExperiment(soup_generator, name='soup') as exp:
-        soup_generator = lambda: Soup(100, net_generator).with_params(remove_divergent=True, remove_zero=True, train=20)
+            net_generator = lambda: TrainingNeuralNetworkDecorator(
-        exp = SoupExperiment(name="soup")
+                WeightwiseNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
+            )
 
-        exp.run_exp(net_generator, 100, soup_generator, 1, False)
+            exp.run_exp(net_generator)
+
+            # net_generator = lambda: FFTNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
+            # net_generator = lambda: AggregatingNeuralNetwork(4, 2, 2).with_keras_params(activation='sigmoid')\
+            # .with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
+            # net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
+
+    if False:
+        soup_generator = lambda: Soup(10, net_generator).with_soup_params(remove_divergent=True, remove_zero=True)
+        with SoupExperiment(soup_generator, name='soup') as exp:
+            net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)) \
+                .with_keras_params(activation='linear').with_params(epsilon=0.0001)
+
+            exp.run_exp(net_generator)
+
+            # net_generator = lambda: TrainingNeuralNetworkDecorator(AggregatingNeuralNetwork(4, 2, 2))
+            # .with_keras_params(activation='linear')\
+            # .with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
+            # net_generator = lambda: TrainingNeuralNetworkDecorator(FFTNeuralNetwork(4, 2, 2))\
+            #     .with_keras_params(activation='linear')\
+            # .with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
+            # net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
 
-        # net_generator = lambda: TrainingNeuralNetworkDecorator(AggregatingNeuralNetwork(4, 2, 2))
-        # .with_keras_params(activation='linear')\
-        # .with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
-        # net_generator = lambda: TrainingNeuralNetworkDecorator(FFTNeuralNetwork(4, 2, 2))\
-        #     .with_keras_params(activation='linear')\
-        # .with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
-        # net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()

code/task.py

@@ -0,0 +1,32 @@
+from abc import ABC, abstractmethod
+import numpy as np
+
+from typing import Tuple
+
+
+class Task(ABC):
+
+    def __init__(self, input_shape, output_shape, **kwargs):
+        assert any([x not in kwargs.keys() for x in ["input_shape", "output_shape"]]), 'Dublicated arguments were given'
+        self.input_shape = input_shape
+        self.output_shape = output_shape
+        self.batchsize = kwargs.get('batchsize', 100)
+
+    def get_samples(self) -> Tuple[np.ndarray, np.ndarray]:
+        raise NotImplementedError
+
+
+class TaskAdditionOfN(Task):
+
+    def __init__(self, n: int, input_shape=(4,), output_shape=1, **kwargs):
+        assert any([x not in kwargs.keys() for x in ["input_shape", "output_shape"]]), 'Dublicated arguments were given'
+        assert n <= input_shape[0], f'You cannot Add more values (n={n}) than your input is long (in={input_shape}).'
+        kwargs.update(input_shape=input_shape, output_shape=output_shape)
+        super(TaskAdditionOfN, self).__init__(**kwargs)
+        self.n = n
+
+    def get_samples(self) -> Tuple[np.ndarray, np.ndarray]:
+        x = np.zeros((self.batchsize, *self.input_shape))
+        x[:, :self.n] = np.random.standard_normal((self.batchsize, self.n)) * 0.5
+        y = np.sum(x, axis=1)
+        return x, y