repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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OccamNet_Public | OccamNet_Public-main/constant-fitting/Bases.py | from abc import ABC,abstractmethod
import torch
import sympy as sp
class Base(ABC):
@abstractmethod
def getOutput(self, input):
pass
@abstractmethod
def getSymbolicOutput(self, input):
pass
class BaseWithConstants(Base):
numInputs = 1
def getConstant(self, constant):
s... | 5,810 | 22.151394 | 79 | py |
OccamNet_Public | OccamNet_Public-main/constant-fitting/SparseSetters.py | import torch
import math
from Network import ActivationLayer
class SetPartialSparse:
def __init__(self, sparseInputs):
self.sparseInputs = sparseInputs
def getActivationsSparsity(self, inputSize, activationLists, outputSize):
numItems = [outputSize]
for i in range(len(activationLists)-... | 6,195 | 34.815029 | 111 | py |
OccamNet_Public | OccamNet_Public-main/constant-fitting/lossTest.py | from Losses import CrossEntropyLoss as L1
from Losses import CrossEntropyLoss2 as L2
import torch
import math
y = torch.tensor([[1,2],[2.,3]])
pred = torch.tensor([[[1.,2],[3,2]],[[2,1],[4,3]]])
probs1 = torch.tensor([1.,1])/math.e
probs2 = torch.tensor([[2.,1],[1,2]])/math.e
l1 = L1(1.0,2)
l2 = L2(1.0,2)
print(l1.... | 406 | 24.4375 | 51 | py |
OccamNet_Public | OccamNet_Public-main/constant-fitting/Network.py | from numpy.lib.npyio import save
import torch
import torch.nn as nn
import numpy as np
import math
import matplotlib.pyplot as plt
import Bases
from DataGenerators import FunctionDataGenerator,ImplicitFunctionDataGenerator
from Losses import CrossEntropyLoss
from torch.optim.lr_scheduler import ExponentialLR as decay
i... | 26,954 | 36.489569 | 265 | py |
OccamNet_Public | OccamNet_Public-main/constant-fitting/Losses.py | import torch
import math
class CrossEntropyLoss:
def __init__(self, std, topNumber, anomWeight = 0.2):
self.setStd(std)
self.topNumber = topNumber
self.weighting = torch.tensor([1.0/(n) for n in range(topNumber, 0, -1)])
self.anomWeight = anomWeight
def setStd(self, std):
... | 2,682 | 32.962025 | 121 | py |
OccamNet_Public | OccamNet_Public-main/constant-fitting/ConstantFittingDemo.py | import multiprocessing
import torch
import torch.nn as nn
import numpy as np
import Bases
from Losses import CrossEntropyLoss
from Network import NetworkConstants, ActivationLayer
from SparseSetters import SetPartialSparse as SPS
from SparseSetters import SetNoSparse as SNS
import argparse
import datetime
import pickle... | 1,507 | 29.16 | 377 | py |
OccamNet_Public | OccamNet_Public-main/image-recognition/visualization.py |
import numpy as np
from bases import *
from collections import defaultdict
import torch.nn.functional as F
import sympy as sp
import networkx as nx
import matplotlib.pyplot as plt
from matplotlib import rc, rcParams
def visualize(model, plot_graph=True, traceback=False, cascadeback=False, routing_map=None, viz_type=... | 9,592 | 33.507194 | 142 | py |
OccamNet_Public | OccamNet_Public-main/image-recognition/run_experiment.py | import json
import argparse
from experiment import ExperimentCollection, Experiment
import torch
import numpy as np
experiments_folder = "./experiments/"
if __name__=='__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--collection_name', type=str, default='pattern_recognition')
parser.add... | 2,231 | 40.333333 | 85 | py |
OccamNet_Public | OccamNet_Public-main/image-recognition/experiment.py | from torch.utils.data import Dataset, DataLoader
import torch
import torch.nn as nn
from neural_net import OccamNet
from seql import SEQL
from vanilla import Vanilla
from train import train
import pickle
from utils import get_model_equation
from IPython.core.display import display, HTML
from targets import TARGET_FUNCT... | 18,819 | 40.091703 | 115 | py |
OccamNet_Public | OccamNet_Public-main/image-recognition/neural_net.py | import torch
import torch.nn as nn
from utils import get_arity, get_model_equation
from train import train
from torchvision.models import resnet50
from torch.distributions import Categorical
import torch.nn.functional as F
from visualization import *
class OccamNet(torch.nn.Module):
def __init__(self,
... | 10,205 | 46.691589 | 131 | py |
OccamNet_Public | OccamNet_Public-main/image-recognition/bases.py | from sympy import *
import torch
import math
# BASIS FUNCTIONS DEFINITION
SIGMOID_PRECISION = 10
TANH_PRECISION = 10
# SIGMOID_PRECISION = 1
# TANH_PRECISION = 1
NORMAL_VARIANCE = 0.05
𝓝 = lambda x: torch.exp(-0.5 * (x)**2 / NORMAL_VARIANCE)
σ = lambda x: torch.sigmoid(SIGMOID_PRECISION * x)
tanh = lambda x: torch... | 5,711 | 37.08 | 151 | py |
OccamNet_Public | OccamNet_Public-main/image-recognition/utils.py | from sympy import *
import torch.nn as nn
from inspect import signature
import torch.nn.functional as F
import torch
from bases import *
def get_model_equation(model, arg_max=True):
def argmax_matrix(M):
argmaxes = torch.argmax(M, dim=1).unsqueeze(-1)
matrix = torch.zeros_like(M)
for i, ar... | 2,029 | 30.71875 | 103 | py |
OccamNet_Public | OccamNet_Public-main/image-recognition/targets.py | import torch
import numpy as np
import math
# ===========================================
### STANDARD
# ===========================================
IDENTITY = ['IDENTITY', lambda x: x]
CONSTANT_BUILDING = ['CONSTANT_BUILDING', lambda x: (3 * math.pi / (2 * math.e)) * x]
CONSTANT_MULTIPLY = ['CONSTANT_MULTIPLY', la... | 5,707 | 35.356688 | 108 | py |
OccamNet_Public | OccamNet_Public-main/image-recognition/train.py | from sympy import *
import torch
import torch.nn as nn
from alive_progress import alive_bar
from torch.utils.data import Dataset, DataLoader
import numpy as np
from visualization import print_model_equations
import time
bar_length = 100
EPS = 1e-12
class data(Dataset):
def __init__(self, inputs, targets):
... | 10,103 | 44.107143 | 249 | py |
OccamNet_Public | OccamNet_Public-main/image-recognition/imagenet.py | import torch
import torchvision.transforms as transforms
from torch.autograd import Variable
from torchvision.models import resnet50
from PIL import Image
import os
import numpy as np
net = resnet50(pretrained=True)
modules = list(net.children())[:-1]
feature_extractor = torch.nn.Sequential(*modules)
for p in feature_... | 1,461 | 28.24 | 86 | py |
OccamNet_Public | OccamNet_Public-main/optimized/PMLBDataSetTest.py | from pmlb import fetch_data, regression_dataset_names
import pickle
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_absolute_error, me... | 19,519 | 44.395349 | 233 | py |
OccamNet_Public | OccamNet_Public-main/optimized/ActivationLayer.py | import torch
class ActivationLayer:
def __init__(self, activations):
self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
self.activations = activations
self.totalInputs = 0
for item in activations:
self.totalInputs += item.numInputs
... | 1,328 | 33.973684 | 105 | py |
OccamNet_Public | OccamNet_Public-main/optimized/Bases.py | from abc import ABC,abstractmethod
import torch
import sympy as sp
class Base(ABC):
@abstractmethod
def getOutput(self, input):
pass
@abstractmethod
def getSymbolicOutput(self, input):
pass
class Add(Base):
numInputs = 2
def getLatex(self):
return "+"
def getOutp... | 3,163 | 18.530864 | 54 | py |
OccamNet_Public | OccamNet_Public-main/optimized/SparseSetters.py | import torch
import math
from ActivationLayer import ActivationLayer
class SetNoSparse:
def getActivationsSparsity(self, inputSize, activationLists, outputSize):
numItems = [outputSize]
for i in range(len(activationLists)-1,0,-1):
numItems.insert(0,numItems[0]*self.getMaxInputs(activati... | 2,042 | 29.492537 | 80 | py |
OccamNet_Public | OccamNet_Public-main/optimized/Network.py | import numpy as np
import math
import Bases
from ActivationLayer import ActivationLayer
import matplotlib.pyplot as plt
from matplotlib import rc, rcParams
from matplotlib import patches as patch
from sklearn.metrics import mean_squared_error as MSE
import torch
import torch.nn as nn
from torch.distributions import... | 12,520 | 36.376119 | 162 | py |
OccamNet_Public | OccamNet_Public-main/optimized/cosine.py | from DataGenerators import DataGeneratorSample as DGS
from Losses import CEL, Adaptive, Adaptive2,Adaptive4
from SparseSetters import SetNoSparseNoDuplicates as SNSND
from Network import Network
import Bases
import datetime
import math
import torch
import numpy as np
import matplotlib.pyplot as plt
times = []
if __n... | 1,572 | 28.12963 | 177 | py |
OccamNet_Public | OccamNet_Public-main/optimized/Losses.py | import torch
import math
class CEL:
def __init__(self, std, topNumber, anomWeight = 0.2):
self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
self.setStd(std)
self.topNumber = topNumber
self.weighting = torch.tensor([1.0/(n) for n in range(topNum... | 6,737 | 35.619565 | 115 | py |
OccamNet_Public | OccamNet_Public-main/optimized/DataGenerators.py | import torch
class FunctionDataGenerator:
def __init__(self, batchSize, dataRange, function):
self.batchSize = batchSize
self.dataRange = dataRange
self.function = function
def getBatch(self):
x = (torch.rand([self.batchSize], dtype = torch.float)*(self.dataRange[1]-self.dataRa... | 4,375 | 36.084746 | 160 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/XGBoostClass.py | import xgboost as xgb
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error as MSE
class XGBobj:
def __init__(self, max_depth=6, objective="reg:squarederror", gamma = 0, learning_rate = 0.01,
n_estimators = 1000, subsample = 0.5):
self.param = {'max_depth': ma... | 1,195 | 36.375 | 145 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/PMLBDataSetTest.py | from pmlb import fetch_data, regression_dataset_names
import pickle
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_absolute_error, me... | 24,045 | 44.114447 | 233 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/ActivationLayer.py | import torch
class ActivationLayer:
def __init__(self, activations):
self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
self.activations = activations
self.totalInputs = 0
for item in activations:
self.totalInputs += item.numInputs
... | 1,328 | 33.973684 | 105 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/Bases.py | from abc import ABC,abstractmethod
import torch
import sympy as sp
class Base(ABC):
@abstractmethod
def getOutput(self, input):
pass
@abstractmethod
def getSymbolicOutput(self, input):
pass
class Add(Base):
numInputs = 2
def getLatex(self):
return "+"
def getOutp... | 3,163 | 18.530864 | 54 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/SparseSetters.py | import torch
import math
from ActivationLayer import ActivationLayer
class SetNoSparse:
def getActivationsSparsity(self, inputSize, activationLists, outputSize):
numItems = [outputSize]
for i in range(len(activationLists)-1,0,-1):
numItems.insert(0,numItems[0]*self.getMaxInputs(activati... | 1,060 | 29.314286 | 80 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/run_sc_scale.py | import pickle
from sklearn.metrics import mean_absolute_error, mean_squared_error
from DataGenerators import DataGeneratorSample as DGS
from Losses import CEL
from SparseSetters import SetNoSparse as SNS
from Network import Network
import Bases
import torch
import torch.nn as nn
import time
import sys
logit_test_s... | 4,579 | 37.487395 | 141 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/sampleEfficiencyTest.py | from DataGenerators import DataGeneratorSample as DGS
from Losses import CEL
from SparseSetters import SetNoSparseNoDuplicates as SNSND
from Network import Network
import Bases
import datetime
import math
import torch
import numpy as np
for i in range(10):
size = 100
x = 20*torch.rand(size)-10
y = 2*x**2+... | 733 | 28.36 | 229 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/Network.py | import numpy as np
import math
import Bases
from ActivationLayer import ActivationLayer
import matplotlib.pyplot as plt
from matplotlib import rc, rcParams
from matplotlib import patches as patch
from sklearn.metrics import mean_squared_error as MSE
import torch
import torch.nn as nn
from torch.distributions import... | 16,414 | 36.3918 | 161 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/run_sc.py | import pickle
from sklearn.metrics import mean_absolute_error, mean_squared_error
from DataGenerators import DataGeneratorSample as DGS
from Losses import CEL
from SparseSetters import SetNoSparse as SNS
from Network import Network
import Bases
import torch
import torch.nn as nn
import time
import sys
logit_test_s... | 5,228 | 43.313559 | 209 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/Losses.py | import torch
import math
class CEL:
def __init__(self, std, topNumber, anomWeight = 0.2):
self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
self.setStd(std)
self.topNumber = topNumber
self.weighting = torch.tensor([1.0/(n) for n in range(topNum... | 1,250 | 32.810811 | 103 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/DataGenerators.py | import torch
class FunctionDataGenerator:
def __init__(self, batchSize, dataRange, function):
self.batchSize = batchSize
self.dataRange = dataRange
self.function = function
def getBatch(self):
x = (torch.rand([self.batchSize], dtype = torch.float)*(self.dataRange[1]-self.dataRa... | 4,375 | 36.084746 | 160 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/setup.py | import setuptools
from numpy.distutils.core import Extension, setup
sr1 = Extension(name='aifeynman._symbolic_regress1', sources=[
'aifeynman/symbolic_regress1.f90'])
sr2 = Extension(name='aifeynman._symbolic_regress2', sources=[
'aifeynman/symbolic_regress2.f90'])
sr3 = Extension(name='aifeynman._symbolic_r... | 2,384 | 36.265625 | 80 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_gen_sym.py | import numpy as np
from .RPN_to_eq import RPN_to_eq
from scipy.optimize import fsolve
from sympy import lambdify, N
import torch
import copy
import torch.nn as nn
import torch.nn.functional as F
from .get_pareto import Point, ParetoSet
from .S_get_expr_complexity import get_expr_complexity
from . import test_points
imp... | 5,520 | 34.850649 | 98 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_remove_input_neuron.py | # Remove on input neuron from a NN
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import pandas as pd
import numpy as np
import torch
from torch.utils import data
import pickle
from matplotlib import pyplot as plt
import torch.utils.... | 1,259 | 36.058824 | 176 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_final_gd.py | # Turns a mathematical expression (already RPN turned) to pytorch expression, trains the parameters, and returns the new error, complexity and the new symbolic expression
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import tor... | 5,639 | 37.367347 | 174 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_NN_get_gradients.py | # SAve a file with 2*(n-1) columns contaning the (n-1) independent variables and the (n-1) gradients of the trained NN with respect these variables
import matplotlib.pyplot as plt
import numpy as np
import copy
import os
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
is_cuda = torch.cuda... | 1,180 | 32.742857 | 147 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_add_bf_on_numbers_on_pareto.py | # Adds on the pareto all the snapped versions of a given expression (all paramters are snapped in the end)
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as utils
from torch.au... | 5,383 | 42.072 | 174 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_symmetry.py | # checks for symmetries in the data
from __future__ import print_function
import torch
import os
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import pandas as pd
import numpy as np
import torch
from torch.utils import data
import pickle
from torch.optim.lr_scheduler import CosineAn... | 21,477 | 35.589438 | 146 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_NN_train.py | from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import pandas as pd
import numpy as np
import torch
from torch.utils import data
import pickle
from matplotlib import pyplot as plt
import torch.utils.data as utils
import time
import os
... | 4,960 | 30.598726 | 118 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_gradient_decomposition.py | import matplotlib.pyplot as plt
import numpy as np
import copy
import os
import sys
import torch
import torch.nn as nn
from itertools import chain, combinations, islice
from scipy.stats import mannwhitneyu
from sklearn.neighbors import KernelDensity
from scipy.stats import iqr
from collections import Counter, namedt... | 9,250 | 33.64794 | 178 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/RPN_to_pytorch.py | # Turns a mathematical expression (already RPN turned) to pytorch expression, trains the parameters, and returns the new error, complexity and the new symbolic expression
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import tor... | 5,205 | 35.921986 | 174 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_separability.py | from __future__ import print_function
import torch
import os
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import pandas as pd
import numpy as np
import torch
from torch.utils import data
import pickle
from torch.optim.lr_scheduler import CosineAnnealingLR
from matplotlib import pypl... | 15,016 | 37.40665 | 159 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_NN_eval.py | from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import pandas as pd
import numpy as np
import torch
from torch.utils import data
import pickle
from torch.optim.lr_scheduler import CosineAnnealingLR
from matplotlib import pyplot as plt
... | 3,558 | 28.658333 | 102 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_compositionality.py | import numpy as np
from .RPN_to_eq import RPN_to_eq
from scipy.optimize import fsolve
from sympy import lambdify, N
import torch
import torch.nn as nn
import torch.nn.functional as F
from .get_pareto import Point, ParetoSet
from .S_get_expr_complexity import get_expr_complexity
from . import test_points
import os
impor... | 3,621 | 31.630631 | 98 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_run_aifeynman.py | import numpy as np
import matplotlib.pyplot as plt
import os
from os import path
from .get_pareto import Point, ParetoSet
from .RPN_to_pytorch import RPN_to_pytorch
from .RPN_to_eq import RPN_to_eq
from .S_NN_train import NN_train
from .S_NN_eval import NN_eval
from .S_symmetry import *
from .S_separability import *
fr... | 15,905 | 46.059172 | 208 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_run_bf_polyfit.py | # add a function to compte complexity
from .get_pareto import Point, ParetoSet
from .RPN_to_pytorch import RPN_to_pytorch
from .RPN_to_eq import RPN_to_eq
import numpy as np
import matplotlib.pyplot as plt
from .S_brute_force import brute_force
from .S_get_number_DL_snapped import get_number_DL_snapped
from sympy.pars... | 12,095 | 47.384 | 150 | py |
OccamNet_Public | OccamNet_Public-main/pmlb-experiments/AIFeynman/aifeynman/S_add_snap_expr_on_pareto.py | # Adds on the pareto all the snapped versions of a given expression (all paramters are snapped in the end)
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as utils
from torch.au... | 7,908 | 43.184358 | 174 | py |
OccamNet_Public | OccamNet_Public-main/analytic-and-programs/visualization.py | import numpy as np
from bases import *
from collections import defaultdict
import torch.nn.functional as F
import sympy as sp
import networkx as nx
import matplotlib.pyplot as plt
from matplotlib import rc, rcParams
def visualize(model, plot_graph=True, traceback=False, cascadeback=False, routing_map=None, viz_type=[... | 9,460 | 34.302239 | 142 | py |
OccamNet_Public | OccamNet_Public-main/analytic-and-programs/experiment.py | from torch.utils.data import Dataset, DataLoader
import torch
import torch.nn as nn
from neural_net import OccamNet
# from seql import SEQL
# from vanilla import Vanilla
from train import train
import pickle
from utils import get_model_equation
from IPython.core.display import display, HTML
from targets import TARGET_F... | 11,831 | 37.793443 | 136 | py |
OccamNet_Public | OccamNet_Public-main/analytic-and-programs/neural_net.py | import torch
import torch.nn as nn
from utils import get_arity, get_model_equation
import numpy as np
import matplotlib.pyplot as plt
from bases import *
from train import train
import time
from torch.distributions import Categorical
import torch.nn.functional as F
from visualization import *
class OccamNet(torch.nn... | 14,060 | 47.486207 | 163 | py |
OccamNet_Public | OccamNet_Public-main/analytic-and-programs/bases.py | from sympy import *
import torch
import math
# BASIS FUNCTIONS DEFINITION
SIGMOID_PRECISION = 1000000
NORMAL_VARIANCE = 0.05
𝓝 = lambda x: torch.exp(-0.5 * (x)**2 / NORMAL_VARIANCE)
σ = lambda x: torch.sigmoid(SIGMOID_PRECISION * x)
def if_(x):
if x == True: return 1
if x == False: return 0
return Funct... | 2,937 | 42.205882 | 156 | py |
OccamNet_Public | OccamNet_Public-main/analytic-and-programs/utils.py | from sympy import *
import torch.nn as nn
from inspect import signature
import torch.nn.functional as F
import torch
from bases import *
def get_model_equation(model, arg_max=True):
def argmax_matrix(M):
argmaxes = torch.argmax(M, dim=1).unsqueeze(-1)
matrix = torch.zeros_like(M)
for i, ar... | 2,049 | 29.597015 | 99 | py |
OccamNet_Public | OccamNet_Public-main/analytic-and-programs/targets.py | import torch
import numpy as np
import math
# ===========================================
### STANDARD
# ===========================================
IDENTITY = ['IDENTITY', lambda x: x]
CONSTANT_BUILDING = ['CONSTANT_BUILDING', lambda x: (3 * math.pi / (2 * math.e)) * x]
CONSTANT_MULTIPLY = ['CONSTANT_MULTIPLY', la... | 6,490 | 33.71123 | 108 | py |
OccamNet_Public | OccamNet_Public-main/analytic-and-programs/train.py | from sympy import *
from bases import *
from targets import *
import torch.nn.functional as F
import torch
import torch.nn as nn
from torch.utils import data
import matplotlib.pyplot as plt
from alive_progress import alive_bar
from utils import get_model_equation
from torch.distributions import Categorical
import torc... | 6,611 | 39.072727 | 140 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/PMLBDataSetTest.py | from pmlb import fetch_data, regression_dataset_names
import pickle
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_absolute_error, me... | 1,062 | 23.159091 | 67 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/Benchmarks.py | from pmlb import fetch_data, regression_dataset_names
import pickle
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_absolute_error, me... | 10,793 | 26.188917 | 139 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/setup.py | import setuptools
from numpy.distutils.core import Extension, setup
sr1 = Extension(name='aifeynman._symbolic_regress1', sources=[
'aifeynman/symbolic_regress1.f90'])
sr2 = Extension(name='aifeynman._symbolic_regress2', sources=[
'aifeynman/symbolic_regress2.f90'])
sr3 = Extension(name='aifeynman._symbolic_r... | 2,384 | 36.265625 | 80 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_gen_sym.py | import numpy as np
from .RPN_to_eq import RPN_to_eq
from scipy.optimize import fsolve
from sympy import lambdify, N
import torch
import copy
import torch.nn as nn
import torch.nn.functional as F
from .get_pareto import Point, ParetoSet
from .S_get_expr_complexity import get_expr_complexity
from . import test_points
imp... | 5,520 | 34.850649 | 98 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_remove_input_neuron.py | # Remove on input neuron from a NN
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import pandas as pd
import numpy as np
import torch
from torch.utils import data
import pickle
from matplotlib import pyplot as plt
import torch.utils.... | 1,259 | 36.058824 | 176 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_final_gd.py | # Turns a mathematical expression (already RPN turned) to pytorch expression, trains the parameters, and returns the new error, complexity and the new symbolic expression
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import tor... | 5,639 | 37.367347 | 174 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_NN_get_gradients.py | # SAve a file with 2*(n-1) columns contaning the (n-1) independent variables and the (n-1) gradients of the trained NN with respect these variables
import matplotlib.pyplot as plt
import numpy as np
import copy
import os
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
is_cuda = torch.cuda... | 1,180 | 32.742857 | 147 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_add_bf_on_numbers_on_pareto.py | # Adds on the pareto all the snapped versions of a given expression (all paramters are snapped in the end)
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as utils
from torch.au... | 5,383 | 42.072 | 174 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_symmetry.py | # checks for symmetries in the data
from __future__ import print_function
import torch
import os
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import pandas as pd
import numpy as np
import torch
from torch.utils import data
import pickle
from torch.optim.lr_scheduler import CosineAn... | 21,477 | 35.589438 | 146 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_NN_train.py | from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import pandas as pd
import numpy as np
import torch
from torch.utils import data
import pickle
from matplotlib import pyplot as plt
import torch.utils.data as utils
import time
import os
... | 4,960 | 30.598726 | 118 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_gradient_decomposition.py | import matplotlib.pyplot as plt
import numpy as np
import copy
import os
import sys
import torch
import torch.nn as nn
from itertools import chain, combinations, islice
from scipy.stats import mannwhitneyu
from sklearn.neighbors import KernelDensity
from scipy.stats import iqr
from collections import Counter, namedt... | 9,250 | 33.64794 | 178 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/RPN_to_pytorch.py | # Turns a mathematical expression (already RPN turned) to pytorch expression, trains the parameters, and returns the new error, complexity and the new symbolic expression
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import tor... | 5,205 | 35.921986 | 174 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_separability.py | from __future__ import print_function
import torch
import os
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import pandas as pd
import numpy as np
import torch
from torch.utils import data
import pickle
from torch.optim.lr_scheduler import CosineAnnealingLR
from matplotlib import pypl... | 15,016 | 37.40665 | 159 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_NN_eval.py | from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import pandas as pd
import numpy as np
import torch
from torch.utils import data
import pickle
from torch.optim.lr_scheduler import CosineAnnealingLR
from matplotlib import pyplot as plt
... | 3,558 | 28.658333 | 102 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_compositionality.py | import numpy as np
from .RPN_to_eq import RPN_to_eq
from scipy.optimize import fsolve
from sympy import lambdify, N
import torch
import torch.nn as nn
import torch.nn.functional as F
from .get_pareto import Point, ParetoSet
from .S_get_expr_complexity import get_expr_complexity
from . import test_points
import os
impor... | 3,621 | 31.630631 | 98 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_run_aifeynman.py | import numpy as np
import matplotlib.pyplot as plt
import os
from os import path
from .get_pareto import Point, ParetoSet
from .RPN_to_pytorch import RPN_to_pytorch
from .RPN_to_eq import RPN_to_eq
from .S_NN_train import NN_train
from .S_NN_eval import NN_eval
from .S_symmetry import *
from .S_separability import *
fr... | 15,905 | 46.059172 | 208 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_run_bf_polyfit.py | # add a function to compte complexity
from .get_pareto import Point, ParetoSet
from .RPN_to_pytorch import RPN_to_pytorch
from .RPN_to_eq import RPN_to_eq
import numpy as np
import matplotlib.pyplot as plt
from .S_brute_force import brute_force
from .S_get_number_DL_snapped import get_number_DL_snapped
from sympy.pars... | 12,095 | 47.384 | 150 | py |
OccamNet_Public | OccamNet_Public-main/analytic-benchmarks/AIFeynman/aifeynman/S_add_snap_expr_on_pareto.py | # Adds on the pareto all the snapped versions of a given expression (all paramters are snapped in the end)
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as utils
from torch.au... | 7,908 | 43.184358 | 174 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/main_pretrain.py | '''Train CIFAR10/CIFAR100 with PyTorch.'''
from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdm
from tensorboardX import SummaryWriter
from utils.network_utils import get_network
from utils.data_utils import get_dataloader
... | 6,033 | 35.131737 | 124 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/main_prune_separable.py | import argparse
import json
import os
import sys
import torch
import torch.optim as optim
from models import VGG
from pruner.fisher_diag_pruner import FisherDiagPruner
from pruner.kfac_eigen_pruner import KFACEigenPruner
from pruner.kfac_full_pruner import KFACFullPruner
from pruner.kfac_OBD_F2 import KFACOBDF2Pruner
... | 16,279 | 43.480874 | 119 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/main_prune.py | import argparse
import json
import os
import sys
import torch
import torch.optim as optim
from models import VGG
from pruner.fisher_diag_pruner import FisherDiagPruner
from pruner.kfac_eigen_pruner import KFACEigenPruner
from pruner.kfac_full_pruner import KFACFullPruner
from pruner.kfac_OBD_F2 import KFACOBDF2Pruner
... | 14,510 | 43.51227 | 119 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/pruner/kfac_OBD_F2.py | """
F2 = A ⊗ B,
A = in_c * in_c
B = out_c * out_c (Diagonal)
"""
import torch
from pruner.kfac_full_pruner import KFACFullPruner
from utils.common_utils import tensor_to_list
from utils.kfac_utils import (ComputeMatGrad,
fetch_mat_weights)
class KFACOBDF2Pruner(KFACFullPruner):
def... | 1,744 | 35.354167 | 120 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/pruner/kfac_eigen_svd_pruner.py | import numpy as np
import time
import torch
import torch.nn as nn
from sktensor import dtensor, cp_als
from pruner.kfac_eigen_pruner import KFACEigenPruner
def get_UDV_decomposition(W, method='svd'):
# current implementation is svd
c_out, khkw, c_in = W.shape
method = method.lower()
with torch.no_grad... | 3,905 | 32.965217 | 120 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/pruner/fisher_diag_pruner.py | import torch
from pruner.kfac_full_pruner import KFACFullPruner
from utils.common_utils import tensor_to_list
from utils.kfac_utils import (ComputeMatGrad,
fetch_mat_weights)
class FisherDiagPruner(KFACFullPruner):
def __init__(self,
model,
builder... | 3,496 | 32.304762 | 120 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/pruner/kfac_OBS_F2.py | """
F2 = A ⊗ B,
A = in_c * in_c
B = out_c * out_c (Diagonal)
"""
import torch
from pruner.kfac_full_pruner import KFACFullPruner
from utils.common_utils import tensor_to_list
from utils.kfac_utils import (fetch_mat_weights,
mat_to_weight_and_bias)
class KFACOBSF2Pruner(KFACFullPruner):... | 1,963 | 36.056604 | 95 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/pruner/kfac_eigen_pruner.py | import torch
import torch.nn as nn
from collections import OrderedDict
from utils.kfac_utils import (ComputeCovA,
ComputeCovG,
ComputeCovAPatch,
fetch_mat_weights)
from utils.common_utils import (tensor_to_list,
... | 17,905 | 45.149485 | 129 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/pruner/kfac_full_pruner.py | import torch
import torch.nn as nn
from collections import OrderedDict
from models.resnet import _weights_init
from utils.kfac_utils import (ComputeCovA,
ComputeCovAPatch,
ComputeCovG,
fetch_mat_weights,
... | 18,290 | 47.517241 | 124 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/models/presnet.py | from __future__ import absolute_import
import math
import torch.nn as nn
import numpy as np
import torch
from utils.common_utils import try_cuda
from utils.prune_utils import (ConvLayerRotation,
LinearLayerRotation,
register_bottleneck_layer,
... | 9,824 | 34.215054 | 136 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/models/resnet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from utils.prune_utils import (ConvLayerRotation,
LinearLayerRotation,
register_bottleneck_layer,
update_QQ_dict)
from ... | 8,835 | 34.918699 | 120 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/models/vgg.py | import math
import torch
import torch.nn as nn
from utils.common_utils import try_contiguous
from utils.prune_utils import register_bottleneck_layer, update_QQ_dict
from utils.prune_utils import LinearLayerRotation, ConvLayerRotation
# from layers.bottleneck_layers import LinearBottleneck, Conv2dBottleneck
from models... | 5,547 | 36.486486 | 116 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/utils/utils.py | '''Some helper functions for PyTorch, including:
- get_mean_and_std: calculate the mean and std value of dataset.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''
import os
import sys
import time
import math
import torch
import torch.nn as nn
import torch.nn.i... | 3,463 | 25.646154 | 96 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/utils/prune_utils.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from torch.nn.modules.utils import _pair
# ======================================================
# Find layer dependency
# Update input indices (adapt to previous layers)
# Update output indices
... | 13,185 | 35.425414 | 112 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/utils/data_utils.py | import torch
import torchvision
import torchvision.transforms as transforms
def get_transforms(dataset):
transform_train = None
transform_test = None
if dataset == 'cifar10':
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHoriz... | 4,087 | 43.923077 | 113 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/utils/common_utils.py | import os
import time
import json
import logging
import torch
from pprint import pprint
from easydict import EasyDict as edict
def get_logger(name, logpath, filepath, package_files=[],
displaying=True, saving=True):
logger = logging.getLogger(name)
logger.setLevel(logging.INFO)
log_path =... | 4,614 | 27.84375 | 76 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/utils/compute_wallclock_time.py | import torch
import time
from models import *
def compute_wallclock_time(net, input_res, batch_size):
with torch.no_grad():
x = torch.cuda.FloatTensor(batch_size, 3, input_res, input_res).normal_()
net = net.cuda()
torch.cuda.synchronize()
torch.cuda.synchronize()
a = time.... | 515 | 27.666667 | 81 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/utils/kfac_utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.common_utils import try_contiguous
def _extract_patches(x, kernel_size, stride, padding):
"""
:param x: The input feature maps. (batch_size, in_c, h, w)
:param kernel_size: the kernel size of the conv filter (tuple of two elem... | 9,290 | 33.032967 | 105 | py |
EigenDamage-Pytorch | EigenDamage-Pytorch-master/utils/compute_flops.py | import numpy as np
import torch
import torchvision
import torch.nn as nn
from collections import OrderedDict
from torch.autograd import Variable
from utils.prune_utils import ConvLayerRotation, LinearLayerRotation
def print_model_param_nums(model=None):
if model == None:
model = torchvision.models.alexne... | 5,819 | 36.792208 | 131 | py |
UNIT | UNIT-master/test.py | """
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from __future__ import print_function
from utils import get_config, pytorch03_to_pytorch04
from trainer import MUNIT_Trainer, UNIT_Trainer
import... | 4,453 | 39.490909 | 126 | py |
UNIT | UNIT-master/test_batch.py | """
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from __future__ import print_function
from utils import get_config, get_data_loader_folder, pytorch03_to_pytorch04
from trainer import MUNIT_Trai... | 5,005 | 43.696429 | 132 | py |
UNIT | UNIT-master/utils.py | """
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from torch.utils.serialization import load_lua
from torch.utils.data import DataLoader
from networks import Vgg16
from torch.autograd import Vari... | 16,617 | 47.44898 | 141 | py |
UNIT | UNIT-master/data.py | """
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import torch.utils.data as data
import os.path
def default_loader(path):
return Image.open(path).convert('RGB')
def default_flist_reader(f... | 3,941 | 29.323077 | 105 | py |
UNIT | UNIT-master/networks.py | """
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from torch import nn
from torch.autograd import Variable
import torch
import torch.nn.functional as F
try:
from itertools import izip as zip
... | 20,436 | 39.230315 | 157 | py |
UNIT | UNIT-master/train.py | """
Copyright (C) 2018 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
from utils import get_all_data_loaders, prepare_sub_folder, write_html, write_loss, get_config, write_2images, Timer
import argparse
from torch.a... | 4,358 | 43.938144 | 120 | py |
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