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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mopo | mopo-master/softlearning/policies/gaussian_policy.py | """GaussianPolicy."""
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
from softlearning.distributions.squash_bijector import SquashBijector
from softlearning.models.feedforward import feedforward_model
from .base_policy import LatentSpacePolicy
SC... | 8,809 | 33.147287 | 78 | py |
mopo | mopo-master/softlearning/policies/uniform_policy.py | from collections import OrderedDict
import tensorflow as tf
from .base_policy import BasePolicy
class UniformPolicy(BasePolicy):
def __init__(self, input_shapes, output_shape, action_range=(-1.0, 1.0)):
super(UniformPolicy, self).__init__()
self._Serializable__initialize(locals())
self.... | 1,887 | 26.362319 | 77 | py |
mopo | mopo-master/softlearning/models/feedforward.py | import tensorflow as tf
from softlearning.utils.keras import PicklableKerasModel
def feedforward_model(input_shapes,
output_size,
hidden_layer_sizes,
activation='relu',
output_activation='linear',
preproces... | 1,265 | 26.521739 | 68 | py |
mopo | mopo-master/softlearning/algorithms/sac.py | from collections import OrderedDict
from numbers import Number
import numpy as np
import tensorflow as tf
from tensorflow.python.training import training_util
from .rl_algorithm import RLAlgorithm
def td_target(reward, discount, next_value):
return reward + discount * next_value
class SAC(RLAlgorithm):
""... | 14,507 | 32.897196 | 80 | py |
mopo | mopo-master/softlearning/algorithms/sql.py | from collections import OrderedDict
import numpy as np
import tensorflow as tf
from softlearning.misc.kernel import adaptive_isotropic_gaussian_kernel
from .rl_algorithm import RLAlgorithm
EPS = 1e-6
def assert_shape(tensor, expected_shape):
tensor_shape = tensor.shape.as_list()
assert len(tensor_shape) =... | 16,532 | 35.098253 | 86 | py |
mopo | mopo-master/softlearning/algorithms/rl_algorithm.py | import abc
from collections import OrderedDict
from itertools import count
import gtimer as gt
import math
import os
import pdb
import tensorflow as tf
import numpy as np
from softlearning.samplers import rollouts
from softlearning.misc.utils import save_video
class RLAlgorithm(tf.contrib.checkpoint.Checkpointable)... | 12,728 | 34.066116 | 129 | py |
mopo | mopo-master/softlearning/samplers/remote_sampler.py | import pickle
from collections import OrderedDict
import ray
import tensorflow as tf
import numpy as np
from .base_sampler import BaseSampler
from .utils import rollout
class RemoteSampler(BaseSampler):
def __init__(self, **kwargs):
super(RemoteSampler, self).__init__(**kwargs)
self._remote_en... | 3,589 | 29.683761 | 76 | py |
mopo | mopo-master/softlearning/utils/keras.py | import tempfile
import tensorflow as tf
class PicklableKerasModel(tf.keras.Model):
def __getstate__(self):
with tempfile.NamedTemporaryFile(suffix='.hdf5', delete=True) as fd:
tf.keras.models.save_model(self, fd.name, overwrite=True)
model_str = fd.read()
d = {'model_str':... | 1,072 | 31.515152 | 76 | py |
EATA | EATA-main/main.py | from logging import debug
import os
import time
import argparse
import json
import random
import math
from utils.utils import get_logger
from utils.cli_utils import *
from dataset.selectedRotateImageFolder import prepare_test_data
import torch
import torch.nn.functional as F
import numpy as np
import tent
import... | 8,797 | 43.211055 | 235 | py |
EATA | EATA-main/tent.py | """
Copyright to Tent Authors ICLR 2021 Spotlight
"""
from argparse import ArgumentDefaultsHelpFormatter
from copy import deepcopy
import torch
import torch.nn as nn
import torch.jit
from torch.autograd import Variable
class Tent(nn.Module):
"""Tent adapts a model by entropy minimization during testing.
On... | 5,126 | 33.877551 | 118 | py |
EATA | EATA-main/eata.py | """
Copyright to EATA ICML 2022 Authors, 2022.03.20
Based on Tent ICLR 2021 Spotlight.
"""
from argparse import ArgumentDefaultsHelpFormatter
from copy import deepcopy
import torch
import torch.nn as nn
import torch.jit
import math
import torch.nn.functional as F
class EATA(nn.Module):
"""EATA adapts a model ... | 8,585 | 40.679612 | 279 | py |
EATA | EATA-main/dataset/selectedRotateImageFolder.py | import os
import copy
import random
import math
import numpy as np
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import torch.utils.data
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229... | 7,787 | 37.94 | 151 | py |
EATA | EATA-main/models/Res.py | # Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.nn as nn
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.mo... | 11,645 | 36.934853 | 106 | py |
EATA | EATA-main/utils/utils.py | import os
import sys
import logging
import random
import numpy as np
import torch
import torch.nn as nn
device = "cuda:0" if torch.cuda.is_available() else "cpu"
def mean(items):
return sum(items)/len(items)
def max_with_index(values):
best_v = values[0]
best_i = 0
for i, v in enumerate(values):
... | 3,308 | 25.902439 | 98 | py |
EATA | EATA-main/utils/cli_utils.py | import os
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
... | 3,444 | 30.036036 | 88 | py |
WaveRNN | WaveRNN-master/train_wavernn.py | import time
import numpy as np
import torch
from torch import optim
import torch.nn.functional as F
from utils.display import stream, simple_table
from utils.dataset import get_vocoder_datasets
from utils.distribution import discretized_mix_logistic_loss
from utils import hparams as hp
from models.fatchord_version impo... | 6,132 | 37.33125 | 137 | py |
WaveRNN | WaveRNN-master/train_tacotron.py | import torch
from torch import optim
import torch.nn.functional as F
from utils import hparams as hp
from utils.display import *
from utils.dataset import get_tts_datasets
from utils.text.symbols import symbols
from utils.paths import Paths
from models.tacotron import Tacotron
import argparse
from utils import data_par... | 7,541 | 36.152709 | 137 | py |
WaveRNN | WaveRNN-master/gen_tacotron.py | import torch
from models.fatchord_version import WaveRNN
from utils import hparams as hp
from utils.text.symbols import symbols
from utils.paths import Paths
from models.tacotron import Tacotron
import argparse
from utils.text import text_to_sequence
from utils.display import save_attention, simple_table
from utils.dsp... | 7,192 | 41.56213 | 137 | py |
WaveRNN | WaveRNN-master/gen_wavernn.py | from utils.dataset import get_vocoder_datasets
from utils.dsp import *
from models.fatchord_version import WaveRNN
from utils.paths import Paths
from utils.display import simple_table
import torch
import argparse
from pathlib import Path
def gen_testset(model: WaveRNN, test_set, samples, batched, target, overlap, sav... | 5,559 | 37.881119 | 137 | py |
WaveRNN | WaveRNN-master/quick_start.py | import torch
from models.fatchord_version import WaveRNN
from utils import hparams as hp
from utils.text.symbols import symbols
from models.tacotron import Tacotron
import argparse
from utils.text import text_to_sequence
from utils.display import save_attention, simple_table
import zipfile, os
os.makedirs('quick_star... | 4,707 | 37.276423 | 137 | py |
WaveRNN | WaveRNN-master/models/tacotron.py | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pathlib import Path
from typing import Union
class HighwayNetwork(nn.Module):
def __init__(self, size):
super().__init__()
self.W1 = nn.Linear(size, size)
self.W2 = nn.Linear(size, size)
... | 17,427 | 36.080851 | 110 | py |
WaveRNN | WaveRNN-master/models/deepmind_version.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.display import *
from utils.dsp import *
import numpy as np
class WaveRNN(nn.Module):
def __init__(self, hidden_size=896, quantisation=256):
super(WaveRNN, self).__init__()
self.hidden_size = hidden_size
... | 7,340 | 40.710227 | 87 | py |
WaveRNN | WaveRNN-master/models/fatchord_version.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.distribution import sample_from_discretized_mix_logistic
from utils.display import *
from utils.dsp import *
import os
import numpy as np
from pathlib import Path
from typing import Union
class ResBlock(nn.Module):
def __init__(self, di... | 15,271 | 34.027523 | 113 | py |
WaveRNN | WaveRNN-master/utils/checkpoints.py | import torch
from utils.paths import Paths
from models.tacotron import Tacotron
def get_checkpoint_paths(checkpoint_type: str, paths: Paths):
"""
Returns the correct checkpointing paths
depending on whether model is Vocoder or TTS
Args:
checkpoint_type: Either 'voc' or 'tts'
paths: Pa... | 5,010 | 38.148438 | 93 | py |
WaveRNN | WaveRNN-master/utils/dataset.py | import pickle
import random
import torch
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import Sampler
from utils.dsp import *
from utils import hparams as hp
from utils.text import text_to_sequence
from utils.paths import Paths
from pathlib import Path
################################... | 6,718 | 29.130045 | 100 | py |
WaveRNN | WaveRNN-master/utils/distribution.py | import numpy as np
import torch
import torch.nn.functional as F
def log_sum_exp(x):
""" numerically stable log_sum_exp implementation that prevents overflow """
# TF ordering
axis = len(x.size()) - 1
m, _ = torch.max(x, dim=axis)
m2, _ = torch.max(x, dim=axis, keepdim=True)
return m + torch.lo... | 4,812 | 35.18797 | 99 | py |
WaveRNN | WaveRNN-master/utils/__init__.py | # Make it explicit that we do it the Python 3 way
from __future__ import absolute_import, division, print_function, unicode_literals
from builtins import *
import sys
import torch
import re
from importlib.util import spec_from_file_location, module_from_spec
from pathlib import Path
from typing import Union
# Credit... | 3,977 | 37.25 | 128 | py |
WaveRNN | WaveRNN-master/notebooks/models/wavernn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class WaveRNN(nn.Module) :
def __init__(self, hidden_size=896, quantisation=256) :
super(WaveRNN, self).__init__()
self.hidden_size = hidden_size
self.split_size = hidden_size // 2
# The main matmu... | 7,017 | 39.802326 | 87 | py |
bert-syntax | bert-syntax-master/eval_bert.py | # coding=utf-8
from pytorch_pretrained_bert import BertForMaskedLM,tokenization
import torch
import sys
import csv
model_name = 'bert-large-uncased'
if 'base' in sys.argv: model_name = 'bert-base-uncased'
print("using model:",model_name,file=sys.stderr)
bert=BertForMaskedLM.from_pretrained(model_name)
tokenizer=tokeni... | 4,137 | 31.077519 | 170 | py |
TransCoder | TransCoder-main/translate.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# Translate sentences from the input stream.
# The model will be faster is sentences are sorted by length.
# Input sentences mus... | 7,637 | 40.51087 | 133 | py |
TransCoder | TransCoder-main/XLM/src/optim.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import re
import math
import inspect
import torch
from torch import optim
class Adam(optim.Optimizer):
"""
Same as h... | 10,577 | 36.246479 | 101 | py |
TransCoder | TransCoder-main/XLM/src/slurm.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from logging import getLogger
import os
import sys
import torch
import socket
import signal
import subprocess
logger = getLog... | 6,355 | 35.739884 | 99 | py |
TransCoder | TransCoder-main/XLM/src/utils.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import getpass
import os
import pickle
import random
import re
import subprocess
import sys
from concurrent.fut... | 29,283 | 39.391724 | 198 | py |
TransCoder | TransCoder-main/XLM/src/trainer.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import math
import time
from logging import getLogger
from collections import OrderedDict
import numpy as np
import t... | 38,415 | 37.725806 | 140 | py |
TransCoder | TransCoder-main/XLM/src/evaluation/evaluator.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import sys
from logging import getLogger
import os
import subprocess
from collections import OrderedDict
import numpy as np
impo... | 27,320 | 42.435612 | 136 | py |
TransCoder | TransCoder-main/XLM/src/data/dictionary.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import os
import numpy as np
import torch
from logging import getLogger
logger = getLogger()
BOS_WORD = '<s>'
EOS_WORD = '<... | 7,880 | 32.53617 | 96 | py |
TransCoder | TransCoder-main/XLM/src/data/dataset.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from logging import getLogger
import math
import numpy as np
import torch
logger = getLogger()
class StreamDataset(object):... | 16,787 | 34.643312 | 124 | py |
TransCoder | TransCoder-main/XLM/src/data/loader.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from logging import getLogger
import os
import numpy as np
import torch
from .dataset import StreamDataset, Dataset, ParallelD... | 14,870 | 38.868633 | 138 | py |
TransCoder | TransCoder-main/XLM/src/model/pretrain.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from logging import getLogger
import io
import numpy as np
import torch
logger = getLogger()
def load_fasttext_model(path):... | 3,012 | 29.744898 | 89 | py |
TransCoder | TransCoder-main/XLM/src/model/embedder.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from logging import getLogger
import torch
from .transformer import TransformerModel
from ..data.dictionary import Dictionary,... | 4,977 | 33.569444 | 128 | py |
TransCoder | TransCoder-main/XLM/src/model/transformer.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from logging import getLogger
import math
import itertools
import numpy as np
import torch
import torch.nn as nn
import torch.n... | 30,971 | 37.379182 | 147 | py |
TransCoder | TransCoder-main/XLM/src/model/__init__.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from logging import getLogger
import os
import torch
from .pretrain import load_embeddings
# , TRANSFORMER_LAYER_PARAMS
from .... | 11,449 | 44.07874 | 136 | py |
TransCoder | TransCoder-main/preprocessing/src/test_tokenize_python.py | # Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from preprocessing.src.code_tokenizer import tokenize_python, detokenize_python
TESTS = []
TESTS.append((
"a = [3.14,4]",... | 5,026 | 22.712264 | 150 | py |
QuatE | QuatE-master/quate.py | class QuatE(KBCModel):
def __init__(
self, sizes: Tuple[int, int, int], rank: int,
init_size: float = 1e-3
):
super(QuatE, self).__init__()
self.sizes = sizes
self.rank = rank
self.embeddings = nn.ModuleList([
nn.Embedding(s, 4 * rank, sparse=... | 4,200 | 44.663043 | 143 | py |
QuatE | QuatE-master/config/Config.py | # coding:utf-8
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import os
import time
import sys
import datetime
import ctypes
import json
import numpy as np
class MyDataParallel(nn.DataParallel):
def _getattr__(self, name):
return getattr(self.module, nam... | 19,078 | 35.203036 | 88 | py |
QuatE | QuatE-master/models/QuatE.py | import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import numpy as np
from .Model import Model
from numpy.random import RandomState
class QuatE(Model):
def __init__(self, config):
super(QuatE,... | 7,808 | 41.440217 | 123 | py |
QuatE | QuatE-master/models/OctonionE.py | import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import numpy as np
from .Model import Model
from numpy.random import RandomState
class OctonionE(Model):
def __init__(self, config):
super(Oc... | 9,155 | 43.882353 | 118 | py |
QuatE | QuatE-master/models/Model.py | import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
class Model(nn.Module):
def __init__(self, config):
super(Model, self).__init__()
self.config = config
self.batch_h = None
self.batch_t = None
... | 1,425 | 32.952381 | 84 | py |
point_cloud_anomaly_detection | point_cloud_anomaly_detection-main/test.py | import argparse
import os
import random
from typing import List
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import yaml
from addict import Dict
from sklearn.metrics import auc, roc_curve
from torch.utils.data import DataLoader
from libs.checkpoint import resume
from libs.datase... | 10,040 | 30.280374 | 85 | py |
point_cloud_anomaly_detection | point_cloud_anomaly_detection-main/train.py | import argparse
import os
import random
import numpy as np
import torch
import wandb
import yaml
from addict import Dict
# from emd.emd_module import emdModule
from libs.checkpoint import save_checkpoint
from libs.dataset import ShapeNeth5pyDataset
from libs.foldingnet import SkipValiationalFoldingNet
from libs.helpe... | 4,094 | 25.25 | 102 | py |
point_cloud_anomaly_detection | point_cloud_anomaly_detection-main/libs/checkpoint.py | import os
from typing import Tuple
import torch
import torch.nn as nn
import torch.optim as optim
def save_checkpoint(
result_path: str,
epoch: int,
model: nn.Module,
optimizer: optim.Optimizer,
) -> None:
save_states = {
"epoch": epoch,
"model_state_dict": model.state_dict(),
... | 1,073 | 23.409091 | 85 | py |
point_cloud_anomaly_detection | point_cloud_anomaly_detection-main/libs/loss.py | # from collections import Counter, defaultdict
import torch
import torch.nn as nn
# from ortools.linear_solver import pywraplp
class ChamferLoss(nn.Module):
def __init__(self):
super(ChamferLoss, self).__init__()
self.use_cuda = torch.cuda.is_available()
def batch_pairwise_dist(self, x, y):... | 1,506 | 32.488889 | 85 | py |
point_cloud_anomaly_detection | point_cloud_anomaly_detection-main/libs/helper.py | import os
import time
from typing import Any, List, Tuple
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import wandb
from torch import optim
from torch.distributions import Categorical
from torch.utils.data import DataLoader
from .emd.emd_m... | 15,756 | 32.033543 | 88 | py |
point_cloud_anomaly_detection | point_cloud_anomaly_detection-main/libs/dataset.py | import copy
import glob
import json
import os
import random
# from .visualize import vis_points_3d
from typing import Tuple
import h5py
import numpy as np
import pandas as pd
import torch
from torch.utils import data
from .load_obj import loadOBJ
from .sampling import fartherst_point_sampling
class ShapeNetDataset... | 11,983 | 31.042781 | 87 | py |
point_cloud_anomaly_detection | point_cloud_anomaly_detection-main/libs/foldingnet.py | import itertools
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .visualize import vis_points_3d
def knn(x: torch.tensor, k: int) -> int:
batch_size = x.size(0)
num_points = x.size(2)
inner = -2 * torch.matmul(x.transpose(2, 1), x)
xx = torch.sum(x ** 2, d... | 13,746 | 31.88756 | 88 | py |
LGG | LGG-main/eeg_dataset.py | # tested 8/9/2020
import os.path as osp
import scipy.io as sio
import numpy as np
import torch
import h5py
from torch.utils.data import Dataset
class eegDataset(Dataset):
# x_tensor: (sample, channel, datapoint(feature)) type = torch.tensor
# y_tensor: (sample,) type = torch.tensor
def __init__(self, x_... | 570 | 20.961538 | 73 | py |
LGG | LGG-main/utils.py | import os
import time
import h5py
import numpy as np
import pprint
import random
from networks import *
from eeg_dataset import *
from torch.utils.data import DataLoader
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score
def set_gpu(x):
torch.set_num_threads(1)
os.environ["CUDA_DEVICE_ORDE... | 4,036 | 25.913333 | 179 | py |
LGG | LGG-main/train_model.py |
from utils import *
import torch.nn as nn
CUDA = torch.cuda.is_available()
def train_one_epoch(data_loader, net, loss_fn, optimizer):
net.train()
tl = Averager()
pred_train = []
act_train = []
for i, (x_batch, y_batch) in enumerate(data_loader):
if CUDA:
x_batch, y_batch = x_... | 7,897 | 33.33913 | 104 | py |
LGG | LGG-main/networks.py | # This is the networks script
import torch
import math
import torch.nn as nn
import torch.nn.functional as F
from layers import GraphConvolution
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
class PowerLayer(nn.Module):
'''
The power layer: calculates the log-transformed power of the data
'''
... | 6,817 | 37.738636 | 111 | py |
LGG | LGG-main/layers.py | import math
import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
class GraphConvolution(Module):
"""
simple GCN layer
"""
def __init__(self, in_features, out_features, bias=True):
super(GraphConvolution, self).__init_... | 1,097 | 29.5 | 89 | py |
LGG | LGG-main/cross_validation.py | import numpy as np
import datetime
import os
import csv
import h5py
import copy
import os.path as osp
from train_model import *
from utils import Averager, ensure_path
from sklearn.model_selection import KFold
import pickle
ROOT = os.getcwd()
class CrossValidation:
def __init__(self, args):
self.args = a... | 12,152 | 40.477816 | 115 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/pygad/kerasga/kerasga.py | import copy
import numpy
import tensorflow.keras
def model_weights_as_vector(model):
"""
Reshapes the Keras model weight as a vector.
Parameters
----------
model : TYPE
The Keras model.
Returns
-------
TYPE
The weights as a 1D vector.
"""
weights_vector = []
... | 4,821 | 30.723684 | 183 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/pygad/kerasga/__init__.py | from .kerasga import *
__version__ = "1.3.0"
| 46 | 10.75 | 22 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/pygad/torchga/__init__.py | from .torchga import *
__version__ = "1.3.0"
| 46 | 10.75 | 22 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/pygad/torchga/torchga.py | import copy
import numpy
import torch
def model_weights_as_vector(model):
weights_vector = []
for curr_weights in model.state_dict().values():
# Calling detach() to remove the computational graph from the layer.
# cpu() is called for making shore the data is moved from GPU to cpu
# num... | 3,272 | 34.967033 | 185 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/examples/KerasGA/cancer_dataset_generator.py | import tensorflow as tf
import tensorflow.keras
import pygad.kerasga
import pygad
def fitness_func(ga_instanse, solution, sol_idx):
global train_generator, data_outputs, keras_ga, model
predictions = pygad.kerasga.predict(model=model,
solution=solution,
... | 3,970 | 43.122222 | 152 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/examples/KerasGA/image_classification_Dense.py | import tensorflow.keras
import pygad.kerasga
import numpy
import pygad
def fitness_func(ga_instanse, solution, sol_idx):
global data_inputs, data_outputs, keras_ga, model
predictions = pygad.kerasga.predict(model=model,
solution=solution,
... | 3,558 | 41.879518 | 161 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/examples/KerasGA/cancer_dataset.py | import tensorflow as tf
import tensorflow.keras
import pygad.kerasga
import pygad
import numpy
def fitness_func(ga_instanse, solution, sol_idx):
global train_data, data_outputs, keras_ga, model
predictions = pygad.kerasga.predict(model=model,
solution=solution,
... | 3,869 | 39.3125 | 152 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/examples/KerasGA/image_classification_CNN.py | import tensorflow.keras
import pygad.kerasga
import numpy
import pygad
def fitness_func(ga_instanse, solution, sol_idx):
global data_inputs, data_outputs, keras_ga, model
predictions = pygad.kerasga.predict(model=model,
solution=solution,
... | 4,121 | 44.296703 | 161 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/examples/KerasGA/XOR_classification.py | import tensorflow.keras
import pygad.kerasga
import numpy
import pygad
def fitness_func(ga_instanse, solution, sol_idx):
global data_inputs, data_outputs, keras_ga, model
predictions = pygad.kerasga.predict(model=model,
solution=solution,
... | 3,648 | 40.942529 | 161 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/examples/KerasGA/regression_example.py | import tensorflow.keras
import pygad.kerasga
import numpy
import pygad
def fitness_func(ga_instanse, solution, sol_idx):
global data_inputs, data_outputs, keras_ga, model
predictions = pygad.kerasga.predict(model=model,
solution=solution,
... | 3,226 | 39.3375 | 161 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/examples/TorchGA/image_classification_Dense.py | import torch
import pygad.torchga
import pygad
import numpy
def fitness_func(ga_instanse, solution, sol_idx):
global data_inputs, data_outputs, torch_ga, model, loss_function
predictions = pygad.torchga.predict(model=model,
solution=solution,
... | 3,666 | 44.271605 | 161 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/examples/TorchGA/image_classification_CNN.py | import torch
import pygad.torchga
import pygad
import numpy
def fitness_func(ga_instanse, solution, sol_idx):
global data_inputs, data_outputs, torch_ga, model, loss_function
predictions = pygad.torchga.predict(model=model,
solution=solution,
... | 4,135 | 42.536842 | 161 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/examples/TorchGA/XOR_classification.py | import torch
import pygad.torchga
import pygad
def fitness_func(ga_instanse, solution, sol_idx):
global data_inputs, data_outputs, torch_ga, model, loss_function
predictions = pygad.torchga.predict(model=model,
solution=solution,
... | 3,605 | 40.448276 | 161 | py |
GeneticAlgorithmPython | GeneticAlgorithmPython-master/examples/TorchGA/regression_example.py | import torch
import pygad.torchga
import pygad
def fitness_func(ga_instanse, solution, sol_idx):
global data_inputs, data_outputs, torch_ga, model, loss_function
predictions = pygad.torchga.predict(model=model,
solution=solution,
... | 3,102 | 39.298701 | 161 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/random_net_distillation.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 8,567 | 39.8 | 80 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/random_net_distillation_test.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 3,948 | 35.564815 | 78 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/agent_config_test.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 3,287 | 37.232558 | 80 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/problem_configuration.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 4,459 | 32.283582 | 78 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/agent_config.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 7,676 | 33.895455 | 80 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/gin_external_configurables.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 1,437 | 34.95 | 74 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/trainer_test.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 5,860 | 35.179012 | 76 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/env.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 11,070 | 29.498623 | 80 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/trainer.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 7,818 | 36.591346 | 80 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/regalloc_priority/config.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 1,834 | 32.981481 | 74 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/distributed/ppo_eval_lib.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 7,384 | 37.06701 | 87 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/inlining/config.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 3,603 | 34.333333 | 79 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/regalloc/regalloc_network.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 7,327 | 40.168539 | 80 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/regalloc/config.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 5,417 | 35.608108 | 80 | py |
ml-compiler-opt | ml-compiler-opt-main/compiler_opt/rl/regalloc/regalloc_network_test.py | # coding=utf-8
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | 2,470 | 34.3 | 78 | py |
IndicXlit | IndicXlit-master/app/ai4bharat/transliteration/rnn/core.py | import os
import torch
import numpy as np
import pandas as pd
import json
from .network import Encoder, Decoder, Seq2Seq
##===================== Glyph handlers =======================================
class GlyphStrawboss():
def __init__(self, glyphs = 'en'):
""" list of letters in a language in unicode
... | 9,182 | 32.637363 | 104 | py |
IndicXlit | IndicXlit-master/app/ai4bharat/transliteration/rnn/network.py | import torch
import torch.nn as nn
class Encoder(nn.Module):
def __init__(self, input_dim, embed_dim, hidden_dim ,
rnn_type = 'gru', layers = 1,
bidirectional =False,
dropout = 0, device = "cpu"):
super(Encoder, self).__init__()
... | 13,571 | 40.378049 | 152 | py |
IndicXlit | IndicXlit-master/app/ai4bharat/transliteration/transformer/custom_interactive.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Translate raw text with a trained model. Batches data on-the-fly.
"""
import ast
import fileinput
import logging... | 14,113 | 38.757746 | 150 | py |
IndicXlit | IndicXlit-master/inference/python/custom_interactive.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Translate raw text with a trained model. Batches data on-the-fly.
"""
import ast
import fileinput
import logging... | 13,455 | 38.116279 | 148 | py |
signSGD | signSGD-master/gradient_expts/cifarTrainer.py | from __future__ import division
import os
import random
import argparse, time
import logging
logging.basicConfig(level=logging.INFO)
import pickle
import mxnet as mx
from mxnet import gluon
from mxnet.gluon.model_zoo import vision as models
from mxnet import autograd as ag
from mxnet import nd
import numpy as np
im... | 8,018 | 34.017467 | 173 | py |
signSGD | signSGD-master/gradient_expts/getCifarData.py | #! /usr/bin/python
import os
from mxnet.test_utils import download
import zipfile
import mxnet as mx
# download cifar
def GetCifar10():
if not os.path.isdir("data"):
os.makedirs('data')
if (not os.path.exists('data/cifar/train.rec')) or \
(not os.path.exists('data/cifar/test.rec')) or \
... | 649 | 29.952381 | 79 | py |
signSGD | signSGD-master/gradient_expts/imagenetTrainer.py | import argparse
import os
import time
import pickle
import logging
logging.basicConfig(level=logging.INFO)
import mxnet as mx
from mxnet import gluon
from mxnet import autograd as ag
from mxnet.gluon.model_zoo import vision
import numpy as np
import gradient_utils
def test(ctx, val_data, net):
metric_top1 = mx.m... | 4,704 | 36.047244 | 115 | py |
signSGD | signSGD-master/gradient_expts/gradient_utils.py | from mxnet import gluon
from mxnet import autograd as ag
import logging
logging.basicConfig(level=logging.INFO)
import numpy as np
import random
def welfordGradient(ctx, train_data, net):
# ctx is training context, i.e. list of CPUs/GPUs
# train_data is the training data
# net is the network including a ... | 2,984 | 36.78481 | 139 | py |
signSGD | signSGD-master/imagenet/train_resnet.py | import argparse
import os
import time
import pickle
import logging
logging.basicConfig(level=logging.INFO)
import mxnet as mx
from mxnet import gluon
from mxnet import autograd as ag
from mxnet.gluon.model_zoo import vision
import numpy as np
import random
def test(ctx, val_data, net):
metric_top1 = mx.metric.Ac... | 8,470 | 40.321951 | 134 | py |
signSGD | signSGD-master/cifar/networkTrainer.py | from __future__ import division
import os
import random
import argparse, time
import logging
logging.basicConfig(level=logging.INFO)
import pickle
import mxnet as mx
from mxnet import gluon
from mxnet.gluon.model_zoo import vision as models
from mxnet import autograd as ag
from mxnet import nd
import numpy as np
cl... | 7,056 | 34.641414 | 173 | py |
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