repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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spyn-repr | spyn-repr-master/dataset.py | import numpy
import csv
import re
DATA_PATH = "data/"
DATA_FULL_PATH = DATA_PATH + 'full/'
DATASET_NAMES = ['accidents',
'ad',
'baudio',
'bbc',
'bnetflix',
'book',
'c20ng',
'cr52',
... | 11,393 | 28.594805 | 88 | py |
spyn-repr | spyn-repr-master/caltech101.py | import numpy
import matplotlib
import matplotlib.pyplot as pyplot
import pickle
import os
from scipy.io import loadmat
RANDOM_SEED = 1337
def load_caltech101_from_mat(data_path,
split_names=['train',
'val',
... | 2,154 | 28.121622 | 84 | py |
spyn-repr | spyn-repr-master/visualize.py | from spn import MARG_IND
import numpy
import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as pyplot
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.gridspec as gridspec
from matplotlib.colors import LogNorm
# from spn.utils import get_best_value_from_frame
import seaborn
from... | 33,780 | 29.765938 | 95 | py |
spyn-repr | spyn-repr-master/mnist.py | from visualize import array_2_mat
from visualize import plot_m_by_n_images
import numpy
import matplotlib
import matplotlib.pyplot as pyplot
import pickle
import os
RANDOM_SEED = 1337
def load_mnist_data_split_from_txt(data_path):
data = numpy.loadtxt(data_path, delimiter=' ')
x, y = data[:, :-1], dat... | 2,696 | 27.691489 | 99 | py |
spyn-repr | spyn-repr-master/bin/eval_spn.py | import argparse
try:
from time import perf_counter
except:
from time import time
perf_counter = time
import dataset
import numpy
import datetime
import os
import logging
from spn.utils import stats_format
from spn.linked.spn import evaluate_on_dataset
from spn.theanok.spn import evaluate_on_dataset_... | 5,069 | 31.292994 | 83 | py |
spyn-repr | spyn-repr-master/bin/mtlearn_exp.py | import subprocess
import numpy
import os
import argparse
import logging
import datetime
import re
try:
from time import perf_counter
except:
from time import time as perf_counter
MTLEARN_EXEC = './mtlearn'
MSCORE_EXEC = './mscore'
SPN2AC_EXEC = './spn2ac'
SPN_EXT = '.spn'
AC_EXT = '.ac'
DATA_DIR = 'd... | 13,099 | 31.26601 | 93 | py |
spyn-repr | spyn-repr-master/bin/classify_repr_exp.py |
import argparse
try:
from time import perf_counter
except:
from time import time
perf_counter = time
import dataset
import numpy
import datetime
import os
import logging
from spn.utils import stats_format
from sklearn.preprocessing import StandardScaler
from sklearn import linear_model
from sklear... | 17,148 | 35.721627 | 108 | py |
spyn-repr | spyn-repr-master/bin/filter_feature_repr.py | import argparse
try:
from time import perf_counter
except:
from time import time
perf_counter = time
import dataset
import numpy
import datetime
import os
import logging
from spn.utils import stats_format
from spn import MARG_IND
from spn.linked.nodes import SumNode
from spn.linked.nodes import Prod... | 12,095 | 35 | 96 | py |
spyn-repr | spyn-repr-master/bin/rbm_repr_data.py | import argparse
try:
from time import perf_counter
except:
from time import time
perf_counter = time
import dataset
import numpy
import datetime
import os
import logging
from sklearn import neural_network
from spn.utils import stats_format
from spn import MARG_IND
import pickle
MODEL_EXT = 'model'... | 16,883 | 39.684337 | 96 | py |
spyn-repr | spyn-repr-master/bin/theanok_benchmark.py | import sys
sys.setrecursionlimit(50000)
import dataset
import numpy
from numpy.testing import assert_array_almost_equal
import theano.misc.pkl_utils
import datetime
import os
import logging
from spn.utils import stats_format
from spn.linked.spn import evaluate_on_dataset
from spn.theanok.spn import *
from spn.t... | 5,970 | 31.451087 | 92 | py |
spyn-repr | spyn-repr-master/bin/marg_feature_gen.py | import argparse
try:
from time import perf_counter
except:
from time import time
perf_counter = time
import dataset
import numpy
import datetime
import os
import logging
from spn import MARG_IND
from spn.linked.representation import extract_features_marginalization_rand
from spn.linked.representatio... | 6,508 | 38.448485 | 104 | py |
spyn-repr | spyn-repr-master/bin/visualize_spn.py | from spn import MARG_IND
from spn.utils import stats_format
from spn.utils import approx_scope_histo_quartiles
from spn.linked.spn import evaluate_on_dataset
from spn.linked.nodes import SumNode
from spn.linked.nodes import ProductNode
from spn.linked.representation import load_features_from_file
from spn.linked.rep... | 59,334 | 41.748559 | 114 | py |
spyn-repr | spyn-repr-master/bin/spn_repr_data.py | import sys
sys.setrecursionlimit(1000000000)
import argparse
try:
from time import perf_counter
except:
from time import time
perf_counter = time
import dataset
import numpy
import datetime
import os
import logging
from spn.utils import stats_format
from spn import MARG_IND
from spn.linked.represen... | 35,122 | 41.990208 | 105 | py |
spyn-repr | spyn-repr-master/bin/merge_repr.py | from spn.linked.representation import load_features_from_file
from spn.linked.representation import save_features_to_file
import numpy
from numpy.testing import assert_array_equal
import os
import logging
import argparse
import pickle
DATA_EXT = 'data'
TRAIN_DATA_EXT = 'ts.{}'.format(DATA_EXT)
VALID_DATA_EXT = 'v... | 5,310 | 32.19375 | 95 | py |
spyn-repr | spyn-repr-master/bin/feature_split.py | from spn.linked.representation import load_features_from_file
from spn.linked.representation import save_features_to_file
import numpy
from numpy.testing import assert_array_equal
import os
import logging
import argparse
def batch_feature_split(feature_path, batch_size):
#
# load features first
featur... | 2,748 | 32.938272 | 102 | py |
spyn-repr | spyn-repr-master/bin/libra_repr_data.py | import argparse
try:
from time import perf_counter
except:
from time import time
perf_counter = time
import dataset
import numpy
import datetime
import os
import logging
from spn.utils import stats_format
from spn import MARG_IND
from spn.linked.representation import extract_features_marginalization... | 9,969 | 36.481203 | 104 | py |
spyn-repr | spyn-repr-master/bin/learnspn_exp.py | import argparse
try:
from time import perf_counter
except:
from time import time
perf_counter = time
import dataset
import numpy
from numpy.testing import assert_almost_equal
import random
import datetime
import os
import logging
from algo.learnspn import LearnSPN
from spn import NEG_INF
from spn.u... | 12,700 | 36.688427 | 89 | py |
spyn-repr | spyn-repr-master/tests/test_dataset.py | import dataset
import numpy
def test_sampling():
# loading nltcs
print('Loading datasets')
train, valid, test = dataset.load_train_val_test_csvs('nltcs')
# checking for their shape
n_instances = train.shape[0]
n_test_instances = test.shape[0]
n_valid_instances = valid.shape[0]
nltcs... | 3,136 | 31.010204 | 75 | py |
spyn-repr | spyn-repr-master/cltree/probs.py | import numpy
import numba
@numba.jit
def scope_union(factor_scope_1, factor_scope_2):
"""
A factor scope is a numpy boolean array
"""
return factor_scope_1 + factor_scope_2
#@numba.njit
def factor_length(factor_scope, feature_vals):
"""
WRITEME
"""
f_scope = feature_vals[factor_scope... | 4,185 | 26.539474 | 71 | py |
spyn-repr | spyn-repr-master/cltree/utils.py | import graphviz
from cltree.cltree import CLTree
def add_nodes(graph, nodes):
"""
"""
for n in nodes:
if isinstance(n, tuple):
graph.node(n[0], **n[1])
else:
graph.node(n)
return graph
def add_edges(graph, edges):
for e in edges:
if isinstance(e[0... | 2,592 | 21.745614 | 65 | py |
spyn-repr | spyn-repr-master/cltree/cltree.py | import numpy
import numba
import scipy.sparse
from scipy.sparse.csgraph import minimum_spanning_tree
from scipy.sparse.csgraph import depth_first_order
from spn import LOG_ZERO
@numba.njit
def safe_log(x):
"""
Assuming x to be a scalar
"""
if x > 0.0:
return numpy.log(x)
else:
r... | 25,021 | 31.923684 | 86 | py |
spyn-repr | spyn-repr-master/cltree/__init__.py | 0 | 0 | 0 | py | |
spyn-repr | spyn-repr-master/cltree/tests/test_probs.py | import numpy
from cltree.probs import numba_cumsum
from cltree.probs import scope_union
from cltree.probs import compute_factor_stride
from cltree.probs import n_factor_features
from cltree.probs import compute_factor_product
from cltree.probs import factor_product
from cltree.probs import factor_length
from numpy.te... | 5,481 | 35.791946 | 76 | py |
spyn-repr | spyn-repr-master/cltree/tests/test_cltree.py | from spn import LOG_ZERO
import numpy
from numpy.testing import assert_almost_equal
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
# from cltree.cltree import minimum_spanning_tree
# from cltree.cltree import minimum_spanning_tree_numba
from cltree.cltree import CLT... | 20,553 | 31.470774 | 86 | py |
spyn-repr | spyn-repr-master/algo/learnspn.py | import numpy
import numba
from scipy.misc import logsumexp
import sys
import itertools
try:
from time import perf_counter
except:
from time import time
perf_counter = time
from spn import MARG_IND
from spn import LOG_ZERO
from spn import RND_SEED
from spn.linked.nodes import CategoricalSmoothedNode
f... | 61,098 | 37.044209 | 98 | py |
spyn-repr | spyn-repr-master/algo/__init__.py | 0 | 0 | 0 | py | |
spyn-repr | spyn-repr-master/algo/dataslice.py | import numpy
try:
from time import perf_counter
except:
from time import time
perf_counter = time
from spn import LOG_ZERO
class DataSlice(object):
"""
A little util class for storing
the sets of indexes for the instances and features
considered
"""
class_counter = 0
@clas... | 4,572 | 24.547486 | 96 | py |
spyn-repr | spyn-repr-master/spn/utils.py | try:
from itertools import izip as zip
except:
pass
import itertools
from itertools import tee
import numpy
import scipy
import scipy.stats
import os
import visualize
import pandas
import glob
def pairwise(iterable):
"""
s = <s0, s1, ...>
s -> (s0,s1), (s1,s2), (s2, s3), ...
"""
a,... | 13,105 | 31.201474 | 87 | py |
spyn-repr | spyn-repr-master/spn/factory.py | from spn.linked.spn import Spn as SpnLinked
from spn.linked.layers import Layer as LayerLinked
from spn.linked.layers import SumLayer as SumLayerLinked
from spn.linked.layers import ProductLayer as ProductLayerLinked
from spn.linked.layers import CategoricalInputLayer
from spn.linked.layers import CategoricalSmoothedL... | 56,522 | 36.358229 | 97 | py |
spyn-repr | spyn-repr-master/spn/__init__.py | import sys
# marginalize indicator
MARG_IND = -1
# log of zero const, to avoid -inf
# numpy.exp(LOG_ZERO) = 0
LOG_ZERO = -1e3
def IS_LOG_ZERO(log_val):
"""
checks for a value to represent the logarithm of 0.
The identity to be verified is that:
IS_LOG_ZERO(x) && exp(x) == 0
according to the cons... | 6,449 | 21.089041 | 76 | py |
spyn-repr | spyn-repr-master/spn/theanok/initializations.py | import numpy as np
import theano
import theano.tensor as T
def floatX(X):
return np.asarray(X, dtype=theano.config.floatX)
def sharedX(X, dtype=theano.config.floatX, name=None):
return theano.shared(np.asarray(X, dtype=dtype), name=name)
def shared_zeros(shape, dtype=theano.config.floatX, name=None):
... | 1,260 | 22.351852 | 73 | py |
spyn-repr | spyn-repr-master/spn/theanok/layers.py | import numpy
import theano
import theano.tensor as T
from spn import LOG_ZERO
from .initializations import Initialization, sharedX, ndim_tensor
import os
#
# inspired by Keras
#
def exp_activation(x):
return T.exp(x)
def log_activation(x):
return T.log(x).clip(LOG_ZERO, 0.)
def log_sum_exp_activation(x... | 13,956 | 28.259958 | 105 | py |
spyn-repr | spyn-repr-master/spn/theanok/__init__.py | 0 | 0 | 0 | py | |
spyn-repr | spyn-repr-master/spn/theanok/spn.py | import numpy
import theano
import theano.tensor as T
from .initializations import ndim_tensor
import sys
# from .layers import TheanokLayer
# from .layers import SumLayer_logspace
# from .layers import ProductLayer_logspace
# from .layers import InputLayer_logspace
import theano.misc.pkl_utils
import pickle
from c... | 7,735 | 24.363934 | 86 | py |
spyn-repr | spyn-repr-master/spn/theanok/tests/test_layers.py | import numpy
from numpy.testing import assert_array_almost_equal
import theano
from spn.theanok.layers import SumLayer, ProductLayer
from ..layers import SumLayer_logspace
from ..layers import ProductLayer_logspace
from ..layers import MaxLayer_logspace
from spn import LOG_ZERO
def test_theano_sum_layer():
inp... | 8,281 | 29.116364 | 83 | py |
spyn-repr | spyn-repr-master/spn/theanok/tests/test_spn.py | import numpy
from numpy.testing import assert_array_almost_equal
import theano
from ..spn import SequentialSpn
from ..spn import BlockLayeredSpn
from ..layers import SumLayer, ProductLayer
from ..layers import SumLayer_logspace
from ..layers import ProductLayer_logspace
from ..layers import MaxLayer_logspace
from sp... | 11,884 | 29.24173 | 76 | py |
spyn-repr | spyn-repr-master/spn/theanok/tests/__init__.py | 0 | 0 | 0 | py | |
spyn-repr | spyn-repr-master/spn/linked/nodes.py |
from spn import utils
from spn import LOG_ZERO
from spn import MARG_IND
from spn import IS_LOG_ZERO
from spn import RND_SEED
import numpy
from math import log
from math import exp
from cltree.cltree import CLTree
import dataset
import numba
from collections import defaultdict
NODE_SYM = 'u' # unknown type
SUM_... | 28,838 | 28.159757 | 97 | py |
spyn-repr | spyn-repr-master/spn/linked/weight_learning.py | import numpy
from scipy.misc import logsumexp
import numba
from .nodes import SumNode
from .nodes import ProductNode
from .nodes import CategoricalSmoothedNode
from .nodes import CategoricalIndicatorNode
from .nodes import CLTreeNode
from ..factory import retrieve_children_parent_assoc
from collections import dequ... | 9,524 | 27.951368 | 83 | py |
spyn-repr | spyn-repr-master/spn/linked/learning.py | from spn.linked.spn import Spn
from spn.linked.nodes import SumNode
from spn.linked.nodes import ProductNode
from spn.linked.nodes import CategoricalSmoothedNode
from spn.linked.layers import SumLayer
from spn.linked.layers import ProductLayer
from spn.linked.layers import CategoricalSmoothedLayer
from spn.factory i... | 51,985 | 34.316576 | 79 | py |
spyn-repr | spyn-repr-master/spn/linked/layers.py | from spn.linked.nodes import SumNode
from spn.linked.nodes import ProductNode
from spn.linked.nodes import CategoricalIndicatorNode
from spn.linked.nodes import CategoricalSmoothedNode
from spn.linked.nodes import CLTreeNode
from math import exp
import numba
@numba.jit
def eval_numba(nodes):
for node in nodes:
... | 13,261 | 24.357553 | 99 | py |
spyn-repr | spyn-repr-master/spn/linked/__init__.py | 0 | 0 | 0 | py | |
spyn-repr | spyn-repr-master/spn/linked/representation.py | from .nodes import SumNode
from .nodes import ProductNode
from .nodes import mpe_states_from_leaf
from spn import RND_SEED
from spn import MARG_IND
from spn.linked.spn import evaluate_on_dataset
from spn.theanok.spn import evaluate_on_dataset_batch
from dataset import dataset_to_instances_set
from collections import... | 65,192 | 34.087729 | 98 | py |
spyn-repr | spyn-repr-master/spn/linked/spn.py | from .layers import Layer
from .layers import SumLayer
from .layers import ProductLayer
from .layers import compute_feature_vals
from spn import AbstractSpn, AbstractLayeredSpn
from spn import LOG_ZERO
from spn import RND_SEED
from .nodes import SumNode
from .nodes import ProductNode
from .nodes import sample_from_l... | 32,300 | 33.546524 | 97 | py |
spyn-repr | spyn-repr-master/spn/linked/tests/test_layers.py | from spn.linked.layers import Layer
from spn.linked.layers import ProductLayer
from spn.linked.layers import SumLayer
from spn.linked.layers import CategoricalInputLayer
from spn.linked.layers import CategoricalIndicatorLayer
from spn.linked.layers import CategoricalSmoothedLayer
from spn.linked.layers import Categoric... | 27,427 | 27.422798 | 78 | py |
spyn-repr | spyn-repr-master/spn/linked/tests/test_weight_learning.py | import numpy
from numpy.testing import assert_array_almost_equal
from ..weight_learning import evaluate_indicator_node
from ..weight_learning import evaluate_categorical_node
from ..weight_learning import evaluate_sum_node
from ..weight_learning import evaluate_product_node
from ..weight_learning import ml_evaluation
... | 9,356 | 34.988462 | 98 | py |
spyn-repr | spyn-repr-master/spn/linked/tests/test_representation.py | import dataset
from spn import MARG_IND
from ..spn import Spn
from ..spn import evaluate_on_dataset
from ..layers import SumLayer
from ..layers import ProductLayer
from ..layers import CategoricalIndicatorLayer
from ..nodes import SumNode
from ..nodes import ProductNode
from ..nodes import CategoricalIndicatorNode
... | 32,624 | 34.655738 | 101 | py |
spyn-repr | spyn-repr-master/spn/linked/tests/test_spn.py | from spn.linked.spn import Spn
from spn.linked.layers import SumLayer
from spn.linked.layers import ProductLayer
from spn.linked.layers import CategoricalIndicatorLayer
from spn.linked.layers import CategoricalSmoothedLayer
from spn.linked.layers import CategoricalInputLayer
from ..nodes import SumNode
from ..nodes i... | 30,480 | 28.679649 | 95 | py |
spyn-repr | spyn-repr-master/spn/linked/tests/__init__.py | 0 | 0 | 0 | py | |
spyn-repr | spyn-repr-master/spn/linked/tests/test_nodes.py | from spn.linked.nodes import Node
from spn.linked.nodes import SumNode
from spn.linked.nodes import ProductNode
from spn.linked.nodes import CategoricalIndicatorNode
from spn.linked.nodes import CategoricalSmoothedNode
from spn.linked.nodes import CLTreeNode
from spn.tests import compute_smoothed_ll
from spn import L... | 21,756 | 27.515072 | 79 | py |
spyn-repr | spyn-repr-master/spn/linked/tests/test_learning.py | from spn.linked.spn import Spn
from spn.linked.layers import CategoricalIndicatorLayer
from spn.linked.layers import SumLayer
from spn.linked.layers import ProductLayer
from spn.linked.nodes import SumNode
from spn.linked.nodes import ProductNode
import numpy
from numpy.testing import assert_almost_equal
from numpy.... | 15,315 | 30.841996 | 88 | py |
normalizing_flows | normalizing_flows-master/test.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributions as D
from torch.utils.data import DataLoader, Dataset
import unittest
from unittest.mock import MagicMock
from maf import MADE, MADEMOG, MAF, MAFMOG, RealNVP, BatchNorm, LinearMaskedCoupling, train
from glow import Actnorm, ... | 17,209 | 45.016043 | 156 | py |
normalizing_flows | normalizing_flows-master/data.py | from functools import partial
import numpy as np
import torch
import torchvision.transforms as T
from torch.utils.data import DataLoader, TensorDataset
import datasets
# --------------------
# Helper functions
# --------------------
def logit(x, eps=1e-5):
x.clamp_(eps, 1 - eps)
return x.log() - (1 - x).log... | 4,218 | 36.336283 | 146 | py |
normalizing_flows | normalizing_flows-master/glow.py | """
Glow: Generative Flow with Invertible 1x1 Convolutions
arXiv:1807.03039v2
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributions as D
import torchvision.transforms as T
from torchvision.utils import save_image, make_grid
from torch.utils.data import DataLoader
from torch.... | 35,698 | 45.302205 | 181 | py |
normalizing_flows | normalizing_flows-master/bnaf.py | """
Implementation of Block Neural Autoregressive Flow
http://arxiv.org/abs/1904.04676
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributions as D
from torch.utils.data import DataLoader, TensorDataset
import math
import os
import time
import argparse
import pprint
from func... | 20,690 | 42.836864 | 163 | py |
normalizing_flows | normalizing_flows-master/maf.py | """
Masked Autoregressive Flow for Density Estimation
arXiv:1705.07057v4
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributions as D
import torchvision.transforms as T
from torchvision.utils import save_image
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot a... | 31,985 | 41.762032 | 169 | py |
normalizing_flows | normalizing_flows-master/planar_flow.py | """
Variational Inference with Normalizing Flows
arXiv:1505.05770v6
"""
import torch
import torch.nn as nn
import torch.distributions as D
import math
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import argparse
parser = argparse.ArgumentParser()
# action
parser.add_argument('-... | 12,324 | 39.811258 | 151 | py |
normalizing_flows | normalizing_flows-master/datasets/power.py | import numpy as np
import matplotlib.pyplot as plt
import datasets
import datasets.util
class POWER:
class Data:
def __init__(self, data):
self.x = data.astype(np.float32)
self.N = self.x.shape[0]
def __init__(self):
trn, val, tst = load_data_normalised()
... | 2,216 | 24.77907 | 108 | py |
normalizing_flows | normalizing_flows-master/datasets/moons.py | import torch
import torch.distributions as D
from torch.utils.data import Dataset
from sklearn.datasets import make_moons
class MOONS(Dataset):
def __init__(self, dataset_size=25000, **kwargs):
self.x, self.y = make_moons(n_samples=dataset_size, shuffle=True, noise=0.05)
self.input_size = 2
... | 512 | 20.375 | 85 | py |
normalizing_flows | normalizing_flows-master/datasets/hepmass.py | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
from os.path import join
import datasets
import datasets.util
class HEPMASS:
"""
The HEPMASS data set.
http://archive.ics.uci.edu/ml/datasets/HEPMASS
"""
class Data:
def __init__(self,... | 3,029 | 28.134615 | 112 | py |
normalizing_flows | normalizing_flows-master/datasets/toy.py | import torch
import torch.distributions as D
from torch.utils.data import Dataset
class ToyDistribution(D.Distribution):
def __init__(self, flip_var_order):
super().__init__()
self.flip_var_order = flip_var_order
self.p_x2 = D.Normal(0, 4)
self.p_x1 = lambda x2: D.Normal(0.25 * x2*... | 1,214 | 27.255814 | 90 | py |
normalizing_flows | normalizing_flows-master/datasets/gas.py | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import datasets
import datasets.util
class GAS:
class Data:
def __init__(self, data):
self.x = data.astype(np.float32)
self.N = self.x.shape[0]
def __init__(self):
file = datasets.root + 'gas/e... | 1,954 | 22.27381 | 59 | py |
normalizing_flows | normalizing_flows-master/datasets/bsds300.py | import numpy as np
import h5py
import matplotlib.pyplot as plt
import datasets
import datasets.util
class BSDS300:
"""
A dataset of patches from BSDS300.
"""
class Data:
"""
Constructs the dataset.
"""
def __init__(self, data):
self.x = data[:]
... | 1,905 | 23.435897 | 77 | py |
normalizing_flows | normalizing_flows-master/datasets/download_celeba.py | """ Source -- https://github.com/nperraud/download-celebA-HQ/ """
import requests
import tarfile
import zipfile
import gzip
import os
import hashlib
import sys
from glob import glob
from urllib.request import urlretrieve
from subprocess import Popen
import argparse
from tqdm import tqdm
parser = argparse.ArgumentPars... | 8,603 | 32.478599 | 83 | py |
normalizing_flows | normalizing_flows-master/datasets/util.py | """
Select dataset functions from MAF repo
https://github.com/gpapamak/maf/blob/master/util.py
"""
import numpy as np
import matplotlib.pyplot as plt
def plot_hist_marginals(data, lims=None, gt=None):
"""
Plots marginal histograms and pairwise scatter plots of a dataset.
"""
n_bins = int(np.sqrt(data... | 2,135 | 27.864865 | 117 | py |
normalizing_flows | normalizing_flows-master/datasets/miniboone.py | import numpy as np
import matplotlib.pyplot as plt
import datasets
import datasets.util
class MINIBOONE:
class Data:
def __init__(self, data):
self.x = data.astype(np.float32)
self.N = self.x.shape[0]
def __init__(self):
file = datasets.root + 'miniboone/data.npy'... | 2,250 | 26.790123 | 96 | py |
normalizing_flows | normalizing_flows-master/datasets/__init__.py | root = 'data/'
#from .power import POWER
#from .gas import GAS
#from .hepmass import HEPMASS
#from .miniboone import MINIBOONE
#from .bsds300 import BSDS300
#from .toy import TOY
#from .moons import MOONS
#from .mnist import MNIST
#from torchvision.datasets import MNIST, CIFAR10
| 283 | 19.285714 | 48 | py |
normalizing_flows | normalizing_flows-master/datasets/celeba.py | import os
from PIL import Image
import numpy as np
import torch
from torch.utils.data import Dataset
class CelebA(Dataset):
processed_file = 'processed.pt'
partition_file = 'Eval/list_eval_partition.txt'
attr_file = 'Anno/list_attr_celeba.txt'
img_folder = 'Img/img_align_celeba'
attr_names = '5_o_... | 5,419 | 43.793388 | 490 | py |
normalizing_flows | normalizing_flows-master/datasets/mnist.py | import numpy as np
import gzip
import pickle
import matplotlib.pyplot as plt
import datasets
import datasets.util as util
class MNIST:
"""
The MNIST dataset of handwritten digits.
"""
alpha = 1.0e-6
class Data:
"""
Constructs the dataset.
"""
def __init__(self, ... | 2,886 | 28.459184 | 97 | py |
SpinalNet | SpinalNet-master/Regression/Regression_NN_and_SpinalNet.py | # -*- coding: utf-8 -*-
"""
This script performs regression on toy datasets.
There exist several relations between inputs and output.
We investigate both of the traditional feed-forward and SpinalNet
for all of these input-output relations.
----------
Multiplication:
y = x1*x2*x3*x4*x5*x6*x7*x8 + 0.2*torch.rand(x1... | 5,420 | 28.302703 | 105 | py |
SpinalNet | SpinalNet-master/Transfer Learning/Transfer_Learning_hymenoptera.py | '''
Most part of the code and dataset is copied from PyTorch demo:
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
'''
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
impor... | 7,504 | 29.384615 | 78 | py |
SpinalNet | SpinalNet-master/Transfer Learning/Transfer_Learning_STL10.py | '''
We write this code with the help of PyTorch demo:
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
Data is downloaded from pytorch and divided into folders
using script 'Pytorch_data_to_folders.py'
Effects:
transforms.Resize((272,272)),
transforms.RandomRotation(15,),
... | 7,704 | 30.068548 | 93 | py |
SpinalNet | SpinalNet-master/Transfer Learning/Transfer_Learning_CIFAR100.py | '''
We write this code with the help of PyTorch demo:
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
Dataset is distributed in folders with following script:
https://au.mathworks.com/matlabcentral/answers/329597-save-cifar-100-images
Performances:
Data augmentation:
transforms.Res... | 8,199 | 28.818182 | 93 | py |
SpinalNet | SpinalNet-master/Transfer Learning/Transfer_Learning_CIFAR10.py | '''
We write this code with the help of PyTorch demo:
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
The dataset is downloaded from https://www.kaggle.com/swaroopkml/cifar10-pngs-in-folders
Performances:
Data augmentation:
transforms.Resize((272,272)),
transforms.RandomRotati... | 9,644 | 30.314935 | 93 | py |
SpinalNet | SpinalNet-master/Transfer Learning/Transfer_Learning_SVHN.py | '''
Data is downloaded from pytorch and divided into folders
using script 'Pytorch_data_to_folders.py'
Effects:
transforms.Resize((272,320)),
transforms.RandomRotation(15,),
transforms.CenterCrop(272),
transforms.RandomCrop(256),
transforms.ToTensor(),
wide_resnet101_2 Spinal... | 9,514 | 30.611296 | 93 | py |
SpinalNet | SpinalNet-master/Transfer Learning/Transfer_Learning_CINIC10.py | '''
We write this code with the help of PyTorch demo:
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
The Dataset is downloaded from https://www.kaggle.com/mengcius/cinic10
Effects:
transforms.Resize((272,272)),
transforms.RandomRotation(15,),
transforms.RandomC... | 10,712 | 31.761468 | 113 | py |
SpinalNet | SpinalNet-master/Transfer Learning/Transfer_Learning_Caltech101.py | '''
We write this code with the help of PyTorch demo:
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
Dataset is Downloaded from https://www.kaggle.com/huangruichu/caltech101/version/2
Effects:
transforms.Resize((230,230)),
transforms.RandomRotation(15,),
transfo... | 10,203 | 30.788162 | 93 | py |
SpinalNet | SpinalNet-master/Transfer Learning/Pytorch_data_to_folders.py | # -*- coding: utf-8 -*-
"""
We need to create train and val folders manually before running the script
@author: Dipu
"""
import torchvision
import matplotlib
import matplotlib.pyplot as plt
import numpy
import imageio
import os
data_train = torchvision.datasets.SVHN('./data', split='train', download=True,
... | 1,195 | 28.170732 | 83 | py |
SpinalNet | SpinalNet-master/Transfer Learning/Transfer_Learning_Fruits360.py | '''
We write this code with the help of PyTorch demo:
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
Dataset is Downloaded from https://www.kaggle.com/moltean/fruits
Effects:
transforms.Resize((140,140)),
transforms.RandomRotation(15,),
transforms.RandomResizedC... | 9,786 | 32.064189 | 93 | py |
SpinalNet | SpinalNet-master/Transfer Learning/Transfer_Learning_Stanford_Cars.py |
'''
Stanford Cars
We write this code with the help of PyTorch demo:
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
Dataset is downloaded from https://www.kaggle.com/jutrera/stanford-car-dataset-by-classes-folder?
Effect:
transforms.Resize((456,456)),
transforms.RandomRotat... | 9,929 | 30.52381 | 97 | py |
SpinalNet | SpinalNet-master/Transfer Learning/Transfer_Learning_Oxford102flower.py | '''
We write this code with the help of PyTorch demo:
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
The dataset is downloaded from https://www.kaggle.com/c/oxford-102-flower-pytorch/data
Effects:
transforms.Resize((464,464)),
transforms.RandomRotation(15,),
tra... | 9,691 | 30.986799 | 93 | py |
SpinalNet | SpinalNet-master/Transfer Learning/Transfer_Learning_Bird225.py | '''
We write this code with the help of PyTorch demo:
https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html
Data Link:
https://www.kaggle.com/gpiosenka/100-bird-species
Version 30
Downloaded on 20/08/2020
Performances:
Data augmentation:
transforms.Resize((230,230)),
t... | 8,387 | 28.850534 | 120 | py |
SpinalNet | SpinalNet-master/Transfer Learning/Transfer_Learning_MNIST.py | # Execution info: https://www.kaggle.com/dipuk0506/transfer-learning-on-mnist
from __future__ import print_function, division
import matplotlib
import imageio
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision impo... | 10,753 | 29.725714 | 93 | py |
SpinalNet | SpinalNet-master/CIFAR-10/ResNet_default_and_SpinalFC_CIFAR10.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal ResNet code for CIFAR-10.
This code trains both NNs as two different models.
There is option of choosing ResNet18(), ResNet34(), SpinalResNet18(), or
SpinalResNet34().
This code randomly changes the learning rate to get a good result.
@author: ... | 13,289 | 29.906977 | 101 | py |
SpinalNet | SpinalNet-master/CIFAR-10/VGG_default_and_SpinalFC_CIFAR10.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal VGG code for CIFAR-10.
This code trains both NNs as two different models.
There is option of choosing NN among:
vgg11_bn(), vgg13_bn(), vgg16_bn(), vgg19_bn() and
Spinalvgg11_bn(), Spinalvgg13_bn(), Spinalvgg16_bn(), Spinalvgg19_bn()
Thi... | 8,991 | 28.578947 | 116 | py |
SpinalNet | SpinalNet-master/CIFAR-10/CNN_dropout_CIFAR10.py | # -*- coding: utf-8 -*-
"""
This Script contains the default CNN dropout code for comparison.
The code is collected and changed from:
https://zhenye-na.github.io/2018/09/28/pytorch-cnn-cifar10.html
@author: Dipu
"""
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transfo... | 4,878 | 27.04023 | 97 | py |
SpinalNet | SpinalNet-master/CIFAR-10/CNN_dropout_SpinalFC_CIFAR10.py | # -*- coding: utf-8 -*-
"""
This Script contains the CNN dropout with Spinal fully-connected layer.
@author: Dipu
"""
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import random
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else ... | 7,532 | 29.746939 | 93 | py |
SpinalNet | SpinalNet-master/MNIST_VGG/EMNIST_digits_VGG_and _SpinalVGG.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal VGG code for EMNIST(Digits).
This code trains both NNs as two different models.
This code randomly changes the learning rate to get a good result.
@author: Dipu
"""
import torch
import torchvision
import torch.nn as nn
import math
import torch... | 11,675 | 32.551724 | 116 | py |
SpinalNet | SpinalNet-master/MNIST_VGG/KMNIST_VGG_and_SpinalVGG.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal VGG code for kMNIST.
This code trains both NNs as two different models.
This code randomly changes the learning rate to get a good result.
@author: Dipu
"""
import torch
import torchvision
import torch.nn as nn
import math
import torch.nn.func... | 11,721 | 32.301136 | 116 | py |
SpinalNet | SpinalNet-master/MNIST_VGG/MNIST_VGG_and_SpinalVGG.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal VGG code for MNIST.
This code trains both NNs as two different models.
This code randomly changes the learning rate to get a good result.
@author: Dipu
"""
import torch
import torchvision
import torch.nn as nn
import math
import torch.nn.funct... | 11,616 | 32.191429 | 116 | py |
SpinalNet | SpinalNet-master/MNIST_VGG/FashionMNIST_VGG_and _SpinalVGG.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal VGG code for Fashion-MNIST.
This code trains both NNs as two different models.
This code randomly changes the learning rate to get a good result.
@author: Dipu
"""
import torch
import torchvision
import torch.nn as nn
import math
import torch.... | 11,734 | 32.528571 | 116 | py |
SpinalNet | SpinalNet-master/MNIST_VGG/EMNIST_letters_VGG_and _SpinalVGG.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal VGG code for EMNIST(Letters).
This code trains both NNs as two different models.
This code randomly changes the learning rate to get a good result.
@author: Dipu
"""
import torch
import torchvision
import torch.nn as nn
import math
import torch.n... | 11,677 | 32.751445 | 116 | py |
SpinalNet | SpinalNet-master/MNIST_VGG/QMNIST_VGG_and _SpinalVGG.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal VGG code for QMNIST.
This code trains both NNs as two different models.
This code randomly changes the learning rate to get a good result.
@author: Dipu
"""
import torch
import torchvision
import torch.nn as nn
import math
import torch.nn.func... | 11,636 | 32.34384 | 116 | py |
SpinalNet | SpinalNet-master/MNIST/Arch2_Fashion_MNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the SpinalNet Arch2 Fashion MNIST code.
@author: Dipu
"""
import torch
import torchvision
import numpy as np
n_epochs = 200
batch_size_train = 64
batch_size_test = 1000
learning_rate = 0.005
momentum = 0.5
log_interval = 500
first_HL =300
max_accuracy= 0.0
random... | 10,213 | 34.099656 | 105 | py |
SpinalNet | SpinalNet-master/MNIST/Arch2_KMNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the SpinalNet Arch2 KMNIST code.
@author: Dipu
"""
import torch
import torchvision
n_epochs = 200
batch_size_train = 64
batch_size_test = 1000
momentum = 0.5
log_interval = 5000
first_HL = 50
random_seed = 1
torch.backends.cudnn.enabled = False
torch.manu... | 9,711 | 32.839721 | 117 | py |
SpinalNet | SpinalNet-master/MNIST/SpinalNet_MNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the SpinalNet MNIST code.
It ususlly provides better performance for the same number of epoch.
The same code can also be used for KMNIST, QMNIST and FashionMNIST.
torchvision.datasets.MNIST needs to be changed to
torchvision.datasets.FashionMNIST for FashionMNIST si... | 5,560 | 29.387978 | 78 | py |
SpinalNet | SpinalNet-master/MNIST/Arch2_QMNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the SpinalNet Arch2 QMNIST code.
@author: Dipu
"""
import torch
import torchvision
n_epochs = 200
batch_size_train = 64
batch_size_test = 1000
momentum = 0.5
log_interval = 5000
first_HL = 50
prob = 0.5
random_seed = 1
torch.backends.cudnn.enabled = False... | 9,751 | 32.512027 | 117 | py |
SpinalNet | SpinalNet-master/MNIST/default_pytorch_EMNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the default EMNIST code for comparison.
The code is collected from:
nextjournal.com/gkoehler/pytorch-mnist
As the EMNIST needs split='digits', we make a different file for EMNIST
@author: Dipu
"""
import torch
import torchvision
n_epochs = 8
batch_size_train = 6... | 4,274 | 28.081633 | 84 | py |
SpinalNet | SpinalNet-master/MNIST/Arch2_MNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the SpinalNet Arch2 MNIST code.
@author: Dipu
"""
import torch
import torchvision
import numpy as np
n_epochs = 200
batch_size_train = 64
batch_size_test = 1000
learning_rate = 0.01
momentum = 0.5
log_interval = 500
first_HL =30
max_accuracy= 0.0
torch.backends.cud... | 10,686 | 33.253205 | 105 | py |
SpinalNet | SpinalNet-master/MNIST/default_pytorch_MNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the default MNIST code for comparison.
The code is collected from:
nextjournal.com/gkoehler/pytorch-mnist
The same code can also be used for KMNIST, QMNIST and FashionMNIST.
torchvision.datasets.MNIST needs to be changed to
torchvision.datasets.FashionMNIST for ... | 4,349 | 28.391892 | 76 | py |
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