TaskDecorator, Tasks and Experiments

code/experiment.py

@@ -24,7 +24,7 @@ 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
@@ -73,11 +73,11 @@ class Experiment(ABC):
         raise NotImplementedError
         pass
 
-    def run_exp(self, network_generator, exp_iterations, step_limit=100, prints=False, reset_model=False):
+    def run_exp(self, network_generator, exp_iterations, step_limit=100, prints=False, 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):
             network = network_generator()
-            self.run_net(network, step_limit, run_id=run_id + 1)
+            self.run_net(network, step_limit, run_id=run_id + 1, **kwargs)
             self.historical_particles[run_id] = network
             if prints:
                 print("Fixpoint? " + str(network.is_fixpoint()))
@@ -96,11 +96,12 @@ class FixpointExperiment(Experiment):
         self.counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
         self.interesting_fixpoints = []
 
-    def run_exp(self, network_generator, exp_iterations, logging=True, **kwargs):
+    def run_exp(self, network_generator, exp_iterations, logging=True, reset_model=False, **kwargs):
         kwargs.update(reset_model=False)
         super(FixpointExperiment, self).run_exp(network_generator, exp_iterations, **kwargs)
         if logging:
             self.log(self.counters)
+        if reset_model:
             self.reset_model()
 
     def run_net(self, net, step_limit=100, run_id=0, **kwargs):
@@ -109,7 +110,7 @@ class FixpointExperiment(Experiment):
         for i in range(step_limit):
             if net.is_diverged() or net.is_fixpoint():
                 break
-            net.self_attack()
+            net.set_weights(net.apply_to_weights(net.get_weights()))
             if run_id:
                 net.save_state(time=i)
         self.count(net)
@@ -141,30 +142,69 @@ class FixpointExperiment(Experiment):
 class MixedFixpointExperiment(FixpointExperiment):
 
     def __init__(self, **kwargs):
-        super(MixedFixpointExperiment, self).__init__(name=kwargs.get('name', self.__class__.__name__))
+        kwargs['name'] = self.__class__.__name__ if 'name' not in kwargs else kwargs['name']
+        super(MixedFixpointExperiment, self).__init__(**kwargs)
 
-    def run_net(self, net, step_limit=100, run_id=0, **kwargs):
+    def run_net(self, net, step_limit=100, run_id=0, trains_per_application=100, **kwargs):
-        for i in range(step_limit):
+        assert hasattr(net, 'train'), 'This Network must be trainable, i.e. use the "TrainingNeuralNetworkDecorator"!'
+
+        for evolution_step in range(step_limit):
+            net.set_weights(net.apply_to_weights(net.get_weights()))
             if net.is_diverged() or net.is_fixpoint():
                 break
-            net.self_attack()
+            epoch_num = run_id * trains_per_application * evolution_step
-            with tqdm(postfix=["Loss", dict(value=0)]) as bar:
+            with tqdm(postfix={"epoch": 0, "loss": 0, None: None},
-                for _ in range(kwargs.get('trains_per_application', 100)):
+                      bar_format="This Epoch:{postfix[epoch]} Loss: {postfix[loss]}%|{r_bar}") as bar:
-                    loss = net.train()
+                for epoch in range(epoch_num, epoch_num + trains_per_application):
-                    bar.postfix[1]["value"] = loss
+                    loss = net.train(epoch=epoch)
+                    bar.postfix.update(epoch=epoch, loss=loss)
                     bar.update()
-            if run_id:
+            if run_id and hasattr(net, 'save_sate'):
                 net.save_state()
         self.count(net)
 
 
+class TaskExperiment(MixedFixpointExperiment):
+
+    def __init__(self, **kwargs):
+        kwargs['name'] = self.__class__.__name__ if 'name' not in kwargs else kwargs['name']
+        super(TaskExperiment, self).__init__(**kwargs)
+        self.task_performance = []
+        self.self_performance = []
+
+    def run_exp(self, network_generator, exp_iterations, logging=True, reset_model=False, **kwargs):
+        kwargs.update(reset_model=False, logging=logging)
+        super(FixpointExperiment, self).run_exp(network_generator, exp_iterations, **kwargs)
+        if reset_model:
+            self.reset_model()
+        pass
+
+    def run_net(self, net, step_limit=100, run_id=0, **kwargs):
+        assert hasattr(net, 'evaluate')
+        kwargs.update(step_limit=step_limit, run_id=run_id)
+        super(TaskExperiment, self).run_net(net, **kwargs)
+
+        # Get Performance without Training
+        selfX, selfY = net.get_samples(self_samples=True)
+
+        self.task_performance.append(net.evaluate(*net.get_samples(task_samples=True),
+                                                  batchsize=net.get_amount_of_weights()))
+        self.self_performance.append(net.evaluate(*net.get_samples(self_samples=True),
+                                                  batchsize=net.get_amount_of_weights()))
+        pass
+
+
 class SoupExperiment(Experiment):
 
     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(SoupExperiment, 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, exp_iterations,
+                soup_generator=None, soup_iterations=0, prints=False, **kwargs):
         for i in range(soup_iterations):
+            if not soup_generator:
+                raise ValueError('A Soup Generator needs to be given!')
             soup = soup_generator()
             soup.seed()
             for _ in tqdm(range(exp_iterations)):
@@ -181,7 +221,8 @@ class SoupExperiment(Experiment):
 class IdentLearningExperiment(Experiment):
 
     def __init__(self, **kwargs):
-        super(IdentLearningExperiment, self).__init__(name=kwargs.get('name', self.__class__.__name__))
+        kwargs['name'] = self.__class__.__name__ if 'name' not in kwargs else kwargs['name']
+        super(IdentLearningExperiment, self).__init__(**kwargs)
 
     def run_net(self, net, trains_per_application=100, step_limit=100, run_id=0, **kwargs):
         pass

code/network.py

@@ -1,16 +1,24 @@
+# Librarys
 import numpy as np
 from abc import abstractmethod, ABC
 from typing import List, Union, Tuple
 from types import FunctionType
 
+# Functions and Operators
 from operator import mul
 from functools import reduce
+from itertools import accumulate
+from statistics import mean
+from random import random as prng
 
+# 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
 
+# Experiment Class
 from experiment import *
+from task import *
 
 # Supress warnings and info messages
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
@@ -35,14 +43,6 @@ class NeuralNetwork(ABC):
     This is the Base Network Class, including abstract functions that must be implemented.
     """
 
-    @staticmethod
-    def max(weights: List[np.ndarray]):
-        np.max(weights)
-
-    @staticmethod
-    def avg(weights: List[np.ndarray]):
-        return np.average(weights)
-
     @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])
@@ -52,7 +52,7 @@ class NeuralNetwork(ABC):
         return any([((lower_bound < x) & (x < upper_bound)).any() for x in weights])
 
     @staticmethod
-    def weight_amount(weights: List[np.ndarray]):
+    def get_weight_amount(weights: List[np.ndarray]):
         return sum([x.size for x in weights])
 
     @staticmethod
@@ -72,11 +72,12 @@ class NeuralNetwork(ABC):
 
     @staticmethod
     def reshape_flat_array(array, shapes: List[Tuple[int]]) -> List[np.ndarray]:
-        sizes: List[int] = [int(np.prod(shape)) for shape in shapes]
+        # 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(np.cumsum([0] + sizes), np.cumsum([0] + sizes)[1:])]
+        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
@@ -107,6 +108,9 @@ class NeuralNetwork(ABC):
     def print_weights(self, weights=None):
         print(self.repr(weights or self.get_weights()))
 
+    def get_amount_of_weights(self):
+        return self.get_weight_amount(self.get_weights())
+
     def get_weights(self) -> List[np.ndarray]:
         return self.model.get_weights()
 
@@ -120,31 +124,14 @@ class NeuralNetwork(ABC):
         return self.model.set_weights(new_weights)
 
     def apply_to_network(self, other_network) -> List[np.ndarray]:
-        # TODO: add a dogstring, telling the user what this does, e.g. what is applied?
+        """
+        Take a networks weights and apply _this_ networks function.
+        :param other_network:
+        :return:
+        """
         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.are_weights_diverged(self.get_weights())
 
@@ -171,11 +158,11 @@ class NeuralNetwork(ABC):
         return not biggerEpsilon
 
     def aggregate_weights_by(self, weights: List[np.ndarray], func: FunctionType, num_aggregates: int):
-        collection_sizes = self.weight_amount(weights) // num_aggregates
+        collection_sizes = self.get_weight_amount(weights) // num_aggregates
         flat = self.weights_to_flat_array(weights)
-        weights = flat[:collection_sizes * num_aggregates].reshape((num_aggregates, -1))
+        array_for_aggregation = flat[:collection_sizes * num_aggregates].reshape((num_aggregates, -1))
         left_overs = flat[collection_sizes * num_aggregates:]
-        aggregated_weights = func(weights, num_aggregates)
+        aggregated_weights = func(array_for_aggregation, num_aggregates)
         return aggregated_weights, left_overs
 
     def shuffle_weights(self, weights: List[np.ndarray]):
@@ -184,13 +171,19 @@ class NeuralNetwork(ABC):
         return self.reshape_flat_array_like(flat, weights)
 
     @abstractmethod
-    def get_samples(self):
+    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]:
-        # TODO: add a dogstring, telling the user what this does, e.g. what is applied?
+        """
+        Take weights as inputs; retunr the evaluation of _this_ network.
+        "Apply this function".
+
+        :param old_weights:
+        :return:
+        """
         raise NotImplementedError
 
 
@@ -240,6 +233,51 @@ class ParticleDecorator:
     def get_states(self):
         return self.states
 
+    def attack(self, other_network):
+        """
+        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:
+        :return:
+        """
+        other_network.set_weights(self.apply_to_network(other_network))
+        return self
+
+    def self_attack(self, iterations=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 other_network:
+        :return:
+        """
+        for _ in range(iterations):
+            self.attack(self)
+        return self
+
+
+class TaskDecorator(TaskAdditionOf2):
+
+    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()
+        elif prng() >= kwargs.get('split', 0.5):
+            return super(TaskDecorator, self).get_samples()
+        else:
+            return self.network.get_samples()
+
 
 class WeightwiseNeuralNetwork(NeuralNetwork):
 
@@ -258,8 +296,8 @@ class WeightwiseNeuralNetwork(NeuralNetwork):
         # TODO: Write about it... What does it do?
         return self.model.predict(inputs)
 
-    def get_samples(self, weights: List[np.ndarray] = None):
+    def get_samples(self, **kwargs: List[np.ndarray]):
-        weights = weights or self.get_weights()
+        weights = kwargs.get('weights', self.get_weights())
         sample = np.asarray([
             [weight, idx, *x] for idx, layer in enumerate(weights) for x, weight in np.ndenumerate(layer)
         ])
@@ -271,7 +309,7 @@ class WeightwiseNeuralNetwork(NeuralNetwork):
     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(weights)
+        transformed_weights, _ = self.get_samples(weights=weights)
         new_flat_weights = self.apply(transformed_weights)
         # use the original weight shape to transform the new tensor
         return self.reshape_flat_array_like(new_flat_weights, weights)
@@ -334,9 +372,6 @@ class AggregatingNeuralNetwork(NeuralNetwork):
     def get_shuffler(self):
         return self.params.get('shuffler', self.shuffle_not)
 
-    def get_amount_of_weights(self):
-        return self.weight_amount(self.get_weights())
-
     def apply(self, inputs):
         # You need to add an dimension here... "..." copies array values
         return self.model.predict(inputs[None, ...])
@@ -362,7 +397,7 @@ class AggregatingNeuralNetwork(NeuralNetwork):
         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)]))
@@ -475,13 +510,23 @@ class TrainingNeuralNetworkDecorator:
             self.model_compiled = True
         return self
 
-    def train(self, batchsize=1, store_states=True, epoch=0):
+    def train(self, batchsize=1, store_states=False, epoch=0):
         self.compiled()
         x, y = self.network.get_samples()
         savestatecallback = [SaveStateCallback(network=self, epoch=epoch)] if store_states else None
-        history = self.network.model.fit(x=x, y=y, epochs=epoch+1, verbose=0,
+        """
-                                         batch_size=batchsize, callbacks=savestatecallback,
+        Please Note:
-                                         initial_epoch=epoch)
+        
+        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=savestatecallback)
         return history.history['loss'][-1]
 
     def learn_from(self, other_network, batchsize=1):
@@ -489,48 +534,59 @@ 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 False:
+    if True:
         # WeightWise Neural Network
-        net_generator = lambda: ParticleDecorator(
+        net_generator = lambda: TrainingNeuralNetworkDecorator(TaskDecorator(
-            WeightwiseNeuralNetwork(width=2, depth=2
+            WeightwiseNeuralNetwork(width=2, depth=2))).with_keras_params(activation='linear')
-                                    ).with_keras_params(activation='linear'))
+        with TaskExperiment() as exp:
-        with FixpointExperiment() as exp:
+            exp.run_exp(net_generator, 10, trains_per_application=10)
-            exp.run_exp(net_generator, 10, logging=True)
             exp.reset_all()
 
-    if True:
+    if False:
         # Aggregating Neural Network
-        net_generator = lambda: ParticleDecorator(
+        net_generator = lambda: AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)
-            AggregatingNeuralNetwork(aggregates=4, width=2, depth=2
+        with MixedFixpointExperiment() as exp:
-                                     ).with_keras_params())
+            exp.run_exp(net_generator, 10)
-        with FixpointExperiment() as exp:
-            exp.run_exp(net_generator, 10, logging=True)
             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:
             exp.run_exp(net_generator, 10)
             exp.log(exp.counters)
             exp.reset_model()
             exp.reset_all()
 
-    if True:
+    if False:
         # ok so this works quite realiably
-        run_count = 10000
+        run_count = 1000
-        net_generator = lambda: TrainingNeuralNetworkDecorator(
+        net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(
-            ParticleDecorator(WeightwiseNeuralNetwork(width=2, depth=2)
+            width=2, depth=2).with_params(epsilon=0.0001, steplimit=2, trains_per_application=10
-                              )).with_params(epsilon=0.0001).with_keras_params(optimizer='sgd')
+                                          )).with_keras_params(optimizer='sgd')
         with MixedFixpointExperiment() as exp:
             for run_id in tqdm(range(run_count+1)):
                 exp.run_exp(net_generator, 1)
@@ -538,17 +594,18 @@ if __name__ == '__main__':
                     exp.run_exp(net_generator, 1)
             K.clear_session()
 
-    if True:
+    if False:
         with FixpointExperiment() as exp:
             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()
                 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))
@@ -560,8 +617,8 @@ 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()
                 if run_id % 500 == 0:

code/soup.py

@@ -1,4 +1,8 @@
 import random
+from operator import mul
+from functools import reduce
+
+from tensorflow.python.keras.layers import Dense, Dropout, BatchNormalization
 
 from network import *
 
@@ -17,6 +21,7 @@ class Soup(object):
         self.params = dict(attacking_rate=0.1, learn_from_rate=0.1, train=0, learn_from_severity=1)
         self.params.update(kwargs)
         self.time = 0
+        self.is_seeded = False
 
     def __copy__(self):
         copy_ = Soup(self.size, self.generator, **self.params)
@@ -43,9 +48,13 @@ class Soup(object):
         return self.historical_particles.get(uid, otherwise)
     
     def seed(self):
+        if not self.is_seeded:
             self.particles = []
             for _ in range(self.size):
                 self.particles += [self.generate_particle()]
+        else:
+            print('already seeded!')
+        self.is_seeded = True
         return self       
     
     def evolve(self, iterations=1):
@@ -59,6 +68,7 @@ class Soup(object):
                     particle.attack(other_particle)
                     description['action'] = 'attacking'
                     description['counterpart'] = other_particle.get_uid()
+
                 if prng() < self.params.get('learn_from_rate'):
                     other_particle_id = int(prng() * len(self.particles))
                     other_particle = self.particles[other_particle_id]
@@ -66,6 +76,7 @@ class Soup(object):
                         particle.learn_from(other_particle)
                     description['action'] = 'learn_from'
                     description['counterpart'] = other_particle.get_uid()
+
                 for _ in range(self.params.get('train', 0)):
                     # callbacks on save_state are broken for TrainingNeuralNetwork
                     loss = particle.train(store_states=False)
@@ -73,11 +84,13 @@ class Soup(object):
                     description['loss'] = loss
                     description['action'] = 'train_self'
                     description['counterpart'] = None
+
                 if self.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():
                     new_particle = self.generate_particle()
                     self.particles[particle_id] = new_particle
@@ -107,6 +120,56 @@ class Soup(object):
             print(particle.is_fixpoint())
 
 
+class SolvingSoup(Soup):
+
+    def __init__(self, task: Task, particle_amount: int, particle_generator, depth: int=None, **kwargs):
+        super(SolvingSoup, self).__init__(particle_amount, particle_generator, **kwargs)
+        self.model = Sequential()
+        self.depth = depth or particle_amount - 1
+        self.task = task
+
+        self.network_params = dict()
+        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 seed(self):
+        super(SolvingSoup, self).seed()
+
+        # Static First Layer
+        self.model.add(Dense(self.network_params.get('first_layer_units', 10), input_shape=self.task.input_shape))
+        self.model.add(BatchNormalization())
+
+        for layer_num in range(self.depth):
+            # ToDo !!!!!!!!!!
+            self.model.add(Dense())
+            self.model.add(Dropout(rate=self.params.get('sparsity_rate', 0.1)))
+
+        has_to_be_zero =
+
+        if has_to_be_zero:
+            raise ValueError(f'This Combination does not Work!, There are still {has_to_be_zero} unnassigned Weights!')
+        self.model.add(Dense(left_over_units))
+        self.model.add(Dense(self.task.output_shape))
+        pass
+
+    def compile_model(self, **kwargs):
+        compile_params = copy.deepcopy(self.compile_params)
+        compile_params.update(kwargs)
+        return self.model.compile(**compile_params)
+
+    def get_total_weight_amount(self):
+        if self.is_seeded:
+            return sum([x.get_amount_of_weights for x in self.particles])
+
+    def predict(self, x):
+        return self.model.predict(x)
+
+
+
+
 if __name__ == '__main__':
     if True:
         with SoupExperiment(name='soup') as exp:
@@ -136,3 +199,4 @@ if __name__ == '__main__':
             #     .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 +1,28 @@
+from abc import ABC, abstractmethod
+import numpy as np
+
+from typing import Tuple, List, Union
+
+
+class Task(ABC):
+
+    def __init__(self, input_shape, output_shape, **kwargs):
+        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 TaskAdditionOf2(Task):
+
+    def __init__(self, **kwargs):
+        super(TaskAdditionOf2, self).__init__(input_shape=(4,), output_shape=(1, ), **kwargs)
+
+    def get_samples(self) -> Tuple[np.ndarray, np.ndarray]:
+        x = np.zeros((self.batchsize, *self.input_shape))
+        x[:, :2] = np.random.standard_normal((self.batchsize, 2)) * 0.5
+        y = np.zeros_like(x)
+        y[:, -1] = np.sum(x, axis=1)
+        return x, y