repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
dataqa | dataqa-master/continuum/validation_tool/catalogue.py | from __future__ import division
from functions import axis_lim, flux_at_freq, two_freq_power_law, config2dic, SED
import os
import glob
import numpy as np
import pandas as pd
from astropy.io import fits as f
from astropy.table import Table
from astropy.coordinates import SkyCoord
from astropy.io.votable import parse_s... | 63,782 | 45.286647 | 160 | py |
dataqa | dataqa-master/continuum/validation_tool/radio_image.py | from __future__ import division
from functions import remove_extn, get_pixel_area
import os
import numpy as np
from astropy.io import fits as f
from astropy.wcs import WCS
from astropy.coordinates import SkyCoord
from astropy.utils.exceptions import AstropyWarning
import warnings
from inspect import currentframe, get... | 15,725 | 36.265403 | 139 | py |
dataqa | dataqa-master/crosscal/__init__.py | 0 | 0 | 0 | py | |
dataqa | dataqa-master/crosscal/dish_delay_plot.py | """
Simple function to plot the dish delay.
This needs to run separately from the other crosscal plots because
it requires all 40 beams to be accessible
"""
from crosscal_plots import GDSols
def get_dish_delay_plots(obs_id, fluxcal, basedir=None):
GD = GDSols(obs_id, fluxcal, False, basedir=basedir)
GD.get_... | 352 | 22.533333 | 66 | py |
dataqa | dataqa-master/crosscal/crosscal_plots.py | #python "module" for QA plots for cross-cal
#Will want to plot calibration solutions
#also potential for raw and corrected data
from __future__ import print_function
#load necessary packages
import os
import numpy as np
from astropy.io import ascii
import apercal
import casacore.tables as pt
import logging
import mat... | 57,674 | 42.825988 | 145 | py |
dataqa | dataqa-master/inspection_plots/__init__.py | 0 | 0 | 0 | py | |
dataqa | dataqa-master/inspection_plots/inspection_plots.py | # Module with functionality to get the inspection plots for an Apertif observation
import numpy as np
import os
import glob
import logging
import subprocess
logger = logging.getLogger(__name__)
FNULL = open(os.devnull, 'w')
def get_inspection_plot_list(is_calibrator=False):
"""
Function to return a list of... | 5,046 | 32.423841 | 248 | py |
dataqa | dataqa-master/osa_overview/create_report_nb.py | # version of ../create_report to run as part of the pipeline
import os
import sys
from astropy.table import Table
import logging
import glob
import time
import argparse
import socket
from apercal.libs import lib
from dataqa.report import html_report as hp
from dataqa.report import html_report_dir as hpd
from dataqa.r... | 4,528 | 33.052632 | 133 | py |
dataqa | dataqa-master/selfcal/selfcal_maps.py | """
This script contains functionality to plot the selfcal images
"""
import os
from apercal.libs import lib
import glob
import socket
import logging
from astropy.io import fits
from astropy.wcs import WCS
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.colors as mc
logger = ... | 10,194 | 32.983333 | 128 | py |
dataqa | dataqa-master/selfcal/selfcal_plots.py | # python "module" for QA plots for cross-cal
# Will want to plot calibration solutions
# also potential for raw and corrected data
# load necessary packages
import os
import numpy as np
import datetime
from apercal.subs import readmirlog
from apercal.subs import misc
import matplotlib.pyplot as plt
from matplotlib.pyp... | 7,673 | 44.952096 | 119 | py |
dataqa | dataqa-master/selfcal/__init__.py | 0 | 0 | 0 | py | |
robust-nli | robust-nli-master/src/losses.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def convert_2d_prob_to_3d(prob_dist):
prob_dist = torch.cat([(prob_dist[:, 0] / 2.0).view(-1, 1),
prob_dist[:, 1].view(-1, 1),
(prob_dist[:, 0] / 2.0).view(-1, 1)], dim=1)
return prob_dist
... | 4,401 | 35.081967 | 116 | py |
robust-nli | robust-nli-master/src/BERT/utils_glue.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 cop... | 29,876 | 37.550968 | 154 | py |
robust-nli | robust-nli-master/src/BERT/run_glue.py | """ Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet)."""
from __future__ import absolute_import, division, print_function
import logging
import os
import random
from utils_glue import GLUE_TASKS_NUM_LABELS
from eval_utils import load_and_cache_examples, evaluate, get_parser
import ... | 13,321 | 42.966997 | 163 | py |
robust-nli | robust-nli-master/src/BERT/heuristics_utils.py | # These codes are from the codes for
# Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference by
# Tom McCoy, Ellie Pavlick, Tal Linzen, ACL 2019
def have_lexical_overlap(premise, hypothesis, get_hans_new_features=False):
prem_words = []
hyp_words = []
for word in premi... | 2,890 | 28.20202 | 95 | py |
robust-nli | robust-nli-master/src/BERT/__init__.py | 0 | 0 | 0 | py | |
robust-nli | robust-nli-master/src/BERT/utils_bert.py | import torch
from torch import nn
import sys
sys.path.append("../")
from torch.nn import CrossEntropyLoss, MSELoss
from pytorch_transformers.modeling_bert import BertPreTrainedModel, BertModel
from losses import FocalLoss, POELoss, RUBILoss
from utils_glue import get_word_similarity_new, get_length_features
from mutil... | 12,786 | 48.949219 | 134 | py |
robust-nli | robust-nli-master/src/BERT/eval_utils.py | from torch.utils.data import (DataLoader, SequentialSampler, TensorDataset)
from os.path import join
import numpy as np
from utils_glue import (compute_metrics, convert_examples_to_features,
processors)
import argparse
import torch
import os
import glob
import logging
from tqdm import tqdm, tran... | 25,678 | 51.620902 | 135 | py |
robust-nli | robust-nli-master/src/BERT/mutils.py | import csv
import os
import torch
def write_to_csv(scores, params, outputfile):
"""
This function writes the parameters and the scores with their names in a
csv file.
"""
# creates the file if not existing.
file = open(outputfile, 'a')
# If file is empty writes the keys to the file.
p... | 1,741 | 30.107143 | 77 | py |
robust-nli | robust-nli-master/src/InferSent/data.py | # Copyright (c) 2017-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
def get_batch(batch, word_vec, emb_dim=300):
# sent in batch in decreasing order ... | 3,317 | 33.926316 | 84 | py |
robust-nli | robust-nli-master/src/InferSent/models.py | # Copyright (c) 2017-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.
#
"""
This file contains the definition of encoders used in https://arxiv.org/pdf/1705.02364.pdf
"""
import time
import sys
sys.... | 15,032 | 34.878282 | 115 | py |
robust-nli | robust-nli-master/src/InferSent/train_nli.py | # Copyright (c) 2017-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
import time
import argparse
import os
import numpy as np
import torch
from torch.autograd import Variable
from d... | 14,974 | 39.582656 | 130 | py |
robust-nli | robust-nli-master/src/InferSent/mutils.py | # Copyright (c) 2017-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 inspect
from torch import optim
import torch
import os
import csv
def construct_model_name(params, names_par... | 4,580 | 28.941176 | 79 | py |
robust-nli | robust-nli-master/data/scripts/nli_hardset.py | import json_lines
import os
import argparse
def process_nli_hardset(datapth, outdir):
if not os.path.exists(outdir):
os.makedirs(outdir)
# Writes data in the file.
sentences2 = []
sentences1 = []
labels = []
pair_ids = []
with open(datapth, 'rb') as f:
for item in json_line... | 1,411 | 33.439024 | 103 | py |
robust-nli | robust-nli-master/data/scripts/glue_diagnostic.py | # This scripts process the SICK-E dataset and
# writes it in the format of SNLI dataset.
import os
import argparse
import pandas as pd
import numpy as np
class GlueDiagnosticDataset(object):
def __init__(self, testpath, outputdir):
self.testpath = testpath
self.outputdir = outputdir
# Creat... | 2,134 | 37.125 | 115 | py |
robust-nli | robust-nli-master/data/scripts/recast_white.py | import os
import argparse
import glob
class RecastWhiteDataset(object):
def __init__(self, datadir, outputdir):
self.datadir = datadir
self.outputdir = outputdir
def writeData(self, lines, fpath):
"""Writes the given data in the format of each
data in one line.
"""
... | 3,255 | 40.74359 | 104 | py |
robust-nli | robust-nli-master/data/scripts/scitail.py | # This scripts process the Scitail dataset and writes it in the
# format of SNLI dataset.
import os
import json_lines
import argparse
class SciTailDataset(object):
def __init__(self, datadir, outputdir):
self.datadir = datadir
self.outputdir = outputdir
# Creates the output directory if d... | 2,217 | 36.59322 | 88 | py |
robust-nli | robust-nli-master/data/scripts/download_glue.py | # The codes are adapted from https://raw.githubusercontent.com/nyu-mll/jiant/master/scripts/download_glue_data.py
import os
import sys
import shutil
import argparse
import tempfile
import urllib.request
import zipfile
TASKS = ["MNLI", "SNLI"]
TASK2PATH = {
"MNLI":'https://firebasestorage.googleapis.com/v0/b/mtl-se... | 1,366 | 34.973684 | 169 | py |
robust-nli | robust-nli-master/data/scripts/joci.py | import csv
import random
import os
import argparse
class JOCIDataset(object):
def __init__(self, datadir, outputdir):
self.datadir = datadir
self.outputdir = outputdir
# Creates the output directory if does not exist.
if not os.path.exists(outputdir):
os.makedirs(outputd... | 2,952 | 33.337209 | 97 | py |
robust-nli | robust-nli-master/data/scripts/add_one_rte.py | import os
import argparse
class AddOneRTEDataset(object):
def __init__(self, datadir, outputdir):
self.datadir = datadir
self.outputdir = outputdir
# Creates the output directory if does not exist.
if not os.path.exists(outputdir):
os.makedirs(outputdir)
def writeDa... | 3,144 | 36.891566 | 105 | py |
robust-nli | robust-nli-master/data/scripts/sick.py | # This scripts process the SICK-E dataset and
# writes it in the format of SNLI dataset.
import os
import pandas as pd
import argparse
class SickDataset(object):
def __init__(self, datadir, outputdir):
self.datadir = datadir
self.outputdir = outputdir
# Creates the output directory if doe... | 2,346 | 40.175439 | 97 | py |
robust-nli | robust-nli-master/data/scripts/qqp.py | import csv
import random
import os
import argparse
import numpy as np
import csv
class QQPDataset(object):
def __init__(self, datadir, outputdir):
self.datadir = datadir
self.outputdir = outputdir
if not os.path.exists(outputdir):
os.makedirs(outputdir)
self.label_dict =... | 2,200 | 34.5 | 91 | py |
robust-nli | robust-nli-master/data/scripts/hans.py | import csv
import sys
from os.path import join
import os
import argparse
def read_tsv(input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r", encoding="utf-8-sig") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for li... | 1,703 | 31.769231 | 68 | py |
robust-nli | robust-nli-master/data/scripts/mpe.py | import csv
import random
import os
import argparse
import pandas as pd
import numpy as np
class MPEDataset(object):
def __init__(self, datadir, outputdir):
self.datadir = datadir
self.outputdir = outputdir
if not os.path.exists(outputdir):
os.makedirs(outputdir)
def writeDa... | 2,653 | 39.212121 | 115 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/main.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 4,678 | 54.702381 | 133 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/run_pre.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 1,079 | 29 | 75 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/run_meta.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 1,455 | 37.315789 | 110 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/trainer/__init__.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 380 | 37.1 | 75 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/trainer/meta.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/Sha-Lab/FEAT
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the r... | 13,374 | 44.493197 | 143 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/trainer/pre.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 9,314 | 42.528037 | 139 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/models/mtl.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 5,292 | 38.796992 | 115 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/models/conv2d_mtl.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/pytorch/pytorch
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in th... | 4,195 | 40.137255 | 94 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/models/resnet_mtl.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/Sha-Lab/FEAT
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the r... | 6,842 | 30.246575 | 90 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/models/__init__.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 380 | 37.1 | 75 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/utils/misc.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/Sha-Lab/FEAT
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the r... | 2,219 | 24.227273 | 75 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/utils/gpu_tools.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 547 | 31.235294 | 75 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/utils/__init__.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 380 | 37.1 | 75 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/dataloader/dataset_loader.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/Sha-Lab/FEAT
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the r... | 3,153 | 37.463415 | 125 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/dataloader/__init__.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 380 | 37.1 | 75 | py |
meta-transfer-learning | meta-transfer-learning-main/pytorch/dataloader/samplers.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/Sha-Lab/FEAT
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the r... | 1,381 | 32.707317 | 75 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/main.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 7,702 | 51.401361 | 140 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/run_experiment.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 5,436 | 43.203252 | 134 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/trainer/__init__.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 380 | 37.1 | 75 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/trainer/meta.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/cbfinn/maml
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the ro... | 16,927 | 51.571429 | 138 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/trainer/pre.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/cbfinn/maml
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the ro... | 3,972 | 39.958763 | 102 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/models/resnet18.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/cbfinn/maml
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the ro... | 17,202 | 49.89645 | 129 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/models/resnet12.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/cbfinn/maml
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the ro... | 12,316 | 50.320833 | 129 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/models/pre_model.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 3,134 | 47.230769 | 142 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/models/__init__.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 380 | 37.1 | 75 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/models/meta_model.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/cbfinn/maml
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the ro... | 12,839 | 54.107296 | 142 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/data_generator/meta_data_generator.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/cbfinn/maml
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the ro... | 7,979 | 45.941176 | 141 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/data_generator/__init__.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 380 | 37.1 | 75 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/data_generator/pre_data_generator.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/cbfinn/maml
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the ro... | 2,850 | 43.546875 | 189 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/utils/misc.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/cbfinn/maml
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the ro... | 10,805 | 33.634615 | 100 | py |
meta-transfer-learning | meta-transfer-learning-main/tensorflow/utils/__init__.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Tianjin University
## liuyaoyao@tju.edu.cn
## Copyright (c) 2019
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##++++++++++++++... | 380 | 37.1 | 75 | py |
adventures-in-ml-code | adventures-in-ml-code-master/tf_dataset_tutorial.py | import tensorflow as tf
import numpy as np
from sklearn.datasets import load_digits
def simple_dataset_with_error():
x = np.arange(0, 10)
# create dataset object from the numpy array
dx = tf.data.Dataset.from_tensor_slices(x)
# create a one-shot iterator
iterator = dx.make_one_shot_iterator()
#... | 5,550 | 39.816176 | 116 | py |
adventures-in-ml-code | adventures-in-ml-code-master/lstm_tutorial.py | import tensorflow as tf
import numpy as np
import collections
import os
import argparse
import datetime as dt
"""To run this code, you'll need to first download and extract the text dataset
from here: http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz. Change the
data_path variable below to your local... | 11,368 | 42.895753 | 117 | py |
adventures-in-ml-code | adventures-in-ml-code-master/keras_word2vec.py | from keras.models import Model
from keras.layers import Input, Dense, Reshape, merge
from keras.layers.embeddings import Embedding
from keras.preprocessing.sequence import skipgrams
from keras.preprocessing import sequence
import urllib
import collections
import os
import zipfile
import numpy as np
import tensorflow ... | 5,397 | 34.513158 | 101 | py |
adventures-in-ml-code | adventures-in-ml-code-master/convolutional_neural_network_tutorial.py | import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
def run_cnn():
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# Python optimisation variables
learning_rate = 0.0001
epochs = 10
batch_size = 50
# declare the training data p... | 5,605 | 46.508475 | 120 | py |
adventures-in-ml-code | adventures-in-ml-code-master/dueling_q_tf2_atari.py | import gym
import tensorflow as tf
from tensorflow import keras
import random
import numpy as np
import datetime as dt
import imageio
STORE_PATH = 'C:\\Users\\Andy\\TensorFlowBook\\TensorBoard'
MAX_EPSILON = 1
MIN_EPSILON = 0.1
EPSILON_MIN_ITER = 500000
GAMMA = 0.99
BATCH_SIZE = 32
TAU = 0.08
POST_PROCESS_IMAGE_SIZE =... | 8,874 | 39.711009 | 125 | py |
adventures-in-ml-code | adventures-in-ml-code-master/policy_gradient_reinforce_tf2.py | import gym
import tensorflow as tf
from tensorflow import keras
import numpy as np
import datetime as dt
STORE_PATH = '/Users/andrewthomas/Adventures in ML/TensorFlowBook/TensorBoard/PolicyGradientCartPole'
GAMMA = 0.95
env = gym.make("CartPole-v0")
state_size = 4
num_actions = env.action_space.n
network = keras.Seq... | 2,344 | 34 | 116 | py |
adventures-in-ml-code | adventures-in-ml-code-master/r_learning_tensorflow.py | import gym
import numpy as np
import tensorflow as tf
import matplotlib.pylab as plt
import random
import math
MAX_EPSILON = 1
MIN_EPSILON = 0.01
LAMBDA = 0.0001
GAMMA = 0.99
BATCH_SIZE = 50
class Model:
def __init__(self, num_states, num_actions, batch_size):
self._num_states = num_states
self._n... | 7,025 | 31.37788 | 94 | py |
adventures-in-ml-code | adventures-in-ml-code-master/sum_tree_intro.py | import numpy as np
class Node:
def __init__(self, left, right, is_leaf: bool = False, idx = None):
self.left = left
self.right = right
self.is_leaf = is_leaf
if not self.is_leaf:
self.value = self.left.value + self.right.value
self.parent = None
self.idx ... | 2,459 | 27.941176 | 111 | py |
adventures-in-ml-code | adventures-in-ml-code-master/tf_queuing.py | import tensorflow as tf
data_path = "C:\\Users\Andy\PycharmProjects\data\cifar-10-batches-bin\\"
def FIFO_queue_demo_no_coord():
# first let's create a simple random normal Tensor to act as dummy input data
# this operation should be run more than once, everytime the queue needs filling
# back up. Howev... | 10,026 | 41.487288 | 110 | py |
adventures-in-ml-code | adventures-in-ml-code-master/vanishing_gradient.py | from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
base_path = "C:\\Users\\Andy\\PycharmProjects\\Tensorboard\\"
class Model(object):
def __init__(self, input_size, label_size, activation, num_layers=6,
hidden_size=10):
self._input_size = input_size
... | 4,262 | 49.75 | 104 | py |
adventures-in-ml-code | adventures-in-ml-code-master/per_duelingq_spaceinv_tf2.py | import gym
import tensorflow as tf
from tensorflow import keras
import random
import numpy as np
import datetime as dt
import imageio
import os
# STORE_PATH = '/Users/andrewthomas/Adventures in ML/TensorFlowBook/TensorBoard'
# STORE_PATH = "tensorboard"
STORE_PATH = "C:\\Users\\Andy\\TensorFlowBook\\TensorBoard"
MAX_E... | 13,766 | 40.844985 | 165 | py |
adventures-in-ml-code | adventures-in-ml-code-master/cntk_tutorial.py | import os
os.environ['PATH'] = "C:\\Users\Andy\Anaconda2\envs\TensorFlow" + ';' + os.environ['PATH']
import cntk as C
from cntk.train import Trainer
from cntk.io import MinibatchSource, CTFDeserializer, StreamDef, StreamDefs
from cntk.learners import adadelta, learning_rate_schedule, UnitType
from cntk.ops import relu... | 3,759 | 33.495413 | 90 | py |
adventures-in-ml-code | adventures-in-ml-code-master/gensim_word2vec.py | import gensim
from gensim.models import word2vec
import logging
from keras.layers import Input, Embedding, merge
from keras.models import Model
import tensorflow as tf
import numpy as np
import urllib.request
import os
import zipfile
vector_dim = 300
root_path = "C:\\Users\Andy\PycharmProjects\\adventures-in-ml-cod... | 7,078 | 38.327778 | 120 | py |
adventures-in-ml-code | adventures-in-ml-code-master/a2c_tf2_cartpole.py | import tensorflow as tf
from tensorflow import keras
import numpy as np
import gym
import datetime as dt
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
STORE_PATH = '/Users/andrewthomas/Adventures in ML/TensorFlowBook/TensorBoard/A2CCartPole'
CRITIC_LOSS_WEIGHT = 0.5
ACTOR_LOSS_WEIGHT = 1.0
ENTROPY_LOSS_WEIGHT ... | 4,312 | 32.96063 | 118 | py |
adventures-in-ml-code | adventures-in-ml-code-master/pytorch_nn.py | import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
def simple_gradient():
# print the gradient of 2x^2 + 5x
x = Variable(torch.ones(2, 2) * 2, requires_grad=True)
z = 2 * (x * x) + ... | 3,316 | 33.915789 | 81 | py |
adventures-in-ml-code | adventures-in-ml-code-master/tensor_flow_tutorial.py | import tensorflow as tf
import numpy as np
import datetime as dt
from tensorflow.keras.datasets import mnist
STORE_PATH = '/Users/andrewthomas/Adventures in ML/TensorBoard'
def run_simple_graph():
# create TensorFlow variables
const = tf.Variable(2.0, name="const")
b = tf.Variable(2.0, name='b')
c = t... | 4,411 | 32.424242 | 103 | py |
adventures-in-ml-code | adventures-in-ml-code-master/double_q_tensorflow2.py | import gym
import tensorflow as tf
from tensorflow import keras
import random
import numpy as np
import datetime as dt
import math
STORE_PATH = '/Users/andrewthomas/Adventures in ML/TensorFlowBook/TensorBoard'
MAX_EPSILON = 1
MIN_EPSILON = 0.01
LAMBDA = 0.0005
GAMMA = 0.95
BATCH_SIZE = 32
TAU = 0.08
RANDOM_REWARD_STD ... | 4,711 | 32.41844 | 118 | py |
adventures-in-ml-code | adventures-in-ml-code-master/keras_lstm.py | from __future__ import print_function
import collections
import os
import tensorflow as tf
from keras.models import Sequential, load_model
from keras.layers import Dense, Activation, Embedding, Dropout, TimeDistributed
from keras.layers import LSTM
from keras.optimizers import Adam
from keras.utils import to_categorica... | 7,148 | 39.619318 | 109 | py |
adventures-in-ml-code | adventures-in-ml-code-master/dueling_q_tensorflow2.py | import gym
import tensorflow as tf
from tensorflow import keras
import random
import numpy as np
import datetime as dt
import math
STORE_PATH = '/Users/andrewthomas/Adventures in ML/TensorFlowBook/TensorBoard'
MAX_EPSILON = 1
MIN_EPSILON = 0.01
EPSILON_MIN_ITER = 5000
DELAY_TRAINING = 300
GAMMA = 0.95
BATCH_SIZE = 32
... | 6,519 | 35.629213 | 118 | py |
adventures-in-ml-code | adventures-in-ml-code-master/tf_visualization.py | import tensorflow as tf
import numpy as np
from tensorflow.keras.datasets import mnist
STORE_PATH = 'C:\\Users\\Andy\\TensorFlowBook\\TensorBoard'
def get_batch(x_data, y_data, batch_size):
idxs = np.random.randint(0, len(y_data), batch_size)
return x_data[idxs,:,:], y_data[idxs]
def nn_example():
(x_tra... | 4,100 | 44.065934 | 113 | py |
adventures-in-ml-code | adventures-in-ml-code-master/ppo_tf2_cartpole.py | import tensorflow as tf
from tensorflow import keras
import tensorflow_probability as tfp
import numpy as np
import gym
import datetime as dt
STORE_PATH = 'C:\\Users\\andre\\TensorBoard\\PPOCartpole'
CRITIC_LOSS_WEIGHT = 0.5
ENTROPY_LOSS_WEIGHT = 0.01
ENT_DISCOUNT_RATE = 0.995
BATCH_SIZE = 64
GAMMA = 0.99
CLIP_VALUE ... | 6,351 | 35.297143 | 119 | py |
adventures-in-ml-code | adventures-in-ml-code-master/keras_eager_tf_2.py | import tensorflow as tf
from tensorflow import keras
import datetime as dt
tf.enable_eager_execution()
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
# prepare training data
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(32).shuffle(10000)
train_dataset = trai... | 3,037 | 44.343284 | 128 | py |
adventures-in-ml-code | adventures-in-ml-code-master/tf_word2vec.py | import urllib.request
import collections
import math
import os
import random
import zipfile
import datetime as dt
import numpy as np
import tensorflow as tf
def maybe_download(filename, url, expected_bytes):
"""Download a file if not present, and make sure it's the right size."""
if not os.path.exists(filena... | 8,637 | 38.263636 | 123 | py |
adventures-in-ml-code | adventures-in-ml-code-master/r_learning_python.py | import gym
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, InputLayer
import matplotlib.pylab as plt
env = gym.make('NChain-v0')
def naive_sum_reward_agent(env, num_episodes=500):
# this is the table that will hold our summated rewards for
# each action in each state
... | 4,424 | 32.522727 | 94 | py |
adventures-in-ml-code | adventures-in-ml-code-master/conv_net_py_torch.py | import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
import torchvision.datasets
from bokeh.plotting import figure
from bokeh.io import show
from bokeh.models import LinearAxis, Range1d
import numpy as np
# Hyperparameters
num_epochs = 6
num_classes = ... | 3,793 | 32.575221 | 102 | py |
adventures-in-ml-code | adventures-in-ml-code-master/keras_cnn.py | from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.layers import Dense, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.models import Sequential
import matplotlib.pylab as plt
batch_size = 128
num_classes = 10
epochs = 10
# input image dimensions
img_x, img... | 2,477 | 31.181818 | 96 | py |
adventures-in-ml-code | adventures-in-ml-code-master/neural_network_tutorial.py | from sklearn.datasets import load_digits
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np
import numpy.random as r
import matplotlib.pyplot as plt
def convert_y_to_vect(y):
y_vect = np.zeros((len(y),... | 4,528 | 31.582734 | 99 | py |
adventures-in-ml-code | adventures-in-ml-code-master/weight_init_tensorflow.py | import tensorflow as tf
import os
from tensorflow.examples.tutorials.mnist import input_data
from functools import partial
base_path = "C:\\Users\\Andy\\PycharmProjects\\Tensorboard\\weights\\"
def maybe_create_folder_structure(sub_folders):
for fold in sub_folders:
if not os.path.isdir(base_path + fold):... | 6,524 | 51.620968 | 110 | py |
ModProp | ModProp-main/setup.py | """
The Clear BSD License
Copyright (c) 2019 the LSNN team, institute for theoretical computer science, TU Graz
All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted (subject to the limitations in the disclaimer below) provided that the following condition... | 3,706 | 53.514706 | 844 | py |
ModProp | ModProp-main/bin/rewiring_tools_NP2.py | """
Code modified to enable dense connectivity when Dale's law is enforced
and for type-specific approximation of feedback weights for ModProp
gradient approximation via automatic differentiation
Modified from https://github.com/IGITUGraz/LSNN-official
with the following copyright message retained from ... | 29,033 | 46.286645 | 844 | py |
ModProp | ModProp-main/bin/neuron_models.py | """
Code modified for rate-based neurons (ReLU activation) and for activation derivative
approximation (for nonlocal terms only) for ModProp via automatic differentiation
The overall ModProp framework proposed is "communicating the credit information
via cell-type-specific neuromodulators and processing it at... | 35,594 | 44.634615 | 844 | py |
ModProp | ModProp-main/bin/delayedXOR_task.py | '''
Code adapted for training a RNN using ModProp to perform a delayed XOR task.
The overall ModProp framework proposed is "communicating the credit information
via cell-type-specific neuromodulators and processing it at the receiving cells
via pre-determined temporal filtering taps."
Remarks:
- If yo... | 24,338 | 45.715931 | 844 | py |
ModProp | ModProp-main/bin/plot_curves.py | # -*- coding: utf-8 -*-
"""
Code for plotting the learning curves of saved runs
"""
import sys
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from file_saver_dumper_no_h5py import save_file, load_file, get_storage_path_reference
import json
import os
## Setup
SMALL_SIZE = 12
MEDIUM_SIZE =... | 3,600 | 33.961165 | 121 | py |
ModProp | ModProp-main/lsnn/spiking_models.py | """
The Clear BSD License
Copyright (c) 2019 the LSNN team, institute for theoretical computer science, TU Graz
All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted (subject to the limitations in the disclaimer below) provided that the following condition... | 21,033 | 39.922179 | 844 | py |
ModProp | ModProp-main/lsnn/__init__.py | 0 | 0 | 0 | py |
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