repo_name stringlengths 6 130 | hexsha list | file_path list | code list | apis list |
|---|---|---|---|---|
alle-pawols/allennlp | [
"7d4a67263d7a210aca22d4f2b03e8568d3c34a48",
"7d4a67263d7a210aca22d4f2b03e8568d3c34a48"
] | [
"allennlp/training/trainer.py",
"allennlp/fairness/bias_direction.py"
] | [
"import logging\nimport os\nfrom dataclasses import dataclass\nfrom typing import Any, Dict, Optional, Union\n\nimport torch.optim.lr_scheduler\n\nfrom allennlp.common import Registrable\nfrom allennlp.common.checks import ConfigurationError, check_for_gpu\nfrom allennlp.common.util import int_to_device\n\nlogger =... | [
[
"torch.cuda.device_count"
],
[
"numpy.concatenate",
"torch.pca_lowrank",
"sklearn.svm.SVC",
"torch.linalg.norm",
"torch.mean",
"torch.Tensor",
"torch.set_grad_enabled",
"numpy.vstack"
]
] |
sugartom/OpenSeq2Seq | [
"2866f67deb15d09798b075c665c4f6d8935708e4"
] | [
"open_seq2seq/models/speech2text_ds2_test.py"
] | [
"# Copyright (c) 2017 NVIDIA Corporation\nfrom __future__ import absolute_import, division, print_function\nfrom __future__ import unicode_literals\n\nimport tensorflow as tf\n\nfrom OpenSeq2Seq.open_seq2seq.test_utils.test_speech_configs.ds2_test_config import \\\n base_params, train_params, eval_params, base_m... | [
[
"tensorflow.test.main"
]
] |
carankt/HiFi-GAN | [
"4b0d9dfce1bc823f72cd38f6d4c48350c96121aa"
] | [
"model/period_discriminator.py"
] | [
"from torch import nn\nimport torch.nn.functional as F\n\n\nclass PeriodDiscriminator(nn.Module):\n\n def __init__(self, period):\n super(PeriodDiscriminator, self).__init__()\n layer = []\n self.period = period\n inp = 1\n for l in range(4):\n out = int(2 ** (5 + l ... | [
[
"torch.nn.Sequential",
"torch.nn.functional.pad",
"torch.nn.LeakyReLU",
"torch.nn.Conv2d"
]
] |
rodluger/starry | [
"da7fee48c5ef94278f0047be0579e2f13492cdd5"
] | [
"tests/greedy/test_reflected_normalization.py"
] | [
"\"\"\"\nStarry was originally designed for maps in thermal light.\nMaps in reflected light are quirky in how things are normalized,\nso we need to make sure albedos, intensities, and fluxes are\nall self-consistent in the code. There are some fudges we had\nto make under the hood to ensure all of these tests pass.... | [
[
"numpy.array",
"numpy.zeros",
"numpy.random.seed",
"numpy.allclose",
"numpy.linspace"
]
] |
peleiden/pelutils | [
"9860734c0e06481aa58a9f767a4cfb5129cb48ec"
] | [
"pelutils/ds/__init__.py"
] | [
"from __future__ import annotations\nimport os\nimport ctypes\nimport functools\nimport platform\nfrom typing import Callable, Generator, Iterable, Type\n\nimport numpy as np\n\n# Path to directory where package files are located\n_base_path = os.path.abspath(os.path.dirname(__file__))\n\n_import_error = ModuleNotF... | [
[
"torch.cat",
"numpy.ascontiguousarray",
"torch.no_grad",
"numpy.prod",
"numpy.unique"
]
] |
ciguaran/pyro | [
"11a96cde05756def826c232d76f9cff66f6e6d4f",
"11a96cde05756def826c232d76f9cff66f6e6d4f"
] | [
"examples/contrib/forecast/bart.py",
"pyro/contrib/examples/finance.py"
] | [
"# Copyright Contributors to the Pyro project.\n# SPDX-License-Identifier: Apache-2.0\n\nimport argparse\nimport logging\n\nimport numpy as np\nimport torch\n\nimport pyro\nimport pyro.distributions as dist\nfrom pyro.contrib.examples.bart import load_bart_od\nfrom pyro.contrib.forecast import ForecastingModel, bac... | [
[
"torch.zeros",
"torch.stack",
"numpy.mean",
"torch.ones",
"numpy.std",
"torch.full",
"torch.eye"
],
[
"pandas.read_csv"
]
] |
bitdotioinc/dagster | [
"4fe395a37b206b1a48b956fa5dd72bf698104cca"
] | [
"examples/legacy_examples/dagster_examples_tests/event_pipeline_demo_tests/test_pipelines.py"
] | [
"import pandas as pd\nimport pytest\nfrom dagster_examples.event_pipeline_demo.pipelines import event_ingest_pipeline\n\nfrom dagster import execute_pipeline\nfrom dagster.seven import mock\nfrom dagster.utils import load_yaml_from_globs, script_relative_path\n\nfrom .conftest import events_jar, spark_home # pylin... | [
[
"pandas.DataFrame"
]
] |
AshleySpindler/AstroSense | [
"8a046427c158fd477dcf209068b98838c202f8ec"
] | [
"test_cae_galzoo2.py"
] | [
"from Autoencoder import Models_Cond as Models\nimport Data.GalZoo2_TFIngest_cond as Data_In\n\nimport tensorflow.keras.backend as K\nimport tensorflow as tf\nfrom tensorflow.keras.utils import multi_gpu_model\nfrom tensorflow.keras import optimizers\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\ntf.kera... | [
[
"numpy.random.permutation",
"tensorflow.Session",
"matplotlib.pyplot.subplots",
"tensorflow.keras.backend.clear_session",
"matplotlib.pyplot.show"
]
] |
Jvoytek9/Owlhacks | [
"406b59074397794f48db173e9027d251ce7a070e"
] | [
"Scripts/backup.py"
] | [
"import os\n\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport pandas as pd\nimport plotly.graph_objs as go\n\napp = dash.Dash(__name__)\nserver = app.server\napp.config.suppress_callback_exceptions = True\napp.title = \"Surfactants\"\n\nif 'DYNO' in os.environ:\n app_... | [
[
"pandas.read_csv"
]
] |
cclauss/LARK-1 | [
"3aeb517a76c219ee5ee5d68c07d07108a8931d57"
] | [
"ERNIE/batching.py"
] | [
"# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless ... | [
[
"numpy.array",
"numpy.random.randint",
"numpy.random.rand"
]
] |
lennart-damen/tindar | [
"089b6ae2c99f4126df2913f0ee75b6fef4a5dd0e"
] | [
"code_evolution/tindar_v3.py"
] | [
"# Tindar class version 3:\n# Add __repr__ methods\n# allow Tindar to be initialised with TindarGenerator objects\n# so that it can copy the connectedness and p parameters\n# change main script to test many instances\n# Fix minor bugs\n\nfrom pulp import *\nimport numpy as np\nfrom pathlib import Path\nfrom timer i... | [
[
"numpy.array",
"numpy.random.binomial",
"numpy.zeros"
]
] |
HarshCasper/napari | [
"3ed7d2db678f4012753f53b2d40cff9d34a8011f"
] | [
"napari/_vispy/experimental/_tests/test_vispy_tiled_image.py"
] | [
"import os\nimport sys\n\nimport numpy as np\nimport pytest\n\nfrom napari._vispy.experimental.vispy_tiled_image_layer import (\n VispyTiledImageLayer,\n)\n\nskip_on_win_ci = pytest.mark.skipif(\n sys.platform.startswith('win') and os.getenv('CI', '0') != '0',\n reason='Screenshot tests are not supported o... | [
[
"numpy.testing.assert_allclose",
"numpy.array",
"numpy.ones",
"numpy.dtype"
]
] |
bpbpublications/Data-Science-for-Business-Professionals | [
"e0795321f4f393b4ae2c5959046e5508a510f2a6"
] | [
"Code_7_1_ML_Model.py"
] | [
"from sklearn.model_selection import train_test_split\r\nX_train_num, X_val_num, y_train_num, y_val_num = train_test_split(X_n, y_n, test_size=0.3, random_state=1)\r\n\r\n\r\n# Bernoulli\r\nfrom sklearn.naive_bayes import BernoulliNB\r\nclf = BernoulliNB()\r\nclf.fit(X_train_num, y_train_num)\r\n\r\nfrom sklearn im... | [
[
"sklearn.metrics.confusion_matrix",
"sklearn.metrics.accuracy_score",
"sklearn.naive_bayes.MultinomialNB",
"sklearn.naive_bayes.BernoulliNB",
"sklearn.feature_extraction.text.CountVectorizer",
"sklearn.model_selection.train_test_split"
]
] |
yonghyuc/DarkKnight_client | [
"2eade9e5cfbb5a20d971fea354c4e9b8bb789118"
] | [
"lib/roi_data_layer/roidb.py"
] | [
"# --------------------------------------------------------\n# Fast R-CNN\n# Copyright (c) 2015 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ross Girshick\n# --------------------------------------------------------\n\"\"\"Transform a roidb into a trainable roidb by adding a bu... | [
[
"numpy.where"
]
] |
limit-scu/2018-AAAI-SC2Net | [
"dd113627dc8a5e12fd8bd9c7c2333fc9b7dc4b60"
] | [
"code/sparse_lstm.py"
] | [
"# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import\nimport numpy as np\nfrom keras import backend as K\nfrom keras import activations, initializations, regularizers\nfrom keras.engine import Layer, InputSpec\nfrom keras.layers import Recurrent, time_distributed_dense\nimport theano.tensor as T\n\ncl... | [
[
"numpy.zeros"
]
] |
hartb/vision | [
"f2f085bf099c4c31bc6f09c21844b2d57dabcb87",
"f2f085bf099c4c31bc6f09c21844b2d57dabcb87"
] | [
"references/similarity/model.py",
"torchvision/models/resnet.py"
] | [
"import torch\nimport torch.nn as nn\nimport torchvision.models as models\n\n\nclass EmbeddingNet(nn.Module):\n def __init__(self, backbone=None):\n super(EmbeddingNet, self).__init__()\n if backbone is None:\n backbone = models.resnet50(num_classes=128)\n\n self.backbone = backbo... | [
[
"torch.nn.functional.normalize"
],
[
"torch.nn.Linear",
"torch.flatten",
"torch.nn.MaxPool2d",
"torch.nn.Sequential",
"torch.nn.init.constant_",
"torch.nn.init.kaiming_normal_",
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.nn.AdaptiveAvgPool2d"
]
] |
MITeaps/pyinsar | [
"4d22e3ef90ef842d6b390074a8b5deedc7658a2b"
] | [
"pyinsar/processing/corrections/troposphere.py"
] | [
"from scipy.interpolate import CubicSpline, UnivariateSpline\nimport pandas as pd\n\n# These functions are all under development\n\ndef vapor_pressure(T):\n '''\n Under development\n '''\n return 6.1037*np.exp(17.641 * (T-273.15) / (T - 29.88))\n\ndef N(P, T, RH, k1=77.6, k2=23.3, k3=3.75E5):\n '''\n... | [
[
"scipy.interpolate.UnivariateSpline",
"scipy.interpolate.CubicSpline",
"pandas.isnull"
]
] |
blitzkrieg0000/BioFace | [
"ff45e3608ad08ab41ef93f4e962c85dafb00709b"
] | [
"PROJECT_0000/faceLib/faceDetectFRLib.py"
] | [
"import cv2\r\nimport numpy as np\r\nimport face_recognition\r\n\r\nfrom .IDetect import IDetect\r\nfrom .core import Core\r\nclass FaceDetectFRLib(Core, IDetect):\r\n \r\n def __init__(self, source, method):\r\n super().__init__(source = source)\r\n self.face = []\r\n self.bboxes = []\r\... | [
[
"numpy.array",
"numpy.zeros"
]
] |
Cyianor/abc2018 | [
"11da8b44b37ea524a5cc04a98b0365331e40b9dc"
] | [
"advanced/gillespie.py"
] | [
"\"\"\"Gillespie algorithm for Michaelis-Menten enzyme reaction.\n\n Copyright (c) 2018, Felix Held\n\"\"\"\nimport numpy.random as rnd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\ndef reactions(mu, states):\n \"\"\"Executes Michaelis-Menten chemical reactions.\n\n :Ar... | [
[
"numpy.array",
"numpy.random.rand",
"numpy.zeros",
"numpy.log",
"matplotlib.pyplot.savefig",
"numpy.sum",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend"
]
] |
nav13n/multimodal-learning | [
"283d09989ab5e547acc881547276e03e00c0ff39"
] | [
"src/datamodules/datasets/hateful_memes_dataset.py"
] | [
"import json\nimport logging\nfrom pathlib import Path\nimport random\nimport tarfile\nimport tempfile\nimport warnings\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport pandas_path\nfrom PIL import Image\nimport torch\nimport fasttext\n\nfrom torch.nn.utils.rnn import pad_sequence... | [
[
"torch.is_tensor",
"torch.nn.utils.rnn.pad_sequence",
"pandas.read_json",
"torch.LongTensor",
"torch.Tensor"
]
] |
robustEval/end-to-end-SLU | [
"930072e94d942fa8017738b191ed2b4de97d7c05"
] | [
"models.py"
] | [
"import torch\nimport numpy as np\nimport sys\nimport os\nimport math\nimport io\n\nnp.random.seed(0)\n\ndef flip(x, dim):\n\txsize = x.size()\n\tdim = x.dim() + dim if dim < 0 else dim\n\tx = x.contiguous()\n\tx = x.view(-1, *xsize[dim:])\n\tx = x.view(x.size(0), x.size(1), -1)[:, getattr(torch.arange(x.size(1)-1,... | [
[
"torch.nn.Linear",
"torch.cat",
"torch.stack",
"torch.nn.GRU",
"torch.nn.ModuleList",
"torch.nn.LeakyReLU",
"torch.ones",
"torch.nn.functional.cross_entropy",
"torch.cuda.is_available",
"torch.load",
"torch.nn.MaxPool1d",
"torch.transpose",
"numpy.random.normal"... |
zzz1515151/TianChi-JingWei-Competation-Round1 | [
"82b26a4cd0e7a6e3c0264c9dbd0100326f9727ad"
] | [
"code/dataloader/transform.py"
] | [
"import torch \r\nimport numpy as np \r\nfrom PIL import Image \r\n\r\nclass RandomFlip(object):\r\n def __call__(self, image, label):\r\n if np.random.random() < 0.5:\r\n image = image.transpose(Image.FLIP_LEFT_RIGHT)\r\n label = label.transpose(Image.FLIP_LEFT_RIGHT)\r\n if ... | [
[
"numpy.random.random",
"numpy.random.uniform"
]
] |
limingdata/DeepCTR | [
"024da0a763ecd623bbccc1976b34b1b17dce4b88"
] | [
"tests/models/WDL_test.py"
] | [
"import numpy as np\nimport pytest\n\nfrom deepctr.models import WDL\nfrom ..utils import check_model,SingleFeat\n\n\n@pytest.mark.parametrize(\n 'sparse_feature_num,wide_feature_num',\n [(1, 0), (1, 2), (2, 0), (2, 1)\n ]\n)\ndef test_WDL(sparse_feature_num, wide_feature_num):\n model_name = \"WDL\"\n... | [
[
"numpy.random.random",
"numpy.random.randint"
]
] |
jiuney/XAI606-EEGNet | [
"45ff28630ed1b09d0853f2cfb148a5dd2693e5ab"
] | [
"trainers/trainer_maker.py"
] | [
"import importlib\nfrom collections import defaultdict\nimport torch.nn as nn\nimport torch.optim as optim\n\n\nclass TrainerMaker:\n def __init__(self, args, model, data):\n print(\"[Make Trainer]\")\n if args.mode == 'train':\n criterion = self.__set_criterion(args.criterion)\n ... | [
[
"torch.optim.lr_scheduler.StepLR",
"torch.nn.MSELoss",
"torch.optim.lr_scheduler.CosineAnnealingLR",
"torch.optim.lr_scheduler.ExponentialLR",
"torch.optim.lr_scheduler.MultiStepLR",
"torch.optim.lr_scheduler.ReduceLROnPlateau",
"torch.nn.CrossEntropyLoss"
]
] |
pchandrasekaran1595/onnx | [
"10da6f2e919c8515877e227a41cd44e86ae0bb2d",
"10da6f2e919c8515877e227a41cd44e86ae0bb2d"
] | [
"onnx/backend/test/case/node/dequantizelinear.py",
"onnx/backend/test/case/node/softsign.py"
] | [
"# SPDX-License-Identifier: Apache-2.0\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport numpy as np # type: ignore\nimport onnx\nfrom ..base import Base\nfrom . import expect\n\n\nclass DequantizeLin... | [
[
"numpy.uint8",
"numpy.array",
"numpy.float32"
],
[
"numpy.array",
"numpy.random.randn",
"numpy.abs"
]
] |
maximzubkov/code-transformer | [
"52600ab17d05a238f35c39a78b22c5c706fbb13c",
"52600ab17d05a238f35c39a78b22c5c706fbb13c"
] | [
"code_transformer/preprocessing/dataset/lm.py",
"code_transformer/experiments/experiment.py"
] | [
"from typing import List\n\nimport torch\n\nfrom code_transformer.modeling.constants import SEP_TOKEN, CLS_TOKEN, MAX_NUM_TOKENS\nfrom code_transformer.modeling.data_utils import sample_targets, permutation_attention_mask\nfrom code_transformer.preprocessing.datamanager.base import CTBatch\nfrom code_transformer.pr... | [
[
"torch.where"
],
[
"torch.optim.lr_scheduler.OneCycleLR",
"torch.no_grad",
"torch.optim.lr_scheduler.MultiStepLR",
"torch.manual_seed",
"torch.utils.data.DataLoader"
]
] |
playing-code/SEED-Encoder-1 | [
"c038060a8cfeb9226c745f4cb725da93997bb827",
"c038060a8cfeb9226c745f4cb725da93997bb827"
] | [
"model/models.py",
"drivers/run_warmup_norm.py"
] | [
"import sys\nsys.path += ['../']\nimport torch\nfrom torch import nn\nfrom transformers import (\n RobertaConfig,\n RobertaModel,\n RobertaForSequenceClassification,\n RobertaTokenizer,\n BertModel,\n BertTokenizer,\n BertConfig,\n BertForSequenceClassification, \n)\nimport torch.nn.function... | [
[
"torch.nn.Linear",
"torch.distributed.is_available",
"torch.nn.LayerNorm",
"torch.nn.Module.__init__",
"torch.nn.functional.log_softmax",
"torch.distributed.is_initialized",
"torch.load",
"torch.distributed.get_rank"
],
[
"torch.device",
"torch.distributed.get_world_siz... |
MarkusSagen/pytorch-adapt | [
"947b9f1b748d2078cecbf4a00c34f73108d9ecde"
] | [
"tests/datasets/utils.py"
] | [
"import os\nfrom collections import defaultdict\n\nimport torch\nimport tqdm\nfrom torchvision import transforms as torch_transforms\n\nskip_reason = \"RUN_DATASET_TESTS is False\"\n\n\ndef simple_transform():\n return torch_transforms.Compose(\n [\n torch_transforms.Resize((2, 2)),\n ... | [
[
"torch.utils.data.ConcatDataset",
"torch.utils.data.DataLoader"
]
] |
ymlwww/rlproject | [
"b4fe26090776b7cde80de635254ce0bf8f4d17cf"
] | [
"vsum_tools.py"
] | [
"import numpy as np\nfrom knapsack import knapsack_dp\nimport math\n\ndef generate_summary(ypred, cps, n_frames, nfps, positions, proportion=0.15, method='knapsack'):\n \"\"\"Generate keyshot-based video summary i.e. a binary vector.\n Args:\n ---------------------------------------------\n - ypred: pre... | [
[
"numpy.concatenate",
"numpy.max",
"numpy.delete",
"numpy.zeros",
"numpy.ones",
"numpy.mean",
"numpy.argmax",
"numpy.argsort"
]
] |
wzh99/GSL | [
"49f8e071ae53d393ba6494ab0aa762787bc49f04"
] | [
"gsl/pat.py"
] | [
"from typing import List, Dict, Callable, Union, Any, Optional, Generic, TypeVar, Set\n\nimport numpy as np\n\nfrom . import attr, spec\nfrom .attr import Attr, Symbol, AttrLike\n\n\nclass Pattern:\n \"\"\"\n Base class for all pattern graph nodes. This class cannot be directly instantiated.\n \"\"\"\n\n ... | [
[
"numpy.array"
]
] |
shuto-keio/3DSSD | [
"fb900f7a1dcd366e326d044fcd8dc2c6ddc697fb"
] | [
"lib/builder/mixup_sampler.py"
] | [
"import tensorflow as tf\nimport sys, os\nimport numpy as np\nimport cv2\nimport itertools\n\nfrom core.config import cfg\n\nclass MixupSampler:\n def __init__(self, shuffle=True):\n \"\"\"\n pc_list: train / val / trainval \n cls_list: ['Car', 'Pedestrian', 'Cyclist']\n \"\"\"\n ... | [
[
"numpy.arange",
"numpy.load",
"numpy.random.shuffle"
]
] |
KelvinKan/CP-Flow | [
"d01303cb4ebeb5a0bbfca638ffaf5b7a8ec22fb1"
] | [
"lib/sylvester/utils/log_likelihood.py"
] | [
"from __future__ import print_function\nimport numpy as np\nfrom scipy.special import logsumexp\nfrom lib.sylvester.optimization.loss import calculate_loss_array\n\n\ndef calculate_likelihood(X, model, args, S=5000, MB=500):\n\n # set auxiliary variables for number of training and test sets\n N_test = X.size(... | [
[
"numpy.array",
"scipy.special.logsumexp",
"numpy.reshape",
"numpy.asarray",
"numpy.log",
"numpy.mean",
"numpy.prod"
]
] |
echaussidon/desitarget | [
"1206380dac5155b9e7bf238c7cb187bc797d78a3"
] | [
"py/desitarget/sv1/sv1_cuts.py"
] | [
"\"\"\"\ndesitarget.sv1.sv1_cuts\n=======================\n\nTarget Selection for DESI Survey Validation derived from `the SV wiki`_.\n\nA collection of helpful (static) methods to check whether an object's\nflux passes a given selection criterion (*e.g.* LRG, ELG or QSO).\n\n.. _`the Gaia data model`: https://gea.... | [
[
"numpy.ones_like",
"numpy.copy",
"numpy.tile",
"numpy.where",
"numpy.radians",
"numpy.divide",
"numpy.concatenate",
"numpy.zeros_like",
"numpy.logical_and",
"numpy.tanh",
"numpy.arange",
"numpy.isfinite",
"numpy.sqrt",
"numpy.log10",
"numpy.isnan",
"... |
LUMO666/Highway | [
"05e1ad318bd14d405bd78d612e5706f7db2b3266"
] | [
"onpolicy/algorithms/r_mappo_single/algorithm/r_actor_critic.py"
] | [
"import math\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom onpolicy.algorithms.utils.cnn import CNNBase\nfrom onpolicy.algorithms.utils.mlp import MLPBase, MLPLayer\nfrom onpolicy.algorithms.utils.rnn import RNNLayer\nfrom onpolicy.algorithms.utils.act import AC... | [
[
"torch.nn.Linear",
"torch.device",
"torch.nn.init.constant_"
]
] |
martindevora/SHERLOCK | [
"5e7492552cbce29e960684a44fd6ad875c8cf60e"
] | [
"experimental/inject.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function, division, absolute_import\n\n#::: modules\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport os, sys\nimport ellc\nfrom transitleastsquares import transitleastsquares\nfrom tr... | [
[
"numpy.random.rand",
"numpy.float",
"numpy.random.seed",
"numpy.cbrt",
"numpy.arange",
"numpy.abs",
"numpy.around"
]
] |
xueyuuu/federated | [
"ad617401324c133eb838e4e0af7442a4dfa71d6e"
] | [
"tensorflow_federated/python/research/baseline_fedavg/simple_fedavg_test.py"
] | [
"# Lint as: python3\n# Copyright 2019, The TensorFlow Federated Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# ... | [
[
"tensorflow.keras.optimizers.SGD",
"numpy.ones_like",
"numpy.random.rand",
"tensorflow.matmul",
"tensorflow.reshape",
"numpy.mean",
"tensorflow.assign_add",
"tensorflow.one_hot",
"tensorflow.train.GradientDescentOptimizer",
"tensorflow.compat.v1.enable_v2_behavior",
"te... |
faz1993/InnerEye-DeepLearning | [
"ccb53d01ad0f1c20336588c0066059b8de5266fd"
] | [
"Tests/ML/histopathology/models/test_deepmil.py"
] | [
"# ------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.\n# -----------------------------------------------------------... | [
[
"torch.rand",
"torch.round",
"torch.stack",
"torch.argmax",
"torch.randn",
"torch.randint",
"torch.nn.BCEWithLogitsLoss",
"torch.allclose",
"torch.Tensor",
"torch.nn.CrossEntropyLoss"
]
] |
cityofasheville/abi-vendro-processing | [
"72ed24216ee4772d72abd26b956d7f97ed23bdd3"
] | [
"arpa-rfp-evaluation/check_stats.py"
] | [
"from googleapiclient.discovery import build\nimport json\nimport sys\nimport time\nfrom csv import reader\nfrom google.oauth2 import service_account\nimport numpy as np\nimport statistics as stat\nfrom os.path import exists\n\n\n\nSERVICE_ACCOUNT_FILE = None\nSCOPES = ['https://www.googleapis.com/auth/spreadsheets... | [
[
"numpy.array"
]
] |
RuidongDavidLin/YOLOV4-tiny-Pytorch | [
"f2bb941ff894e12551bf285eb09fd42db2fb3dee"
] | [
"detect.py"
] | [
"import time\nimport cv2\nimport numpy as np\nfrom PIL import Image\nfrom yolov4tiny.yolo import YOLO\n\nclass yolov4:\n\tdef __init__(self):\n\t\tself.yolo=YOLO()\n\n\tdef getLabels(self,frame):\n\t\tframe = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n\t\tframe = Image.fromarray(np.uint8(frame))\n\t\tdata = self.yolo.... | [
[
"numpy.uint8"
]
] |
eddyleelin/MONAI | [
"8e56191d344692fdfa1b9a52285b2514131061e6"
] | [
"monai/handlers/classification_saver.py"
] | [
"# Copyright 2020 - 2021 MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agre... | [
[
"torch.cat"
]
] |
maayane/SLAB-Diffusion | [
"0b6908b59c1c4ab8796133dcb59ee81b4043f8a2"
] | [
"SLAB_Diffusion/solver_3D.py"
] | [
"\"\"\"*******************************************************\ncontains solvers of the diffusion equation in 3D, with BB boundary conditions\n*******************************************************\"\"\"\n#print __doc__\n\nimport numpy as np\nimport pylab\n\n\ndef solver_3D_norm(Nx,Ny,Nz,Lx,Ly,Initial_xyz,F,v,a,to... | [
[
"numpy.sum",
"numpy.linspace",
"numpy.shape",
"numpy.zeros"
]
] |
abdullahahsann/VideoEnhancementForSurviellance | [
"ed1a7625e2ac7cb51a409be1973ad7733e715222"
] | [
"LLE_MODEL/Evaluation/eval.py"
] | [
"import numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport tensorflow as tf \nimport keras\nimport keras.backend as K\nfrom tensorflow.keras.layers import Conv2D, AveragePooling2D, Activation, Concatenate\nfrom tensorflow.keras import Sequential, Input, Model\nfrom model import lle_model\nf... | [
[
"numpy.array",
"numpy.asarray",
"tensorflow.cast",
"matplotlib.pyplot.show",
"tensorflow.pow",
"numpy.expand_dims",
"matplotlib.pyplot.imshow"
]
] |
jarvisqi/machine_learning | [
"53c5b4087dbfc30e47451fd3f5261ba44f523f39",
"53c5b4087dbfc30e47451fd3f5261ba44f523f39"
] | [
"ml_xgboost/titanic.py",
"ml_sklearn/k-nn.py"
] | [
"# -*- coding:utf-8 -*-\nimport math\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport xgboost as xgb\nfrom sklearn import cross_validation, metrics\nfrom sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\nfrom sklearn.linear_model import LogisticRegression\nfro... | [
[
"sklearn.preprocessing.LabelEncoder",
"sklearn.ensemble.RandomForestClassifier",
"pandas.DataFrame",
"sklearn.model_selection.GridSearchCV",
"numpy.mean",
"sklearn.metrics.accuracy_score",
"sklearn.linear_model.LogisticRegression",
"sklearn.model_selection.train_test_split",
"p... |
blurry-mood/Distilled-Models | [
"ac0e4573cbb9e80038d7eb25fd5e4486e4ce03ea"
] | [
"src/datamodules/imagenet_datamodule.py"
] | [
"import pytorch_lightning as pl\nimport torch\nfrom torch.utils.data import Dataset, DataLoader, random_split\nfrom sklearn.preprocessing import LabelEncoder\nfrom PIL import Image\nimport numpy as np\nimport pandas as pd\n\ndef encode_labels(labels):\n le = LabelEncoder()\n encoded = le.fit_transform... | [
[
"numpy.array",
"sklearn.preprocessing.LabelEncoder",
"torch.Generator",
"numpy.transpose",
"torch.utils.data.DataLoader",
"pandas.read_csv"
]
] |
mortarsynth/ML_task1 | [
"a929dbf84c4c07194ad6cc4f3536b6ddd83a2644"
] | [
"Analysis/analyze_weights.py"
] | [
"import numpy as np\n\n\nif __name__ == '__main__':\n weight_data = np.load('../TrainData/train_weights.npy')\n weights = weight_data[0, :, -1]\n weights = weights.T\n\n \n \n weights_mean = np.mean(weights, axis=1)\n weights_mean = np.array(weights_mean)[np.newaxis]\n weights_mean = weights... | [
[
"numpy.array",
"numpy.savetxt",
"numpy.load",
"numpy.mean",
"numpy.std",
"numpy.append"
]
] |
Ram81/language | [
"cb954c8bf3c9265855b23e768f70abb528ab60a7",
"cb954c8bf3c9265855b23e768f70abb528ab60a7"
] | [
"language/common/inputs/dataset_utils_test.py",
"language/question_answering/decatt_docreader/experiments/nq_export_scorer.py"
] | [
"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unl... | [
[
"tensorflow.data.Dataset.from_tensor_slices",
"tensorflow.Graph",
"tensorflow.Session",
"tensorflow.test.main",
"tensorflow.tables_initializer",
"tensorflow.data.Dataset.from_tensors"
],
[
"tensorflow.contrib.predictor.from_saved_model",
"tensorflow.gfile.Glob",
"tensorflow... |
Vasudeva-bit/sktime | [
"0d031862ac85d20e75434a1b4ab4117bdd544ffe",
"0d031862ac85d20e75434a1b4ab4117bdd544ffe"
] | [
"sktime/transformations/series/boxcox.py",
"sktime/_contrib/datasets/_single_problem_loaders.py"
] | [
"#!/usr/bin/env python3 -u\n# -*- coding: utf-8 -*-\n# copyright: sktime developers, BSD-3-Clause License (see LICENSE file).\n\"\"\"Implmenents Box-Cox and Log Transformations.\"\"\"\n\n__author__ = [\"mloning, aiwalter, fkiraly\"]\n__all__ = [\"BoxCoxTransformer\", \"LogTransformer\"]\n\nimport numpy as np\nfrom ... | [
[
"scipy.optimize.brent",
"numpy.empty",
"numpy.asarray",
"numpy.log",
"numpy.zeros",
"scipy.optimize.fminbound",
"numpy.exp",
"numpy.mean",
"scipy.stats.variation",
"scipy.stats.pearsonr",
"numpy.std",
"numpy.arange",
"scipy.stats.distributions.norm.ppf",
"nu... |
ulysse06/vtam | [
"3c743e62e93c80bb891ebda59d1ea3115766b687"
] | [
"vtam/CommandSortReads.py"
] | [
"import multiprocessing\nimport os\nimport sys\n\nimport pandas\nimport pathlib\nimport shlex\nimport shutil\nimport subprocess\n\n# Compatible with both pre- and post Biopython 1.78:\ntry:\n from Bio.Alphabet import generic_dna\nexcept ImportError:\n generic_dna = None\n\nfrom Bio.Seq import Seq\nfrom vtam.u... | [
[
"pandas.DataFrame",
"pandas.concat"
]
] |
flemingmartin/crawler-bot | [
"0f59250c1b72f24c0318224f27e8248f4305f22b"
] | [
"python_code/q_learning.py"
] | [
"import numpy as np\nimport time\nimport random\nfrom threading import Semaphore\n\nfrom python_code.robot import Robot\n\nclass QLearning():\n\t'''\n\t\tClase QLearning encargada de realizar el entrenamiento\n\t\tdel modelo, su inferencia y su inicialización\n\t'''\n\n\tdef __init__(self,app):\n\t\t'''\n\t\t\tCons... | [
[
"numpy.max",
"numpy.argmax",
"numpy.zeros"
]
] |
CancerModeling/Flows1D0D3D | [
"ca87bd11acd1f558ee64c379d051e41175a13b6a",
"ca87bd11acd1f558ee64c379d051e41175a13b6a"
] | [
"tools/visualization/write_cow_plots_comparison.py",
"tools/visualization/write_cow_plots.py"
] | [
"import os\nimport numpy as np\nimport json\nfrom matplotlib import pyplot as plt\nimport argparse\n\n\nparser = argparse.ArgumentParser(description='Plots the vessel data.')\nparser.add_argument('--filepaths', type=str, nargs='+', required=True)\nparser.add_argument('--vessels', type=int, nargs='+', help='A list o... | [
[
"numpy.array",
"numpy.sum",
"numpy.ones",
"matplotlib.pyplot.figure",
"numpy.loadtxt",
"matplotlib.pyplot.show",
"numpy.sqrt",
"matplotlib.pyplot.clf"
],
[
"numpy.array",
"numpy.sum",
"numpy.ones",
"matplotlib.pyplot.figure",
"numpy.loadtxt",
"matplotlib... |
mayscopeland/open_projections | [
"a0c3e292038ae7fa694b74acdf5b909b83538207"
] | [
"scrape_dt.py"
] | [
"import pandas as pd\nimport numpy as np\n\ndef main():\n \n get_all_leagues(True)\n get_all_leagues(False)\n\ndef get_all_leagues(is_batting):\n \n print(get_factors(\"ACL\", is_batting))\n print(get_factors(\"FCL\", is_batting))\n \n print(get_factors(\"LAW\", is_batting))\n print(get_f... | [
[
"numpy.where",
"pandas.read_html"
]
] |
huckgroup/formose-2021 | [
"f1c5e809e0cbbbc744a4fe636069cdfc83ad6091"
] | [
"SCRIPTS/DATA_PRESENT/SI_Residence_Time_Figure.py"
] | [
"'''\nA selection of data sets showing how the concentration of formaldehyde\naffects the reaction composition. Figure 2C.\n'''\nimport sys\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport matplotlib.cm as cm\nimport matplotlib.pyplot as plt\n\n# add the SCRIPTS directory to the system pat... | [
[
"numpy.where",
"pandas.read_csv",
"numpy.sum",
"matplotlib.pyplot.subplots"
]
] |
sairamreddy94/oopt-gnpy | [
"e9b287ceaccc7f701af976012701c1b30118aff6"
] | [
"tests/amplifier_test.py"
] | [
"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @Author: Jean-Luc Auge\n# @Date: 2018-02-02 14:06:55\n\nfrom gnpy.core.elements import Edfa\nimport numpy as np\nfrom json import load\nimport pytest\nfrom gnpy.core import network_from_json\nfrom gnpy.core.elements import Transceiver, Fiber, Edfa\nfrom gnpy.cor... | [
[
"numpy.sum",
"numpy.array",
"numpy.zeros"
]
] |
KleinYuan/tf-cnn | [
"492703035d9d37fbffcb5adf9f89bae5dc8c7a96"
] | [
"models/cnn_net.py"
] | [
"import tensorflow as tf\n\nfrom core.base_net import BaseNet\nfrom utils import blocks\n\n\nclass Net(BaseNet):\n\n def __init__(self, config, logger):\n super(Net, self).__init__(config, logger)\n self.dropout = config.DROPOUT\n self.num_features = config.NUM_FEATURES\n self.num_con... | [
[
"tensorflow.variable_scope",
"tensorflow.placeholder",
"tensorflow.nn.dropout",
"tensorflow.reshape"
]
] |
NahidEbrahimian/Self-Correction-Human-Parsing-on-Binary-Images | [
"eb924b0ceff71a515212972e64fad0de27b8afe9"
] | [
"modules/deeplab.py"
] | [
"import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as functional\r\n\r\nfrom models._util import try_index\r\nfrom .bn import ABN\r\n\r\n\r\nclass DeeplabV3(nn.Module):\r\n def __init__(self,\r\n in_channels,\r\n out_channels,\r\n hidden_channels=... | [
[
"torch.nn.functional.avg_pool2d",
"torch.nn.init.constant_",
"torch.nn.Conv2d",
"torch.nn.init.calculate_gain",
"torch.nn.functional.pad",
"torch.nn.init.xavier_normal_"
]
] |
mirwu/elasticdl | [
"a82c1189c57b87ba797221a10d492794a21bf459"
] | [
"elasticdl/python/worker/worker.py"
] | [
"import os\nimport time\nimport traceback\n\nimport numpy as np\nimport tensorflow as tf\nfrom google.protobuf import empty_pb2\n\nfrom elasticdl.proto import elasticdl_pb2, elasticdl_pb2_grpc\nfrom elasticdl.python.collective_ops.communicator import CollectiveCommunicator\nfrom elasticdl.python.common.constants im... | [
[
"numpy.concatenate",
"tensorflow.GradientTape",
"tensorflow.concat",
"tensorflow.saved_model.save",
"numpy.empty_like",
"tensorflow.math.add_n"
]
] |
ermiasgelaye/web-scraping-challenge | [
"f99c3436dfb0169595c46dae7733d90e21385cc6",
"f99c3436dfb0169595c46dae7733d90e21385cc6",
"f99c3436dfb0169595c46dae7733d90e21385cc6"
] | [
".history/Missions_to_Mars/scrape_mars_20200809102137.py",
".history/Missions_to_Mars/scrape_mars_20200808193953.py",
".history/Missions_to_Mars/scrape_mars_20200808195409.py"
] | [
"from splinter import Browser\nfrom bs4 import BeautifulSoup as bs\nimport pandas as pd\nimport time\nimport re\n\n# This is for debugging\n\ndef savetofile(contents):\n file = open('_temporary.txt',\"w\",encoding=\"utf-8\")\n file.write(contents)\n file.close()\n\n\ndef scrape():\n executable_path = {\... | [
[
"pandas.read_html"
],
[
"pandas.read_html"
],
[
"pandas.read_html"
]
] |
kaushal-py/seg-degan | [
"a74c506d5b1b67901a7e0663fba02ddb44f762a6"
] | [
"code/utils/ext_transforms.py"
] | [
"import torchvision\nimport torch\nimport torchvision.transforms.functional as F\nimport random \nimport numbers\nimport numpy as np\nfrom PIL import Image\n\n\n#\n# Extended Transforms for Semantic Segmentation\n#\nclass ExtRandomHorizontalFlip(object):\n \"\"\"Horizontally flip the given PIL Image randomly wi... | [
[
"numpy.array"
]
] |
sevakon/Practical_DL | [
"10f01d8bdeaafbff0f1cfbfa3160dfc81f355f0f"
] | [
"seminar07-gen_models_2/supervised_deformation_finder/utils.py"
] | [
"import torch\nimport torch.nn.functional as F\n\nmean_imagenet = torch.FloatTensor([0.485, 0.456, 0.406])[:, None, None]\nstd_imagenet = torch.FloatTensor([0.229, 0.224, 0.225])[:, None, None]\n\n\ndef prepare_generator_output_for_celeba_regressor(imgs):\n imgs = torch.clamp(imgs, -1, 1)\n imgs = F.interpola... | [
[
"torch.FloatTensor",
"torch.clamp",
"torch.nn.functional.interpolate"
]
] |
izzatnadzmi/ConcentricTubeRobot | [
"a10f3648bdc70509f123ea7818a664165ee069a4"
] | [
"controller.py"
] | [
"'''\n Author: Izzat Kamarudzaman\n Python Version: 3.7.2\n\n Main code for CTR system. Calls CTR model and trajectory generator.\n Also contains classes for Jacobian Linearisation and Controller.\n'''\n\nimport numpy as np\nimport sys\nimport time\nsys.path.append(\"../\")\nsys.path.append(\"./test\")\... | [
[
"numpy.array",
"numpy.random.choice",
"numpy.zeros",
"matplotlib.pyplot.xlabel",
"numpy.ones",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.legend",
"numpy.mean",
"numpy.eye",
"matplotlib.pyplot.subplots",
"numpy.arange",
"numpy.abs",
"matplotlib.pyplot.ylabel",... |
dbczumar/clipper | [
"80c97d27a38d60caaebb2a1ae6a995dd7ff1c82d"
] | [
"examples/basic_query/example_client.py"
] | [
"from __future__ import print_function\nfrom clipper_admin import ClipperConnection, DockerContainerManager\nfrom clipper_admin.deployers import python as python_deployer\nimport json\nimport requests\nfrom datetime import datetime\nimport time\nimport numpy as np\nimport signal\nimport sys\n\n\ndef predict(addr, x... | [
[
"numpy.random.random"
]
] |
mortonjt/cockatoo | [
"f99634b39daf885be8dd12f098174eca340864fe"
] | [
"cockatoo/sim.py"
] | [
"import numpy as np\nfrom skbio.stats.composition import closure\nfrom gneiss.cluster import random_linkage\nfrom gneiss.balances import _balance_basis\nfrom scipy.stats import ortho_group\n\n\ndef multinomial_bioms(k, D, N, M, min_sv=0.11, max_sv=5.0, sigma_sq=0.1):\n \"\"\" Simulates biom tables from multinomi... | [
[
"numpy.random.normal",
"numpy.matmul",
"numpy.zeros",
"scipy.stats.ortho_group.rvs",
"numpy.random.poisson",
"numpy.exp",
"numpy.random.randint",
"numpy.sqrt",
"numpy.abs",
"numpy.linspace",
"numpy.diag",
"numpy.random.multinomial"
]
] |
joshcalcino/plonk | [
"1372d8b866483e5d19bea340e9b21c766fdafe8c"
] | [
"plonk/utils/math.py"
] | [
"\"\"\"Utils for math.\"\"\"\n\nimport numpy as np\nfrom numpy import ndarray\n\nfrom .._units import Quantity\n\n\ndef cross(x, y, **kwargs):\n \"\"\"Cross product.\n\n Parameters\n ----------\n x, y\n The two arrays (N, 3) to take the cross product of. Can be\n ndarray or pint Quantity.\... | [
[
"numpy.average",
"numpy.linalg.norm",
"numpy.cross",
"numpy.sqrt"
]
] |
ibobak/shap | [
"4d8d55b5e3fca7eac0f0c983ed6fc66d4b45625b"
] | [
"shap/plots/_force.py"
] | [
"\"\"\" Visualize the SHAP values with additive force style layouts.\n\"\"\"\n\nfrom __future__ import division, unicode_literals\nimport os\nimport io\nimport string\nimport json\nimport random\ntry:\n from IPython.core.display import display, HTML\n from IPython import get_ipython\n have_ipython = True\n... | [
[
"scipy.sparse.issparse",
"numpy.sum",
"numpy.flipud",
"numpy.argsort",
"numpy.all",
"numpy.vstack"
]
] |
maierbn/opendihu | [
"577650e2f6b36a7306766b0f4176f8124458cbf0",
"577650e2f6b36a7306766b0f4176f8124458cbf0",
"577650e2f6b36a7306766b0f4176f8124458cbf0",
"577650e2f6b36a7306766b0f4176f8124458cbf0"
] | [
"examples/electrophysiology/fibers/multiple_fibers_cubes_partitioning/helper.py",
"examples/electrophysiology/fibers/fibers_fat_emg/extract_electrodes.py",
"examples/electrophysiology/fibers/analytical_fibers_emg/plot_rosenfalck.py",
"examples/electrophysiology/fibers/fibers_fat_emg/failed/helper.py"
] | [
"# Multiple 1D fibers (monodomain)\n# This is a helper script that sets a lot of the internal variables which are all defined in variables.py\n# This script for multiple_fibers is a copy of the file in fibers_emg.\n#\n# if variables.fiber_file=cuboid.bin, it uses a small cuboid test example\n\nimport numpy as np\ni... | [
[
"numpy.round",
"numpy.linspace",
"numpy.genfromtxt",
"numpy.size"
],
[
"numpy.genfromtxt",
"numpy.mean"
],
[
"matplotlib.pyplot.rcParams.update",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.plot",
"numpy.exp",
"matplotlib.pypl... |
passion4energy/mpca | [
"f3c2b695105a0c2e625064b435931a48d93cb11e",
"f3c2b695105a0c2e625064b435931a48d93cb11e"
] | [
"mpca/pcamodel.py",
"examples/simple_example.py"
] | [
"from mpca.utils.datahandling import read_h5, write_h5\nfrom sklearn.decomposition import PCA\nimport numpy as np\n\n\nclass pcamodel:\n\t'''\n\tWrapper class for a 2-component PCA model.\n\n\tPCA is performed using sklearn PCA.\n\n\tThe model is defined by the loadings and the scores.\n\n\tImplements saving and lo... | [
[
"sklearn.decomposition.PCA",
"numpy.zeros",
"numpy.mean"
],
[
"numpy.delete",
"numpy.dot",
"numpy.random.choice",
"sklearn.preprocessing.StandardScaler",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.savefig",
"matplotlib.pyplot.legend",
"matplotlib.pyplot.close",
... |
nickchak21/particledist | [
"59b788a894655273ec177a3a6bb4cf9526f8c402"
] | [
"build/lib/particledist/ParticleDistributionCMSEfficient.py"
] | [
"#!/usr/bin/env python3\n\nfrom __future__ import absolute_import, division, print_function\nfrom time import process_time\nimport energyflow as ef\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass ParticleDistributionCMSEfficient:\n def __init__(self, sim):\n t1_start = process_time()\n\n ... | [
[
"numpy.array"
]
] |
PyFund/iexcloud | [
"c4c2a558e3ebeca60acfee43399ef51c4881dcd0"
] | [
"iexcloud/stock.py"
] | [
"import pandas as pd\nimport requests\nimport json\n\nfrom typing import Union, List\nfrom requests import Response\nfrom iexcloud.config import get_token, get_url\n\n\nclass Stock(object):\n def __init__(self, symbol: str, output=\"pandas\"):\n\n self.symbol: str = symbol\n self.output: str = outp... | [
[
"pandas.read_json"
]
] |
hli8nova/ODSC2021_NLP | [
"4de1b599913114f29be03ead8a6038d4ab762ead"
] | [
"part1/07a_disaster_detection_tfidf.py"
] | [
"#!/usr/bin/env python\n# coding: utf-8\n\n# ## Disaster or not: Text Classification using TFIDF and Logistic Regression\n\nimport numpy as np\nimport pandas as pd\nimport pickle\n\n\n# ### Load data\n\nfrom pathlib import Path\n\ncwd = Path(__file__).parent\ndata_path = cwd / \"disaster_data/train.csv\"\npd.read_c... | [
[
"sklearn.model_selection.train_test_split",
"sklearn.linear_model.LogisticRegression",
"pandas.read_csv",
"sklearn.feature_extraction.text.TfidfVectorizer"
]
] |
richjoyce/stader | [
"24811178026fe057a51c279ec99e79aae733659f"
] | [
"examples/horizon2.py"
] | [
"import matplotlib.pyplot as plt\nfrom matplotlib.animation import FuncAnimation\nfrom matplotlib.patches import Polygon\nimport numpy as np\n\nimport stader\n\nd = stader.load_aircraft('b747_flight_condition2')\nac = stader.Aircraft(d)\n\nmsec = 15\ndt = msec/1000\n\nshow_state = False\nif show_state:\n fig = p... | [
[
"numpy.array",
"numpy.sin",
"matplotlib.animation.FuncAnimation",
"numpy.rad2deg",
"matplotlib.patches.Polygon",
"numpy.ones",
"matplotlib.pyplot.subplots",
"matplotlib.pyplot.figure",
"matplotlib.pyplot.subplot2grid",
"numpy.cos",
"numpy.deg2rad",
"matplotlib.pyplo... |
Poseyy/eth_analytics | [
"068ac14c868f2fb062567b7ed820869b5c4f1f59"
] | [
"defi_historical/misc/users.py"
] | [
"import pandas as pd \nimport plotly.graph_objects as go\nimport plotly.express as px\n\n# Users over time \nusers_data = pd.read_csv('data/total_users.csv') \nuniswap_users_data = pd.read_csv('data/uniswap_users.csv') \ncompound_users_data = pd.read_csv('data/compound_users.csv') \nyearn_users_data = pd.read_cs... | [
[
"pandas.read_csv"
]
] |
adamoppenheimer/bipartitepandas | [
"2690143d4880d41d99324eb23d855ba804cf6c64"
] | [
"bipartitepandas/bipartitebase.py"
] | [
"'''\nClass for a bipartite network\n'''\nfrom pandas.core.indexes.base import InvalidIndexError\nfrom tqdm.auto import tqdm\nimport numpy as np\n# from numpy_groupies.aggregate_numpy import aggregate\nimport pandas as pd\nfrom pandas import DataFrame, Int64Dtype\n# from scipy.sparse.csgraph import connected_compon... | [
[
"numpy.max",
"pandas._lib.fast_zip",
"numpy.median",
"pandas.DataFrame",
"numpy.min",
"numpy.mean",
"pandas.DataFrame.rename",
"pandas.factorize",
"pandas.DataFrame.drop",
"pandas.DataFrame.merge",
"numpy.var"
]
] |
furyou81/superset | [
"319125ec3b292a9e92666e059580b8ddd952c832"
] | [
"superset/utils/core.py"
] | [
"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); y... | [
[
"pandas.to_datetime",
"pandas.core.dtypes.common.is_numeric_dtype",
"pandas.api.types.infer_dtype"
]
] |
wanghuajing/unet-pytorch | [
"53e187e15d3676f08c8f594017f026047faff66a"
] | [
"train.py"
] | [
"import time\n\nimport numpy as np\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.optim as optim\nimport torchvision.models as models\nfrom PIL import Image\nfrom torch import nn\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\nfrom torchvision import models\nfrom tq... | [
[
"torch.optim.lr_scheduler.StepLR",
"torch.no_grad",
"numpy.shape",
"torch.from_numpy",
"torch.cuda.is_available",
"torch.utils.data.DataLoader",
"torch.load",
"torch.nn.DataParallel"
]
] |
taebinkim7/weighted-dwd | [
"6b3bbf1d7f4f1467a2c3483dcf4fc1b212a8acfa"
] | [
"wdwd/svm.py"
] | [
"import cvxpy as cp\nimport numpy as np\n\nfrom sklearn.base import BaseEstimator # , TransformerMixin, ClassifierMixin\n\nfrom wdwd.linear_model import LinearClassifierMixin\n\n\ndef solve_svm(X, y, C, sample_weight=None, solver_kws={}):\n \"\"\"\n Solves soft-margin SVM problem.\n\n min_{beta, intercept... | [
[
"numpy.unique"
]
] |
michaelfeil/dtu_mlops_cookiecutter | [
"eb29f44f09e01c76a21be71074603ee5f87605ef"
] | [
"dtumlops/visualization/visualize.py"
] | [
"import os\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport torch\nfrom sklearn.manifold import TSNE\n\nfrom dtumlops.data.data_utils import retrieve_mnist\nfrom dtumlops.models.model import MyAwesomeModel\n\nfeatures = {}\na = 0\nclass VisCNNFeatures:\n def get_features(self, name):\n \"... | [
[
"sklearn.manifold.TSNE",
"torch.load"
]
] |
DEKHTIARJonathan/horovod | [
"333ce607c5ed0c5a38defd234f818aeb27a5394b"
] | [
"test/parallel/test_torch.py"
] | [
"# Copyright 2018 Uber Technologies, Inc. All Rights Reserved.\n# Modifications copyright (C) 2019 Intel Corporation\n# Modifications copyright (C) 2020, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance... | [
[
"torch.nn.Linear",
"torch.cat",
"torch.ones",
"torch.nn.Parameter",
"torch.cuda.is_available",
"torch.cuda.FloatTensor",
"torch.load",
"torch.allclose",
"numpy.linalg.norm",
"torch.IntTensor",
"torch.is_tensor",
"torch.FloatTensor",
"torch.manual_seed",
"tor... |
HenriJSantos/MS | [
"313f2b72f42fa21121a5266df9708b6f12d7545a"
] | [
"code/weekday_aggregator.py"
] | [
"import pandas as pd\n\ndf = pd.read_csv(\"data/aggregated/agg.csv\", header=0, sep=\",\", low_memory=False)\ndf['AGG_PERIOD_START'] = pd.to_datetime(df['AGG_PERIOD_START'])\n\n#remove holidays\ndf = df[(df['AGG_PERIOD_START'] != pd.to_datetime(\"2015-01-01\")) & # Dia de Ano Novo\n (df['AGG_PERIOD_STAR... | [
[
"pandas.to_datetime",
"pandas.read_csv"
]
] |
SaschaKersken/Daten-Prozessanalyse | [
"370f07a75b9465329deb3671adbfbef8483f76f6"
] | [
"listings/chapter08/neural-network-keras-mnist.py"
] | [
"import numpy as np\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\n\n# Kategorien, \ncategories = 10\n\n# Daten laden\n(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n\n# Feature-Skalierung\nx_train = x_train.astype(\"float32\") / 255\nx_test = x_test.astype(\"float32\"... | [
[
"tensorflow.keras.utils.to_categorical",
"tensorflow.keras.datasets.mnist.load_data",
"tensorflow.keras.layers.Flatten",
"tensorflow.keras.layers.Dense",
"tensorflow.keras.layers.Conv2D",
"tensorflow.keras.layers.Dropout",
"tensorflow.keras.layers.MaxPooling2D",
"tensorflow.keras.I... |
tiralonghipol/Firmware | [
"1d562aaf4a0f7351d64fa6c5ec3372d9cb7755c8"
] | [
"Tools/ecl_ekf/plotting/pdf_report.py"
] | [
"#! /usr/bin/env python3\n\"\"\"\nfunction collection for plotting\n\"\"\"\n\n# matplotlib don't use Xwindows backend (must be before pyplot import)\nimport matplotlib\nmatplotlib.use('Agg')\n\nimport numpy as np\nfrom matplotlib.backends.backend_pdf import PdfPages\nfrom pyulog import ULog\n\nfrom analysis.post_pr... | [
[
"matplotlib.use",
"matplotlib.backends.backend_pdf.PdfPages",
"numpy.diff",
"numpy.amax",
"numpy.amin"
]
] |
sehagler/OrorbiaMikolovReitter2017 | [
"e3717abdf7140b557843d24fef4a0948fedc5216"
] | [
"py/scrn.py"
] | [
"# Delta Recurrent Neural Network (Delta-RNN) Framework\n#\n# This gives an implementation of the Delta-RNN framework given in Ororbia et al. 2017, arXiv:1703.08864 [cs.CL], \n# https://arxiv.org/abs/1703.08864 using Python and Tensorflow.\n#\n# This code implements a variety of RNN models using the Delta-RNN Frame... | [
[
"tensorflow.truncated_normal",
"tensorflow.matmul",
"tensorflow.name_scope"
]
] |
frizman04/language-style-transfer-python3 | [
"9110eb9d5b72d2926f805ac258915c0f1a369638"
] | [
"code/language_model.py"
] | [
"import os\r\nimport sys\r\nimport time\r\nimport ipdb\r\nimport random\r\nimport numpy as np\r\nimport tensorflow as tf\r\n\r\nfrom vocab import Vocabulary, build_vocab\r\nfrom options import load_arguments\r\nfrom file_io import load_sent\r\nfrom utils import *\r\nfrom nn import *\r\n\r\nclass Model(object):\r\n\... | [
[
"tensorflow.train.AdamOptimizer",
"numpy.sum",
"tensorflow.Session",
"tensorflow.train.Saver",
"numpy.exp",
"tensorflow.reshape",
"tensorflow.matmul",
"tensorflow.ConfigProto",
"tensorflow.variable_scope",
"tensorflow.placeholder",
"tensorflow.reduce_sum",
"tensorfl... |
240400968/SoftMaskedBert-PyTorch | [
"50cdd74ea7101f0043fdb532bfdd2381e1630105"
] | [
"main.py"
] | [
"\"\"\"\n@Time : 2021-01-12 15:23:56\n@File : main.py\n@Author : Abtion\n@Email : abtion{at}outlook.com\n\"\"\"\nimport argparse\nimport os\nimport torch\nfrom transformers import BertTokenizer\nfrom src.dataset import get_corrector_loader\nfrom src.models import SoftMaskedBertModel\nfrom src.data_proc... | [
[
"torch.device",
"torch.cuda.is_available"
]
] |
jomorlier/eVTOL | [
"753b9261ebb3180bdc344120766c0190f355be90"
] | [
"model_validation_studies/rotor_validation/rotor_validation.py"
] | [
"#Validation of rotor model against test data\n\nimport os\nimport sys\nsys.path.append(os.path.abspath(os.path.dirname(__file__) + '/../..'))\n\nimport numpy as np\nfrom gpkit import Variable, Model, Vectorize, ureg\nfrom matplotlib import pyplot as plt\nfrom aircraft_models import Rotors, FlightState, RotorsAero\... | [
[
"matplotlib.pyplot.xlim",
"numpy.size",
"matplotlib.pyplot.savefig",
"numpy.arange",
"matplotlib.pyplot.tight_layout",
"matplotlib.pyplot.gca",
"matplotlib.pyplot.subplots_adjust",
"matplotlib.pyplot.subplot",
"numpy.array",
"matplotlib.pyplot.title",
"matplotlib.pyplot... |
ClarenceYC/tianshou | [
"39f8391cfb4f219a267c1040e2d463be91c645b0",
"39f8391cfb4f219a267c1040e2d463be91c645b0"
] | [
"test/base/test_returns.py",
"tianshou/policy/modelfree/sac.py"
] | [
"from timeit import timeit\n\nimport numpy as np\nimport torch\n\nfrom tianshou.data import Batch, ReplayBuffer, to_numpy\nfrom tianshou.policy import BasePolicy\n\n\ndef compute_episodic_return_base(batch, gamma):\n returns = np.zeros_like(batch.rew)\n last = 0\n for i in reversed(range(len(batch.rew))):\... | [
[
"numpy.array",
"numpy.zeros_like",
"numpy.allclose",
"torch.tensor",
"numpy.random.randint",
"numpy.random.random"
],
[
"torch.Size",
"torch.min",
"torch.distributions.Normal",
"numpy.finfo",
"torch.tanh"
]
] |
gamleksi/affordance_gym | [
"e2e6eeafd4d725c5c912ac97d4d5ad1cf318af5f"
] | [
"affordance_gym/src/affordance_gym/remote_interface.py"
] | [
"import rospy\nimport numpy as np\nimport cv_bridge\nfrom sensor_msgs.msg import Image\nimport tf\nfrom std_srvs import srv\nfrom affordance_gym.srv import RobotTrajectory, ChangePose, JointValues, JointNames, CurrentPose\n\n'''\n\n\nThis Moveit Commander Interface communicates with the mc_interface node (mc_interf... | [
[
"numpy.uint8"
]
] |
TerenceChen95/Bladder-Cancer-Stage-Detection | [
"1f97c6835629c7fe82def0e8dba3d7a941ee3a2c"
] | [
"resnet50/main_resnet50.py"
] | [
"# -*- coding: utf-8 -*-\r\n\r\nfrom torchvision import transforms,models\r\nimport torch\r\nimport torch.optim as optim\r\nimport torch.nn as nn\r\nfrom collections import OrderedDict\r\nfrom train import train_function, save_checkpoint\r\nfrom test import test_function\r\nfrom dataset import Dataset\r\nfrom PIL i... | [
[
"torch.nn.Linear",
"torch.device",
"torch.nn.Dropout",
"torch.optim.lr_scheduler.StepLR",
"torch.nn.Softmax",
"torch.utils.data.random_split",
"torch.nn.ReLU",
"torch.cuda.is_available",
"torch.utils.data.DataLoader",
"torch.load",
"torch.nn.CrossEntropyLoss"
]
] |
yifan-you-37/GRAC-1 | [
"22b2cde651ae4416475d9594b93ad1c430090144"
] | [
"TD3_ori_noise_v1.py"
] | [
"import copy\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n# Implementation of Twin Delayed Deep Deterministic Policy Gradients (TD3)\n# Paper: https://arxiv.org/abs/1802.09477\n\n\nclass Actor(nn.Module):\n\tdef __init__(self, state_dim, action_dim, max_action):\n\t\t... | [
[
"torch.nn.Linear",
"torch.min",
"torch.no_grad",
"torch.std",
"torch.nn.functional.mse_loss",
"torch.randn_like",
"torch.load",
"torch.mean",
"torch.randn"
]
] |
mukul-ch/apitest | [
"1985d4c73fabd5f08f54b922e73a9306e09c77a5"
] | [
"Lib/site-packages/pandas/tests/io/parser/compression.py"
] | [
"# -*- coding: utf-8 -*-\n\n\"\"\"\nTests compressed data parsing functionality for all\nof the parsers defined in parsers.py\n\"\"\"\n\nimport pytest\n\nimport pandas.util.testing as tm\n\n\nclass CompressionTests(object):\n\n def test_zip(self):\n import zipfile\n\n with open(self.csv1, 'rb') as ... | [
[
"pandas.util.testing.assert_frame_equal",
"pandas.util.testing.assert_raises_regex",
"pandas.util.testing.ensure_clean",
"pandas.util.testing._skip_if_no_lzma"
]
] |
Dimitri1/tensorflow | [
"f6abc3f094b27385a5e0c18ac3edd646d022890b"
] | [
"tensorflow/contrib/quantize/python/quantize.py"
] | [
"# Copyright 2017 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless requ... | [
[
"tensorflow.contrib.quantize.python.common.DropStringPrefix",
"tensorflow.python.ops.control_flow_ops.cond",
"tensorflow.contrib.quantize.python.quant_ops.LastValueQuantize",
"tensorflow.python.platform.tf_logging.info",
"tensorflow.contrib.quantize.python.common.RerouteTensor",
"tensorflo... |
CAG-ru/geonorm | [
"891492927092ee1eb5d0a70623952076fdbeea52"
] | [
"geonorm/geomatch.py"
] | [
"import pandas as pd\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport pathlib\nimport logging\nfrom copy import copy\nimport numpy as np\n\n\nclass Geomatch:\n def __init__(self,\n standard_db=False,\n # п... | [
[
"sklearn.feature_extraction.text.TfidfVectorizer",
"sklearn.metrics.pairwise.cosine_similarity"
]
] |
valentjn/thesis | [
"65a0eb7d5f7488aac93882959e81ac6b115a9ea8"
] | [
"lib/pysgpp/extensions/datadriven/uq/sampler/Sample.py"
] | [
"import numpy as np\nimport pysgpp.extensions.datadriven.uq.jsonLib as ju\nfrom copy import copy\n\n\nclass SampleType:\n \"\"\"\n Describes the type of a sample:\n An active sample is a sample that just contains uncertain\n parameters for which no deterministic value has been specified.\n This allow... | [
[
"numpy.array"
]
] |
KohYoungResearchAmerica/MONAI | [
"eca3f19182b9fcee0be7123728a9826cd382d152",
"eca3f19182b9fcee0be7123728a9826cd382d152"
] | [
"tests/test_spatial_cropd.py",
"monai/networks/layers/spatial_transforms.py"
] | [
"# Copyright 2020 - 2021 MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agre... | [
[
"numpy.random.randint"
],
[
"torch.cat",
"torch.tensor"
]
] |
geoffryan/dynesty | [
"243025cbfc5f1941e9dc1b00348c54f8b877bd6c"
] | [
"py/dynesty/bounding.py"
] | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nBounding classes used when proposing new live points, along with a number of\nuseful helper functions. Bounding objects include:\n\n UnitCube:\n The unit N-cube (unconstrained draws from the prior).\n\n Ellipsoid:\n Bounding ellipsoid.\n\n... | [
[
"numpy.dot",
"scipy.linalg.pinvh",
"scipy.linalg.eigh",
"scipy.cluster.hierarchy.fcluster",
"numpy.exp",
"numpy.mean",
"scipy.spatial.KDTree",
"numpy.where",
"numpy.finfo",
"scipy.linalg.cholesky",
"numpy.max",
"scipy.special.logsumexp",
"numpy.log",
"numpy.... |
RikoNyberg/simulation_of_epidemics | [
"78d893279f73a324e3b8a462002ca211ab35ef78"
] | [
"best_protection_strategy.py"
] | [
"import random\nfrom sklearn.datasets import base\nimport numpy as np\nimport networkx as nx\n\n# Task 5\ndef get_immunized_random(epidemic_graph, n_nodes):\n nodes_to_immunize = random.sample(range(len(epidemic_graph)), n_nodes)\n return nodes_to_immunize\n\n\ndef get_immunized_neighbors(epidemic_graph, n_nodes)... | [
[
"sklearn.datasets.base.Bunch"
]
] |
jianrenw/SOD-TGNN | [
"2533508f9565edee71af96202086ecc688b3dfe0"
] | [
"nuscenes_flicker/vis_iso.py"
] | [
"import numpy as np\nimport json\nimport argparse\nimport os\nimport os.path as osp\nimport matplotlib.pyplot as plt\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--det_dir', type=str)\nparser.add_argument('--output_dir', type=str)\nparser.add_argument('--linear', type=int)\n\nargs = parser.parse_args... | [
[
"numpy.array",
"matplotlib.pyplot.xlabel",
"matplotlib.pyplot.plot",
"matplotlib.pyplot.close",
"matplotlib.pyplot.figure",
"numpy.digitize",
"matplotlib.pyplot.ylabel",
"matplotlib.pyplot.scatter"
]
] |
Aeocp/SpeedDetect_DeepSORT_YOLOX | [
"35048d4753173ee739bb9e855c3227f844a8a8b0"
] | [
"yolox/utils/visualize.py"
] | [
"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.\n\nimport cv2\nimport numpy as np\nimport mmglobal\n\n__all__ = [\"vis\"]\n\n\ndef vis(img, boxes, scores, cls_ids, conf=0.5, class_names=None):\n\n with open(\"/content/test.txt\", \"a\") as writefile:\n ... | [
[
"numpy.array",
"numpy.mean"
]
] |
AntonBiryukovUofC/diffvg | [
"e081098f52b82bfd0b7e91114d289d65ef969a60"
] | [
"apps/generative_models/sketch_vae.py"
] | [
"#!/bin/env python\n\"\"\"Train a Sketch-VAE.\"\"\"\nimport argparse\nfrom enum import Enum\nimport os\nimport wget\nimport time\n\nimport numpy as np\nimport torch as th\nfrom torch.utils.data import DataLoader\nimport torchvision.datasets as dset\nimport torchvision.transforms as transforms\n\nimport ttools\nimpo... | [
[
"torch.nn.Linear",
"torch.cat",
"torch.nn.LSTM",
"torch.nn.Sigmoid",
"numpy.random.seed",
"torch.nn.Tanh",
"torch.nn.ConvTranspose2d",
"torch.optim.lr_scheduler.ExponentialLR",
"torch.clamp",
"torch.manual_seed",
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.cuda.i... |
SCE-SWE-2018-G11/NumericAnalysis | [
"eddca9b35598110227a41de3fa1ed24504b07209"
] | [
"Akaton_2/akaton_2_code/romberg_integration.py"
] | [
"\r\nfrom scipy import integrate\r\nimport numpy as np\r\n\r\ndef romberg(f, a, b):\r\n\r\n return integrate.romberg(f, a, b, show=True)\r\nprint(\"Integral: \", romberg(lambda x: 1/(1+ x**5), 0, 3))\r\n"
] | [
[
"scipy.integrate.romberg"
]
] |
simonbogh/skrl | [
"4ca483c7d24fdf020bdbd9422927ef5737c9dec4",
"4ca483c7d24fdf020bdbd9422927ef5737c9dec4"
] | [
"docs/source/snippets/deterministic_model.py",
"docs/source/snippets/categorical_model.py"
] | [
"import gym\n\nclass DummyEnv:\n observation_space = gym.spaces.Box(low=-1, high=1, shape=(4,))\n action_space = gym.spaces.Box(low=-1, high=1, shape=(3,))\n device = \"cuda:0\"\n\nenv = DummyEnv()\n\n# [start-mlp]\nimport torch\nimport torch.nn as nn\n\nfrom skrl.models.torch import DeterministicModel\n\n... | [
[
"torch.Size",
"torch.nn.Linear",
"torch.cat",
"torch.nn.Tanh",
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.randn",
"torch.nn.Flatten"
],
[
"torch.Size",
"torch.nn.Linear",
"torch.nn.Tanh",
"torch.nn.ReLU",
"torch.nn.Conv2d",
"torch.randn",
"torch.nn.F... |
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