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 |
|---|---|---|---|---|---|---|
baconian-project | baconian-project-master/baconian/algo/dynamics/__init__.py | 0 | 0 | 0 | py | |
baconian-project | baconian-project-master/baconian/algo/dynamics/gaussian_process_dynamiocs_model.py | from baconian.core.core import EnvSpec
from baconian.algo.dynamics.dynamics_model import TrainableDyanmicsModel, LocalDyanmicsModel
import gpflow
import numpy as np
from baconian.common.sampler.sample_data import TransitionData
from baconian.algo.dynamics.third_party.mgpr import MGPR
import tensorflow as tf
from baconi... | 3,282 | 48 | 117 | py |
baconian-project | baconian-project-master/baconian/algo/dynamics/third_party/mgpr.py | """
Code from https://github.com/nrontsis/PILCO
"""
import tensorflow as tf
import gpflow
import numpy as np
from typeguard import typechecked
float_type = gpflow.settings.dtypes.float_type
def randomize(model):
mean = 1
sigma = 0.01
model.kern.lengthscales.assign(
mean + sigma * np.random.norm... | 7,423 | 37.071795 | 105 | py |
baconian-project | baconian-project-master/baconian/algo/dynamics/third_party/gmm.py | """ This file defines a Gaussian mixture model class. """
import logging
import numpy as np
import scipy.linalg
from copy import deepcopy
LOGGER = logging.getLogger(__name__)
def logsum(vec, axis=0, keepdims=True):
# TODO: Add a docstring.
maxv = np.max(vec, axis=axis, keepdims=keepdims)
maxv[maxv == -f... | 7,345 | 33.650943 | 104 | py |
baconian-project | baconian-project-master/baconian/algo/dynamics/third_party/__init__.py | # Date: 3/30/19
# Author: Luke
# Project: baconian-internal | 59 | 19 | 28 | py |
baconian-project | baconian-project-master/baconian/algo/dynamics/reward_func/__init__.py | 0 | 0 | 0 | py | |
baconian-project | baconian-project-master/baconian/algo/dynamics/reward_func/reward_func.py | from baconian.core.core import Basic
import abc
import numpy as np
class RewardFunc(Basic):
allow_duplicate_name = True
def __init__(self, name='reward_func'):
super().__init__(name=name)
@abc.abstractmethod
def __call__(self, state, action, new_state, **kwargs) -> float:
raise NotIm... | 2,146 | 27.25 | 98 | py |
baconian-project | baconian-project-master/baconian/algo/dynamics/terminal_func/terminal_func.py | from baconian.core.core import Basic
import abc
import numpy as np
class TerminalFunc(Basic):
allow_duplicate_name = True
def __init__(self, name='terminal_func'):
super().__init__(name=name)
@abc.abstractmethod
def __call__(self, state, action, new_state, **kwargs) -> bool:
raise No... | 1,189 | 25.444444 | 82 | py |
baconian-project | baconian-project-master/baconian/algo/dynamics/terminal_func/__init__.py | 0 | 0 | 0 | py | |
baconian-project | baconian-project-master/baconian/algo/distribution/mvn.py | """
Module that compute diagonal multivariate normal distribution operation with tensorflow tensor as parameters
"""
import tensorflow as tf
import numpy as np
def kl(mean_p, var_p, mean_q, var_q, dims):
"""
Compute the KL divergence of diagonal multivariate normal distribution q, and p, which is KL(P||Q)
... | 1,439 | 29.638298 | 114 | py |
baconian-project | baconian-project-master/baconian/algo/distribution/__init__.py | 0 | 0 | 0 | py | |
baconian-project | baconian-project-master/baconian/algo/value_func/mlp_q_value.py | import typeguard as tg
from baconian.core.core import EnvSpec
import overrides
import tensorflow as tf
from baconian.tf.tf_parameters import ParametersWithTensorflowVariable
from baconian.tf.mlp import MLP
from baconian.common.special import *
from baconian.core.util import init_func_arg_record_decorator
from baconian.... | 5,335 | 41.349206 | 100 | py |
baconian-project | baconian-project-master/baconian/algo/value_func/__init__.py | from .value_func import ValueFunction, QValueFunction, VValueFunction
from .mlp_q_value import MLPQValueFunction
from .mlp_v_value import MLPVValueFunc
| 152 | 37.25 | 69 | py |
baconian-project | baconian-project-master/baconian/algo/value_func/value_func.py | from baconian.core.core import Basic, EnvSpec
import typeguard as tg
from baconian.core.parameters import Parameters
import abc
class ValueFunction(Basic):
@tg.typechecked
def __init__(self, env_spec: EnvSpec, parameters: Parameters = None, name='value_func'):
super().__init__(name)
self.env_... | 1,733 | 29.421053 | 119 | py |
baconian-project | baconian-project-master/baconian/algo/value_func/mlp_v_value.py | import typeguard as tg
from baconian.core.core import EnvSpec
import overrides
import tensorflow as tf
from baconian.tf.tf_parameters import ParametersWithTensorflowVariable
from baconian.tf.mlp import MLP
from baconian.common.special import *
from baconian.algo.utils import _get_copy_arg_with_tf_reuse
from baconian.al... | 4,350 | 37.848214 | 91 | py |
baconian-project | baconian-project-master/baconian/algo/misc/sample_processor.py | from baconian.common.sampler.sample_data import TransitionData, TrajectoryData
from baconian.algo.value_func import ValueFunction
import scipy.signal
from baconian.common.special import *
def discount(x, gamma):
"""code clip from pat-cody"""
""" Calculate discounted forward sum of a sequence at each point """... | 3,808 | 47.21519 | 120 | py |
baconian-project | baconian-project-master/baconian/algo/misc/epsilon_greedy.py | from baconian.common.spaces.base import Space
import numpy as np
from typeguard import typechecked
from baconian.core.parameters import Parameters
from baconian.common.schedules import Scheduler
class ExplorationStrategy(object):
def __init__(self):
self.parameters = None
def predict(self, **kwargs):... | 1,158 | 32.114286 | 103 | py |
baconian-project | baconian-project-master/baconian/algo/misc/__init__.py | from .replay_buffer import BaseReplayBuffer, UniformRandomReplayBuffer
from .epsilon_greedy import ExplorationStrategy, EpsilonGreedy
from .placeholder_input import PlaceholderInput, MultiPlaceholderInput
from .sample_processor import SampleProcessor
| 251 | 49.4 | 70 | py |
baconian-project | baconian-project-master/baconian/algo/misc/replay_buffer.py | import numpy as np
from typeguard import typechecked
from baconian.common.sampler.sample_data import TransitionData, TrajectoryData, SampleData
from baconian.common.error import *
class RingBuffer(object):
@typechecked
def __init__(self, maxlen: int, shape: (list, tuple), dtype='float32'):
self.maxlen... | 6,768 | 35.989071 | 102 | py |
baconian-project | baconian-project-master/baconian/algo/misc/placeholder_input.py | import tensorflow as tf
import typeguard as tg
import os
from baconian.common.logging import ConsoleLogger
from baconian.tf.tf_parameters import ParametersWithTensorflowVariable
from baconian.core.core import Basic
from baconian.config.global_config import GlobalConfig
class PlaceholderInput(object):
@tg.typeche... | 4,758 | 48.061856 | 116 | py |
baconian-project | baconian-project-master/baconian/tf/tensor_utils.py | from collections import Iterable
from collections import namedtuple
import numpy as np
import tensorflow as tf
def compile_function(inputs, outputs, log_name=None):
def run(*input_vals):
sess = tf.get_default_session()
return sess.run(outputs, feed_dict=dict(list(zip(inputs, input_vals))))
r... | 7,669 | 31.362869 | 79 | py |
baconian-project | baconian-project-master/baconian/tf/tf_parameters.py | import tensorflow as tf
from baconian.core.parameters import Parameters
from baconian.config.global_config import GlobalConfig
from overrides.overrides import overrides
from typeguard import typechecked
import os
from baconian.common.schedules import Scheduler
import numpy as np
class ParametersWithTensorflowVariable... | 9,734 | 44.27907 | 117 | py |
baconian-project | baconian-project-master/baconian/tf/mlp.py | from typeguard import typechecked
from baconian.tf.util import MLPCreator
import tensorflow as tf
import numpy as np
from baconian.tf.tf_parameters import ParametersWithTensorflowVariable
class MLP(object):
@typechecked
def __init__(self,
input_ph: tf.Tensor,
name_scope: str... | 2,861 | 46.7 | 114 | py |
baconian-project | baconian-project-master/baconian/tf/util.py | import os
import tensorflow as tf
from tensorflow.contrib.layers import variance_scaling_initializer as contrib_W_init
from typeguard import typechecked
import collections
import multiprocessing
import tensorflow.contrib as tf_contrib
from baconian.common.error import *
__all__ = ['get_tf_collection_var_list', 'MLPCr... | 6,416 | 40.941176 | 132 | py |
baconian-project | baconian-project-master/baconian/tf/__init__.py | 0 | 0 | 0 | py | |
baconian-project | baconian-project-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 6,083 | 28.970443 | 101 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/run_slot_filling_evaluation.py | # coding:utf-8
import argparse
import json
from source.Evaluate.slot_filling import prepare_data_to_dukehan, prepare_data_to_conll_format
from source.Evaluate.slot_filling_data_processor import cook_slot_filling_data
from set_config import refresh_config_file
import copy
import subprocess
# ============ Args Process =... | 4,041 | 47.119048 | 188 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/run_clustering.py | # coding:utf-8
from source.Cluster import clustering
from source.Cluster import conll_format_clustering
# from source.Cluster.clustering import slot_clustering_and_dump_dict
import argparse
import json
from set_config import refresh_config_file
# ============ Args Process ==========
parser = argparse.ArgumentParser()... | 1,821 | 38.608696 | 144 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/run_onmt_generation.py | # coding:utf-8
import json
import os
import copy
import subprocess
import argparse
from source.AuxiliaryTools.nlp_tool import low_case_tokenizer, sentence_edit_distance
from source.ReFilling.re_filling import re_filling
import math
from collections import Counter
import random
from itertools import combinations
from se... | 14,067 | 48.886525 | 200 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/run_thesaurus.py | # coding: utf-8
import json
import argparse
from source.AuxiliaryTools.nlp_tool import low_case_tokenizer, sentence_edit_distance
from source.ReFilling.re_filling import re_filling
from set_config import refresh_config_file
# ============ Description ==========
# refill source file to test refill only
# ============ ... | 2,090 | 44.456522 | 189 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/run_aug_baseline_slot_filling_for.py | # coding:utf-8
import argparse
import json
from source.Evaluate.slot_filling import prepare_data_to_dukehan, prepare_data_to_conll_format
from source.Evaluate.slot_filling_data_processor import cook_slot_filling_data
from set_config import refresh_config_file
import copy
import subprocess
# ============ Args Process =... | 4,786 | 59.594937 | 188 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/run_gen_with_label.py | # coding:utf-8
import json
import os
import copy
import subprocess
import argparse
from source.AuxiliaryTools.nlp_tool import low_case_tokenizer, sentence_edit_distance
from source.ReFilling.re_filling import re_filling
import math
from collections import Counter
import random
from itertools import combinations
from se... | 8,920 | 46.452128 | 128 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/set_config.py | # coding:utf-8
from source.AuxiliaryTools.ConfigTool import update_config
def refresh_config_file(config_path='./config.json'):
print('Config Position:', config_path)
# For my linux server setting
update_config("/users4/ythou/Projects/TaskOrientedDialogue/data/", config_path=config_path)
# For my wind... | 486 | 33.785714 | 99 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/run_gen_evaluation.py | # coding: utf-8
import argparse
import json
from source.Evaluate.gen_eval import appearance_check
from set_config import refresh_config_file
import copy
import subprocess
from multiprocessing import Process, Queue, current_process, freeze_support, Manager
N_THREAD = 20
# ============ Args Process ==========
parser = ... | 5,770 | 35.99359 | 177 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/setup.py | #!/usr/bin/env python
from setuptools import setup
setup(name='OpenNMT',
description='A python implementation of OpenNMT',
version='0.1',
packages=['onmt', 'onmt.modules'])
| 193 | 20.555556 | 55 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/opts.py | import argparse
from onmt.modules.SRU import CheckSRU
def model_opts(parser):
"""
These options are passed to the construction of the model.
Be careful with these as they will be used during translation.
"""
# Model options
parser.add_argument('-model_type', default='text',
... | 17,059 | 48.449275 | 84 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/translate.py | #!/usr/bin/env python
from __future__ import division
from builtins import bytes
import os
import argparse
import math
import codecs
import torch
import onmt
import onmt.IO
import opts
from itertools import takewhile, count
try:
from itertools import zip_longest
except ImportError:
from itertools import izip_... | 4,516 | 32.708955 | 79 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/train.py | #!/usr/bin/env python
from __future__ import division
import os
import sys
import argparse
import torch
import torch.nn as nn
from torch import cuda
import onmt
import onmt.Models
import onmt.ModelConstructor
import onmt.modules
from onmt.Utils import aeq, use_gpu
import opts
parser = argparse.ArgumentParser(
d... | 10,352 | 31.556604 | 120 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/preprocess.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
import codecs
import torch
import onmt
import onmt.IO
import opts
parser = argparse.ArgumentParser(
description='preprocess.py',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
opts.add_md_help_argument(parser)
# **Preprocess Options**
p... | 3,411 | 34.915789 | 77 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/tools/extract_embeddings.py | from __future__ import division
import torch
import argparse
from onmt.ModelConstructor import make_embeddings, \
make_encoder, make_decoder
parser = argparse.ArgumentParser(description='translate.py')
parser.add_argument('-model', required=True,
help='Path to model .... | 1,987 | 30.0625 | 70 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/test/test_simple.py | import onmt
def test_load():
onmt
pass
| 49 | 6.142857 | 16 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/test/test_models.py | import argparse
import copy
import unittest
import torch
from torch.autograd import Variable
import onmt
import opts
from onmt.ModelConstructor import make_embeddings, \
make_encoder, make_decoder
parser = argparse.ArgumentParser(description='train.py')
opts.model_opts(parser)
opts.train_... | 7,275 | 32.84186 | 79 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/test/test_preprocess.py | import argparse
import copy
import unittest
import onmt
import opts
import torchtext
from collections import Counter
parser = argparse.ArgumentParser(description='preprocess.py')
opts.preprocess_opts(parser)
opt = parser.parse_known_args()[0]
opt.train_src = 'data/src-train.txt'
opt.train_tgt = 'data/tgt-train.txt... | 3,317 | 30.009346 | 79 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/test/__init__.py | 0 | 0 | 0 | py | |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/test/test_attention.py | """
Here come the tests for attention types and their compatibility
"""
| 72 | 17.25 | 63 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/Loss.py | """
This file handles the details of the loss function during training.
This includes: LossComputeBase and the standard NMTLossCompute, and
sharded loss compute stuff.
"""
from __future__ import division
import torch
import torch.nn as nn
from torch.autograd import Variable
import onmt
class LossComp... | 6,611 | 34.548387 | 78 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/Beam.py | from __future__ import division
import torch
import onmt
"""
Class for managing the internals of the beam search process.
Takes care of beams, back pointers, and scores.
"""
class Beam(object):
def __init__(self, size, n_best=1, cuda=False, vocab=None,
global_scorer=None):
self.size ... | 5,428 | 32.512346 | 77 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/Translator.py | import torch
from torch.autograd import Variable
import onmt
import onmt.Models
import onmt.ModelConstructor
import onmt.modules
import onmt.IO
from onmt.Utils import use_gpu
NOISE_TRANSELATE = False
class Translator(object):
def __init__(self, opt, dummy_opt={}):
# Add in default model arguments, poss... | 8,875 | 36.610169 | 122 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/Utils.py | def aeq(*args):
"""
Assert all arguments have the same value
"""
arguments = (arg for arg in args)
first = next(arguments)
assert all(arg == first for arg in arguments), \
"Not all arguments have the same value: " + str(args)
def use_gpu(opt):
return (hasattr(opt, 'gpuid') and len(... | 388 | 26.785714 | 62 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/IO.py | # -*- coding: utf-8 -*-
import codecs
from collections import Counter, defaultdict
from itertools import chain, count
import torch
import torchtext.data
import torchtext.vocab
PAD_WORD = '<blank>'
UNK = 0
BOS_WORD = '<s>'
EOS_WORD = '</s>'
def __getstate__(self):
return dict(self.__dict__, stoi=dict(self.stoi)... | 14,171 | 33.231884 | 78 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/ModelConstructor.py | """
This file is for models creation, which consults options
and creates each encoder and decoder accordingly.
"""
import torch.nn as nn
import onmt
import onmt.Models
import onmt.modules
from onmt.Models import NMTModel, MeanEncoder, RNNEncoder, \
StdRNNDecoder, InputFeedRNNDecoder
from onmt.m... | 7,331 | 37.589474 | 76 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/__init__.py | import onmt.IO
import onmt.Models
import onmt.Loss
from onmt.Trainer import Trainer, Statistics
from onmt.Translator import Translator
from onmt.Optim import Optim
from onmt.Beam import Beam, GNMTGlobalScorer
# For flake8 compatibility
__all__ = [onmt.Loss, onmt.IO, onmt.Models, Trainer, Translator,
Optim,... | 357 | 26.538462 | 64 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/Trainer.py | from __future__ import division
"""
This is the loadable seq2seq trainer library that is
in charge of training details, loss compute, and statistics.
See train.py for a use case of this library.
Note!!! To make this a general library, we implement *only*
mechanism things here(i.e. what to do), and leave the strategy
t... | 6,823 | 33.994872 | 78 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/Optim.py | import torch.optim as optim
from torch.nn.utils import clip_grad_norm
class Optim(object):
def set_parameters(self, params):
self.params = [p for p in params if p.requires_grad]
if self.method == 'sgd':
self.optimizer = optim.SGD(self.params, lr=self.lr)
elif self.method == 'a... | 2,490 | 33.123288 | 76 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/Models.py | from __future__ import division
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence as pack
from torch.nn.utils.rnn import pad_packed_sequence as unpack
import onmt
from onmt.Utils import aeq
class EncoderBase(nn.Module):
"""
EncoderBase ... | 18,492 | 36.209256 | 79 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/ConvMultiStepAttention.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.Utils import aeq
SCALE_WEIGHT = 0.5 ** 0.5
def seq_linear(linear, x):
# linear transform for 3-d tensor
batch, hidden_size, length, _ = x.size()
h = linear(torch.transpose(x, 1, 2).contiguous().view(
batch * length, hid... | 2,610 | 34.767123 | 77 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/Transformer.py | """
Implementation of "Attention is All You Need"
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import onmt
from onmt.Models import EncoderBase
from onmt.Models import DecoderState
from onmt.Utils import aeq
MAX_SIZE = 5000
class PositionwiseFeedForward(nn.Module):
... | 11,553 | 36.391586 | 79 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/Embeddings.py | import torch
import torch.nn as nn
from torch.autograd import Variable
from onmt.modules import BottleLinear, Elementwise
from onmt.Utils import aeq
class PositionalEncoding(nn.Module):
def __init__(self, dropout, dim, max_len=5000):
pe = torch.arange(0, max_len).unsqueeze(1).expand(max_len, dim)
... | 5,928 | 39.609589 | 77 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/CopyGenerator.py | import torch.nn as nn
import torch.nn.functional as F
import torch
import torch.cuda
import onmt
from onmt.Utils import aeq
class CopyGenerator(nn.Module):
"""
Generator module that additionally considers copying
words directly from the source.
"""
def __init__(self, opt, src_dict, tgt_dict):
... | 5,090 | 34.852113 | 78 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/StackedRNN.py | import torch
import torch.nn as nn
class StackedLSTM(nn.Module):
"""
Our own implementation of stacked LSTM.
Needed for the decoder, because we do input feeding.
"""
def __init__(self, num_layers, input_size, rnn_size, dropout):
super(StackedLSTM, self).__init__()
self.dropout = nn... | 1,755 | 28.266667 | 66 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/MultiHeadedAttn.py | import math
import torch
import torch.nn as nn
from torch.autograd import Variable
from onmt.Utils import aeq
from onmt.modules.UtilClass import BottleLinear, \
BottleLayerNorm, BottleSoftmax
class MultiHeadedAttention(nn.Module):
''' Multi-Head Attention module from
"Attention is All... | 3,966 | 34.738739 | 74 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/Gate.py | """
Context gate is a decoder module that takes as input the previous word
embedding, the current decoder state and the attention state, and produces a
gate.
The gate can be used to select the input from the target side context
(decoder state), from the source context (attention state) or both.
"""
import torch
import ... | 3,596 | 38.527473 | 78 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/UtilClass.py | import torch
import torch.nn as nn
class Bottle(nn.Module):
def forward(self, input):
if len(input.size()) <= 2:
return super(Bottle, self).forward(input)
size = input.size()[:2]
out = super(Bottle, self).forward(input.view(size[0]*size[1], -1))
... | 2,769 | 30.123596 | 78 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/StructuredAttention.py | import torch.nn as nn
import torch
import torch.cuda
from torch.autograd import Variable
class MatrixTree(nn.Module):
"""Implementation of the matrix-tree theorem for computing marginals
of non-projective dependency parsing. This attention layer is used
in the paper "Learning Structured Text Representatio... | 1,556 | 33.6 | 77 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/Conv2Conv.py | """
Implementation of "Convolutional Sequence to Sequence Learning"
"""
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Variable
import onmt.modules
from onmt.modules.WeightNorm import WeightNormConv2d
from onmt.Models import EncoderBase
from o... | 8,557 | 35.57265 | 79 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/GlobalAttention.py | import torch
import torch.nn as nn
from onmt.modules.UtilClass import BottleLinear
from onmt.Utils import aeq
class GlobalAttention(nn.Module):
"""
Luong Attention.
Global attention takes a matrix and a query vector. It
then computes a parameterized convex combination of the matrix
based on the ... | 6,419 | 32.968254 | 79 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/SRU.py | """
Implementation of "Training RNNs as Fast as CNNs".
TODO: turn to pytorch's implementation when it is available.
This implementation is adpoted from the author of the paper:
https://github.com/taolei87/sru/blob/master/cuda_functional.py.
"""
import subprocess
import platform
import os
import re
import argparse
impo... | 23,318 | 36.672052 | 79 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/WeightNorm.py | """
Implementation of "Weight Normalization: A Simple Reparameterization
to Accelerate Training of Deep Neural Networks"
As a reparameterization method, weight normalization is same
as BatchNormalization, but it doesn't depend on minibatch.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from tor... | 9,574 | 39.231092 | 78 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/__init__.py | from onmt.modules.UtilClass import LayerNorm, Bottle, BottleLinear, \
BottleLayerNorm, BottleSoftmax, Elementwise
from onmt.modules.Gate import ContextGateFactory
from onmt.modules.GlobalAttention import GlobalAttention
from onmt.modules.ConvMultiStepAttention import ConvMultiStepAttention
from onmt.modules.ImageEn... | 1,489 | 45.5625 | 79 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/OpenNMT/onmt/modules/ImageEncoder.py | import torch.nn as nn
import torch.nn.functional as F
import torch
import torch.cuda
from torch.autograd import Variable
class ImageEncoder(nn.Module):
"""
Encoder recurrent neural network for Images.
"""
def __init__(self, num_layers, bidirectional, rnn_size, dropout):
"""
Args:
... | 3,998 | 36.373832 | 76 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/__init__.py | 0 | 0 | 0 | py | |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/Cluster/clustering.py | # coding:utf-8
"""
main code for clustering
define clustering class
and running this code to get clustering for stanford data
"""
import json
from source.AuxiliaryTools.nlp_tool import low_case_tokenizer
all_file = ['dev.json', 'test.json', 'train.json']
all_task = ["navigate", "schedule", "weather"]
class Cluster:... | 8,064 | 43.558011 | 121 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/Cluster/conll_format_clustering.py | # coding:utf-8
"""
main code for clustering
define clustering class
and running this code to get clustering for stanford data
"""
import json
import os
from source.AuxiliaryTools.nlp_tool import low_case_tokenizer
CONTEXT_WINDOW_SIZE = 2 # 2 is used because it is empirical feature setting in slot filling task
SENT_C... | 19,236 | 48.836788 | 133 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/Cluster/__init__.py | 0 | 0 | 0 | py | |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/Cluster/atis_clustering.py | # coding:utf-8
from source.Cluster.clustering import Cluster
# wait to construct
class AtisCluster(Cluster):
def __init__(self, input_dir, output_dir):
Cluster.__init__(self, input_dir, output_dir)
def unpack_and_cook_raw_data(self, raw_data):
pass
if __name__ == "__main__":
print("Hi, ... | 330 | 19.6875 | 53 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/Generation/seq2seq_translation_tutorial.py | # -*- coding: utf-8 -*-
"""
Translation with a Sequence to Sequence Network and Attention
*************************************************************
**Author**: `Sean Robertson <https://github.com/spro/practical-pytorch>`_
In this project we will be teaching a neural network to translate from
French to English.
::... | 31,375 | 33.939866 | 133 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/Generation/PrepareData.py | # coding: utf-8
from source.AuxiliaryTools.nlp_tool import low_case_tokenizer
from itertools import combinations
import pickle
import json
import os
SOS_token = 0
EOS_token = 1
class WordTable:
def __init__(self, name):
self.name = name
self.word2index = {}
self.word2count = {}
se... | 4,127 | 33.689076 | 117 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/Generation/Evaluation.py | # coding: utf-8
######################################################################
# Evaluation
# ==========
#
# Evaluation is mostly the same as training, but there are no targets so
# we simply feed the decoder's predictions back to itself for each step.
# Every time it predicts a word we add it to the output str... | 4,187 | 34.794872 | 116 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/Generation/Seq2SeqModel.py | # coding: utf-8
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
class EncoderRNN(nn.Module):
def __init__(self, input_size, hidden_size, use_cuda, n_layers=1):
super(EncoderRNN, self).__init__()
self.n_layers = n_layers
self.hidden_siz... | 3,661 | 34.901961 | 112 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/Generation/Training.py | import time
import math
import random
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
from source.AuxiliaryTools.nn_tool import show_plot, variables_from_pair
SOS_token = 0
EOS_token = 1
teacher_forcing_ratio = 0.5
def train(input_variable, target_variable, encoder, de... | 4,278 | 33.788618 | 109 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/Generation/__init__.py | 0 | 0 | 0 | py | |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/ReFilling/re_filling.py | # coding:utf-8
import re
import json
from source.AuxiliaryTools.nlp_tool import sentence_edit_distance
import random
from multiprocessing import Process, Queue, current_process, freeze_support, Manager
import copy
N_THREAD = 20
TASK_SIZE = 500
CONTEXT_WINDOW_SIZE = 2
FULL_SLOT_TABLE_AVAILABLE = True
CONTEXT_REFILL_RATE... | 14,997 | 44.72561 | 173 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/ReFilling/__init__.py | 0 | 0 | 0 | py | |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/Evaluate/gen_eval.py | import json
import argparse
import random
import re
# from nlp_tool import sentence_edit_distance # this is worked out of pycharm
from source.AuxiliaryTools.nlp_tool import sentence_edit_distance
# # ==== load config =====
# with open('../../config.json', 'r') as con_f:
# CONFIG = json.load(con_f)
#
#
# parser =... | 6,950 | 40.622754 | 150 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/Evaluate/__init__.py | 0 | 0 | 0 | py | |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/Evaluate/slot_filling.py | # coding:utf-8
"""
Tips:
slot label format:
B-slot_name, there is no < or > in slot_name
"""
import json
import os
from source.AuxiliaryTools.nlp_tool import low_case_tokenizer
import re
# VERBOSE = True
VERBOSE = False
# DEBUG = False
DEBUG = True
SENT_COUNT_SPLIT = True
# def out_of_slot(i, j, tmp_word_lst,... | 15,138 | 46.309375 | 182 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/AuxiliaryTools/eval_tool.py | # coding: utf-8
import json
import os
import re
LOG_DIR = '../../log/'
CONFIG_PATH = '../../config.json'
with open(CONFIG_PATH, 'r') as reader:
CONFIG = json.load(reader)
RANDOM_SEED = 100
# EXTEND_SETTING = ['extend_']
EXTEND_SETTING = ['extend_', '']
RFO_SETTING = ['_refill-only', '']
TASK_SETTING = ['atis_labele... | 7,655 | 43.254335 | 780 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/AuxiliaryTools/ConfigTool.py | # coding: utf-8
import json
import os
from collections import OrderedDict
def create_dir(p_list):
new_folder_num = 0
for p in p_list:
if type(p) == dict:
new_folder_num += create_dir(p.values())
elif not os.path.isdir(p):
os.makedirs(p)
new_folder_num += 1
... | 4,972 | 60.395062 | 619 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/AuxiliaryTools/data_split.py | # coding: utf-8
DATA_FORMAT = 'conll'
INPUT_FILE = '/users4/ythou/Projects/TaskOrientedDialogue/data/AtisRaw/atis.train'
OUTPUT_DIR = '/users4/ythou/Projects/TaskOrientedDialogue/data/Atis/'
DATA_MARK = 'atis'
TRAIN_DEV_RATE = [0.8, 0.2]
TRAIN_DEV_COUNT = [None, 500]
USE_RATE = False
def split():
with open(INPUT... | 1,072 | 36 | 82 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/AuxiliaryTools/slot_leak_check.py | # coding:utf-8
import json
import os
import re
LOG_DIR = '../../log/'
CONFIG_PATH = '../../config.json'
with open(CONFIG_PATH, 'r') as reader:
CONFIG = json.load(reader)
test_slot_file_path = CONFIG['path']["ClusteringResult"] + 'test_atis_labeled_intent-slot1.json'
origin_train_file_path = CONFIG['path']['RawDat... | 1,696 | 38.465116 | 139 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/AuxiliaryTools/nn_tool.py | # coding: utf-8
from __future__ import unicode_literals, print_function, division
import torch
from torch.autograd import Variable
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
SOS_token = 0
EOS_token = 1
teacher_forcing_ratio = 0.5
MAX_LENGTH = 10
def show_plot(points):
plt.figure()
fi... | 1,083 | 26.794872 | 72 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/AuxiliaryTools/data_tool.py | # coding:utf-8
import json
import argparse
import random
import re
from nlp_tool import sentence_edit_distance # this is worked out of pycharm
# from source.AuxiliaryTools.nlp_tool import sentence_edit_distance
# ==== load config =====
with open('../../config.json', 'r') as con_f:
CONFIG = json.load(con_f)
def... | 6,819 | 43 | 143 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/AuxiliaryTools/diverse_score_demo.py | # coding:utf-8
import math
import editdistance
def sentence_edit_distance(s1, s2):
s1 = s1.split() if type(s1) is str else s1
s2 = s2.split() if type(s2) is str else s2
if type(s1) is list and type(s2) is list:
return editdistance.eval(s1, s2)
else:
print("Error: Only str and list is s... | 1,668 | 36.931818 | 92 | py |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/AuxiliaryTools/__init__.py | 0 | 0 | 0 | py | |
Seq2SeqDataAugmentationForLU | Seq2SeqDataAugmentationForLU-master/source/AuxiliaryTools/nlp_tool.py | import string
from nltk.tokenize import TweetTokenizer
from nltk.tokenize.treebank import TreebankWordTokenizer, TreebankWordDetokenizer
import editdistance
def sentence_edit_distance(s1, s2):
s1 = s1.split() if type(s1) is str else s1
s2 = s2.split() if type(s2) is str else s2
if type(s1) is list and typ... | 2,444 | 30.753247 | 133 | py |
seq2seq | seq2seq-master/setup.py | # Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,... | 910 | 26.606061 | 74 | py |
seq2seq | seq2seq-master/bin/__init__.py | 0 | 0 | 0 | py | |
seq2seq | seq2seq-master/bin/infer.py | #! /usr/bin/env python
# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | 4,428 | 33.069231 | 77 | py |
seq2seq | seq2seq-master/bin/train.py | #! /usr/bin/env python
# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | 10,770 | 37.744604 | 80 | py |
seq2seq | seq2seq-master/bin/tools/generate_beam_viz.py | #! /usr/bin/env python
# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | 3,893 | 28.5 | 79 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.