created separate file for experiment class
This commit is contained in:
Thomas Gabor
45
code/experiment.py
Normal file
45
code/experiment.py
Normal file
@ -0,0 +1,45 @@
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import sys
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import os
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import time
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import copy
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import dill
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class Experiment:
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@staticmethod
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def from_dill(path):
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with open(path, "rb") as dill_file:
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return dill.load(dill_file)
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def __init__(self, name=None, id=None):
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self.experiment_id = id or time.time()
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this_file = os.path.realpath(os.getcwd() + "/" + sys.argv[0])
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self.experiment_name = name or os.path.basename(this_file)
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self.base_dir = os.path.realpath((os.path.dirname(this_file) + "/..")) + "/"
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self.next_iteration = 0
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self.log_messages = []
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def __enter__(self):
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self.dir = self.base_dir + "experiments/exp-" + str(self.experiment_name) + "-" + str(self.experiment_id) + "-" + str(self.next_iteration) + "/"
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os.mkdir(self.dir)
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print("** created " + str(self.dir))
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.save(experiment=self)
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self.save_log()
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self.next_iteration += 1
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def log(self, message, **kwargs):
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self.log_messages.append(message)
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print(message, **kwargs)
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def save_log(self, log_name="log"):
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with open(self.dir + "/" + str(log_name) + ".txt", "w") as log_file:
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for log_message in self.log_messages:
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print(str(log_message), file=log_file)
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def save(self, **kwargs):
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for name,value in kwargs.items():
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with open(self.dir + "/" + str(name) + ".dill", "wb") as dill_file:
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dill.dump(value, dill_file)
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@ -7,6 +7,7 @@ import time
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import os
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import dill
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from experiment import Experiment
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import itertools
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@ -172,38 +173,6 @@ class FeedForwardNetwork(_BaseNetwork):
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bar.update()
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return losses
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class Experiment:
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@staticmethod
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def from_dill(path):
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with open(path, "r") as dill_file:
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return dill.load(dill_file)
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def __init__(self, name=None, id=None):
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self.experiment_id = id or time.time()
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this_file = os.path.realpath(__file__)
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self.experiment_name = name or os.path.basename(this_file)
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self.base_dir = os.path.realpath((os.path.dirname(this_file) + "/..")) + "/"
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self.next_iteration = 0
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def __enter__(self):
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self.dir = self.base_dir + "experiments/exp-" + str(self.experiment_name) + "-" + str(self.experiment_id) + "-" + str(self.next_iteration) + "/"
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os.mkdir(self.dir)
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print("** created " + str(self.dir))
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.save(experiment=self)
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self.next_iteration += 1
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def save(self, **kwargs):
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for name,value in kwargs.items():
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with open(self.dir + "/" + str(name) + ".dill", "wb") as dill_file:
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dill.dump(value, dill_file)
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if __name__ == '__main__':
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with Experiment() as exp:
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features, cells, layers = 2, 2, 2
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111
code/other.py
111
code/other.py
@ -1,8 +1,5 @@
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import os
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import time
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import math
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import copy
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import dill
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import numpy as np
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import tensorflow as tf
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@ -11,34 +8,8 @@ from keras.layers import SimpleRNN, Dense
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from keras.layers import Input, TimeDistributed
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from tqdm import tqdm
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class Experiment:
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@staticmethod
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def from_dill(path):
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with open(path, "r") as dill_file:
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return dill.load(dill_file)
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def __init__(self, name=None, id=None):
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self.experiment_id = id or time.time()
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this_file = os.path.realpath(__file__)
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self.experiment_name = name or os.path.basename(this_file)
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self.base_dir = os.path.realpath((os.path.dirname(this_file) + "/..")) + "/"
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self.next_iteration = 0
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def __enter__(self):
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self.dir = self.base_dir + "experiments/exp-" + str(self.experiment_name) + "-" + str(self.experiment_id) + "-" + str(self.next_iteration) + "/"
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os.mkdir(self.dir)
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print("** created " + str(self.dir))
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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self.save(experiment=self)
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self.next_iteration += 1
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def save(self, **kwargs):
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for name,value in kwargs.items():
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with open(self.dir + "/" + str(name) + ".dill", "wb") as dill_file:
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dill.dump(value, dill_file)
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from experiment import Experiment
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def normalize_id(value, norm):
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if norm > 1:
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@ -64,24 +35,16 @@ def are_weights_within(network_weights, lower_bound, upper_bound):
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return False
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return True
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class WeightwiseNeuralNetwork:
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class NeuralNetwork:
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def __init__(self, width, depth, **keras_params):
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# set up parameters
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self.width = width
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self.depth = depth
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self.params = dict(epsilon=0.00000000000001)
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self.keras_params = dict(activation='linear', use_bias=False)
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self.keras_params.update(keras_params)
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#build network
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self.model = Sequential()
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self.model.add(Dense(units=width, input_dim=4, **self.keras_params))
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for _ in range(depth-1):
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self.model.add(Dense(units=width, **self.keras_params))
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self.model.add(Dense(units=1, **self.keras_params))
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def set_params(self, **kwargs):
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self.params.update(kwargs)
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@ -94,27 +57,6 @@ class WeightwiseNeuralNetwork:
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def set_weights(self, new_weights):
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return self.model.set_weights(new_weights)
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def apply(self, *input):
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stuff = np.transpose(np.array([[input[0]], [input[1]], [input[2]], [input[3]]]))
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return self.model.predict(stuff)[0][0]
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def apply_to_weights(self, old_weights):
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new_weights = copy.deepcopy(old_weights)
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max_layer_id = len(old_weights) - 1
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for layer_id,layer in enumerate(old_weights):
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max_cell_id = len(layer) - 1
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for cell_id,cell in enumerate(layer):
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max_weight_id = len(cell) - 1
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for weight_id,weight in enumerate(cell):
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normal_layer_id = normalize_id(layer_id, max_layer_id)
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normal_cell_id = normalize_id(cell_id, max_cell_id)
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normal_weight_id = normalize_id(weight_id, max_weight_id)
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new_weight = self.apply(weight, normal_layer_id, normal_cell_id, normal_weight_id)
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new_weights[layer_id][cell_id][weight_id] = new_weight
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if self.params.get("print_all_weight_updates", False):
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print("updated old weight " + str(weight) + "\t @ (" + str(layer_id) + "," + str(cell_id) + "," + str(weight_id) + ") to new value " + str(new_weight) + "\t calling @ (" + str(normal_layer_id) + "," + str(normal_cell_id) + "," + str(normal_weight_id) + ")")
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return new_weights
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def apply_to_network(self, other_network):
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new_weights = self.apply_to_weights(other_network.get_weights())
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@ -165,10 +107,45 @@ class WeightwiseNeuralNetwork:
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print(self.repr_weights())
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class WeightwiseNeuralNetwork(NeuralNetwork):
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def __init__(self, width, depth, **keras_params):
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super().__init__(width, depth, **keras_params)
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self.model = Sequential()
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self.model.add(Dense(units=width, input_dim=4, **self.keras_params))
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for _ in range(depth-1):
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self.model.add(Dense(units=width, **self.keras_params))
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self.model.add(Dense(units=1, **self.keras_params))
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def apply(self, *input):
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stuff = np.transpose(np.array([[input[0]], [input[1]], [input[2]], [input[3]]]))
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return self.model.predict(stuff)[0][0]
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def apply_to_weights(self, old_weights):
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new_weights = copy.deepcopy(old_weights)
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max_layer_id = len(old_weights) - 1
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for layer_id,layer in enumerate(old_weights):
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max_cell_id = len(layer) - 1
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for cell_id,cell in enumerate(layer):
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max_weight_id = len(cell) - 1
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for weight_id,weight in enumerate(cell):
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normal_layer_id = normalize_id(layer_id, max_layer_id)
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normal_cell_id = normalize_id(cell_id, max_cell_id)
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normal_weight_id = normalize_id(weight_id, max_weight_id)
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new_weight = self.apply(weight, normal_layer_id, normal_cell_id, normal_weight_id)
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new_weights[layer_id][cell_id][weight_id] = new_weight
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if self.params.get("print_all_weight_updates", False):
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print("updated old weight " + str(weight) + "\t @ (" + str(layer_id) + "," + str(cell_id) + "," + str(weight_id) + ") to new value " + str(new_weight) + "\t calling @ (" + str(normal_layer_id) + "," + str(normal_cell_id) + "," + str(normal_weight_id) + ")")
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return new_weights
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if __name__ == '__main__':
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with Experiment() as exp:
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counts = dict(divergent=0, fix_zero=0, fix_other=0, other=0)
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for run_id in tqdm(range(10000)):
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for run_id in tqdm(range(10)):
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activation = 'linear'
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net = WeightwiseNeuralNetwork(2, 2, activation='linear')
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# net.set_params(print_all_weight_updates=True)
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@ -189,9 +166,9 @@ if __name__ == '__main__':
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counts['fix_zero'] += 1
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else:
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counts['fix_other'] += 1
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net.print_weights()
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exp.log(net.repr_weights())
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net.self_attack()
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net.print_weights()
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exp.log(net.repr_weights())
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else:
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counts['other'] += 1
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print(counts)
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exp.log(counts)
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