python_code stringlengths 0 187k | repo_name stringlengths 8 46 | file_path stringlengths 6 135 |
|---|---|---|
from overrides import overrides
from keras import backend as K
from keras.engine import InputSpec
from ..masked_layer import MaskedLayer
class BOWEncoder(MaskedLayer):
'''
Bag of Words Encoder takes a matrix of shape (num_words, word_dim) and returns a vector of size (word_dim),
which is an average of th... | deep_qa-master | deep_qa/layers/encoders/bag_of_words.py |
from keras import backend as K
from keras.engine import InputSpec
from overrides import overrides
from ..masked_layer import MaskedLayer
from ...tensors.backend import switch
class PositionalEncoder(MaskedLayer):
'''
A ``PositionalEncoder`` is very similar to a kind of weighted bag of words encoder,
whe... | deep_qa-master | deep_qa/layers/encoders/positional_encoder.py |
from overrides import overrides
from keras.engine import InputSpec
from keras import backend as K
from keras.layers.recurrent import GRU, _time_distributed_dense
class AttentiveGru(GRU):
"""
GRUs typically operate over sequences of words. The motivation behind this encoding is that
a weighted average los... | deep_qa-master | deep_qa/layers/encoders/attentive_gru.py |
from keras import backend as K
from keras.layers import GRU, InputSpec
class ShareableGRU(GRU):
def __init__(self, *args, **kwargs):
super(ShareableGRU, self).__init__(*args, **kwargs)
def call(self, x, mask=None, **kwargs):
input_shape = K.int_shape(x)
res = super(ShareableGRU, self)... | deep_qa-master | deep_qa/layers/encoders/shareable_gru.py |
from keras import backend as K
from keras import activations
from overrides import overrides
from .word_alignment import WordAlignmentEntailment
from ..attention import WeightedSum
from ...tensors.backend import switch, apply_feed_forward
class DecomposableAttentionEntailment(WordAlignmentEntailment):
"""
Th... | deep_qa-master | deep_qa/layers/entailment_models/decomposable_attention.py |
'''
Word alignment entailment models operate on word level representations, and define alignment
as a function of how well the words in the premise align with those in the hypothesis. These
are different from the encoded sentence entailment models where both the premise and hypothesis
are encoded as single vectors and ... | deep_qa-master | deep_qa/layers/entailment_models/word_alignment.py |
from .decomposable_attention import DecomposableAttentionEntailment
from .multiple_choice_tuple_entailment import MultipleChoiceTupleEntailment
entailment_models = { # pylint: disable=invalid-name
'decomposable_attention': DecomposableAttentionEntailment,
'multiple_choice_tuple_attention': MultipleChoi... | deep_qa-master | deep_qa/layers/entailment_models/__init__.py |
from keras import backend as K
from .word_alignment import WordAlignmentEntailment
from ...tensors.backend import switch
class MultipleChoiceTupleEntailment(WordAlignmentEntailment):
'''A kind of decomposable attention where the premise (or background) is in
the form of SVO triples, and entailment is compute... | deep_qa-master | deep_qa/layers/entailment_models/multiple_choice_tuple_entailment.py |
from typing import List, Tuple
from collections import defaultdict
import tensorflow
def pin_variable_device_scope(device, variable_device="/cpu:0"):
"""
Tensorflow device scopes can take functions which give a device
for a given op in the graph. Here, we use the device that is
passed to the scope *un... | deep_qa-master | deep_qa/training/train_utils.py |
import logging
import os
from typing import Dict, List
from overrides import overrides
from keras.models import Model, Sequential
from keras.engine.training import _batch_shuffle, _make_batches, _slice_arrays
from keras.callbacks import History, CallbackList, ProgbarLogger, BaseLogger, Callback
import keras.backend as... | deep_qa-master | deep_qa/training/models.py |
from copy import deepcopy
from typing import Any, Dict, List, Tuple
import logging
import dill as pickle
from keras import backend as K
from keras.layers import Dense, Dropout, Layer, TimeDistributed, Embedding
from overrides import overrides
import numpy
import tensorflow
from ..common.checks import ConfigurationErr... | deep_qa-master | deep_qa/training/text_trainer.py |
from .text_trainer import TextTrainer
from .trainer import Trainer
| deep_qa-master | deep_qa/training/__init__.py |
r"""
It turns out that Keras' design is somewhat crazy\*, and there is no list of
optimizers that you can just import from Keras. So, this module specifies a
list, and a helper function or two for dealing with optimizer parameters.
Unfortunately, this means that we have a list that must be kept in sync with
Keras. Oh w... | deep_qa-master | deep_qa/training/optimizers.py |
from typing import List
import tensorflow
import numpy
import keras.backend as K
class Step:
"""
Runs a computation graph.
Parameters
----------
inputs: Feed placeholders to the computation graph.
outputs: Output tensors to fetch.
updates: Additional update ops to be run at function cal... | deep_qa-master | deep_qa/training/step.py |
from keras import backend as K
from ..tensors.backend import VERY_NEGATIVE_NUMBER, VERY_LARGE_NUMBER
def ranking_loss(y_pred, y_true):
"""
Using this loss trains the model to give scores to all correct elements in y_true that are
higher than all scores it gives to incorrect elements in y_true.
For ex... | deep_qa-master | deep_qa/training/losses.py |
import logging
import os
from typing import Any, Dict, List, Tuple
import numpy
from keras.callbacks import CallbackList, EarlyStopping, LambdaCallback, ModelCheckpoint
from keras.models import model_from_json
from ..data.datasets import Dataset, IndexedDataset
from ..common.checks import ConfigurationError
from ..co... | deep_qa-master | deep_qa/training/trainer.py |
from typing import Callable
import os
from copy import deepcopy
import tensorflow
import keras.backend as K
from .train_utils import pin_variable_device_scope, average_gradients
from .models import DeepQaModel
from .step import Step
from ..common.params import Params, ConfigurationError
def compile_parallel_model(m... | deep_qa-master | deep_qa/training/multi_gpu.py |
"""
These are utility functions that are similar to calls to Keras' backend. Some of these are here
because a current function in keras.backend is broken, some are things that just haven't been
implemented.
"""
import keras.backend as K
import tensorflow as tf
VERY_LARGE_NUMBER = 1e30
VERY_SMALL_NUMBER = 1e-30
VERY_N... | deep_qa-master | deep_qa/tensors/backend.py |
deep_qa-master | deep_qa/tensors/__init__.py | |
from keras import backend as K
from .backend import switch
def masked_batch_dot(tensor_a, tensor_b, mask_a, mask_b):
'''
The simplest case where this function is applicable is the following:
tensor_a: (batch_size, a_length, embed_dim)
tensor_b: (batch_size, b_length, embed_dim)
mask_a: None or (... | deep_qa-master | deep_qa/tensors/masked_operations.py |
"""
Similarity functions take a pair of tensors with the same shape, and compute a similarity function
on the vectors in the last dimension. For example, the tensors might both have shape
`(batch_size, sentence_length, embedding_dim)`, and we will compute some function of the two
vectors of length `embedding_dim` for ... | deep_qa-master | deep_qa/tensors/similarity_functions/similarity_function.py |
from typing import List
from keras import backend as K
from overrides import overrides
from ...common.checks import ConfigurationError
from .similarity_function import SimilarityFunction
class Linear(SimilarityFunction):
"""
This similarity function performs a dot product between a vector of weights and som... | deep_qa-master | deep_qa/tensors/similarity_functions/linear.py |
from typing import List
from keras import backend as K
from overrides import overrides
from .similarity_function import SimilarityFunction
class Bilinear(SimilarityFunction):
"""
This similarity function performs a bilinear transformation of the two input vectors. This
function has a matrix of weights ... | deep_qa-master | deep_qa/tensors/similarity_functions/bilinear.py |
from collections import OrderedDict
from .bilinear import Bilinear
from .dot_product import DotProduct
from .linear import Linear
from .cosine_similarity import CosineSimilarity
# The first item added here will be used as the default in some cases.
similarity_functions = OrderedDict() # pylint: disable=invalid-name
... | deep_qa-master | deep_qa/tensors/similarity_functions/__init__.py |
from typing import List
from keras import backend as K
from overrides import overrides
from ...common.checks import ConfigurationError
from .similarity_function import SimilarityFunction
class CosineSimilarity(SimilarityFunction):
"""
This similarity function simply computes the cosine similarity between ea... | deep_qa-master | deep_qa/tensors/similarity_functions/cosine_similarity.py |
from typing import List
from keras import backend as K
from overrides import overrides
from ...common.checks import ConfigurationError
from .similarity_function import SimilarityFunction
class DotProduct(SimilarityFunction):
"""
This similarity function simply computes the dot product between each pair of v... | deep_qa-master | deep_qa/tensors/similarity_functions/dot_product.py |
from .entailment import concrete_models as entailment_models
from .sequence_tagging import concrete_models as sequence_tagging_models
from .reading_comprehension import concrete_models as reading_comprehension_models
from .text_classification import concrete_models as text_classification_models
concrete_models = {} #... | deep_qa-master | deep_qa/models/__init__.py |
from keras.layers import Dense, Input, TimeDistributed
from overrides import overrides
from ...common.params import Params
from ...data.instances.sequence_tagging import concrete_instances
from ...training.text_trainer import TextTrainer
from ...training.models import DeepQaModel
class SimpleTagger(TextTrainer):
... | deep_qa-master | deep_qa/models/sequence_tagging/simple_tagger.py |
from .simple_tagger import SimpleTagger
concrete_models = { # pylint: disable=invalid-name
'SimpleTagger': SimpleTagger,
}
| deep_qa-master | deep_qa/models/sequence_tagging/__init__.py |
from typing import Dict
from keras.layers import Input
from overrides import overrides
from ...data.instances.entailment.snli_instance import SnliInstance
from ...training.text_trainer import TextTrainer
from ...layers.entailment_models import DecomposableAttentionEntailment
from ...training.models import DeepQaModel... | deep_qa-master | deep_qa/models/entailment/decomposable_attention.py |
from .decomposable_attention import DecomposableAttention
concrete_models = { # pylint: disable=invalid-name
'DecomposableAttention': DecomposableAttention,
}
| deep_qa-master | deep_qa/models/entailment/__init__.py |
from typing import Dict, List
from keras.layers import Dense, Input, Concatenate, TimeDistributed
from overrides import overrides
from ...data.instances.reading_comprehension import CharacterSpanInstance
from ...layers import ComplexConcat, Highway
from ...layers.attention import MatrixAttention, MaskedSoftmax, Weigh... | deep_qa-master | deep_qa/models/reading_comprehension/bidirectional_attention.py |
from typing import Dict
from overrides import overrides
from keras.layers import Input, Dropout, Concatenate
from ...data.instances.reading_comprehension.mc_question_passage_instance import McQuestionPassageInstance
from ...common.checks import ConfigurationError
from ...layers.backend import BatchDot
from ...layers.... | deep_qa-master | deep_qa/models/reading_comprehension/gated_attention_reader.py |
from .attention_sum_reader import AttentionSumReader
from .bidirectional_attention import BidirectionalAttentionFlow
from .gated_attention_reader import GatedAttentionReader
concrete_models = { # pylint: disable=invalid-name
'AttentionSumReader': AttentionSumReader,
'BidirectionalAttentionFlow': Bidir... | deep_qa-master | deep_qa/models/reading_comprehension/__init__.py |
from typing import Dict
from overrides import overrides
from keras.layers import Input
from ...data.instances.reading_comprehension import McQuestionPassageInstance
from ...layers import L1Normalize
from ...layers import OptionAttentionSum
from ...layers.attention import Attention
from ...training import TextTrainer
f... | deep_qa-master | deep_qa/models/reading_comprehension/attention_sum_reader.py |
from overrides import overrides
from keras.layers import Dense, Dropout, Input
from ...data.instances.text_classification.text_classification_instance import TextClassificationInstance
from ...training.text_trainer import TextTrainer
from ...training.models import DeepQaModel
from ...common.params import Params
cla... | deep_qa-master | deep_qa/models/text_classification/classification_model.py |
from .classification_model import ClassificationModel
concrete_models = { # pylint: disable=invalid-name
'ClassificationModel': ClassificationModel,
}
| deep_qa-master | deep_qa/models/text_classification/__init__.py |
deep_qa-master | deep_qa/testing/__init__.py | |
# pylint: disable=invalid-name,protected-access
from copy import deepcopy
from unittest import TestCase
import codecs
import gzip
import logging
import os
import shutil
from keras import backend as K
import numpy
from numpy.testing import assert_allclose
from deep_qa.common.checks import log_keras_version_info
from d... | deep_qa-master | deep_qa/testing/test_case.py |
import io
import os
class TeeLogger:
"""
This class is an attempt to maintain logs of both stdout and stderr for when models are run.
To use this class, at the beginning of your script insert these lines::
sys.stdout = TeeLogger("stdout.log", sys.stdout)
sys.stderr = TeeLogger("stdout.log... | deep_qa-master | deep_qa/common/tee_logger.py |
from typing import Any, Dict, List
from collections import MutableMapping
import logging
import pyhocon
from overrides import overrides
from .checks import ConfigurationError
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
PARAMETER = 60
logging.addLevelName(PARAMETER, "PARAM")
def __param(se... | deep_qa-master | deep_qa/common/params.py |
from typing import List
from keras.models import Model
from ..training.models import DeepQaModel
def get_submodel(model: Model,
input_layer_names: List[str],
output_layer_names: List[str],
train_model: bool=False,
name=None):
"""
Returns a ... | deep_qa-master | deep_qa/common/models.py |
from itertools import zip_longest
from typing import Any, Dict, List
import random
def group_by_count(iterable: List[Any], count: int, default_value: Any) -> List[List[Any]]:
"""
Takes a list and groups it into sublists of size ``count``, using ``default_value`` to pad the
list at the end if the list is n... | deep_qa-master | deep_qa/common/util.py |
import logging
import os
REQUIRED_PYTHONHASHSEED = '2157'
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
class ConfigurationError(Exception):
def __init__(self, message):
super(ConfigurationError, self).__init__()
self.message = message
def __str__(self):
return... | deep_qa-master | deep_qa/common/checks.py |
deep_qa-master | deep_qa/common/__init__.py | |
from .datasets.dataset import Dataset, IndexedDataset, TextDataset
from .data_generator import DataGenerator
from .data_indexer import DataIndexer
from .tokenizers import tokenizers
| deep_qa-master | deep_qa/data/__init__.py |
from collections import defaultdict
import codecs
import logging
import tqdm
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
class DataIndexer:
"""
A DataIndexer maps strings to integers, allowing for strings to be mapped to an
out-of-vocabulary token.
DataIndexers are fit to a... | deep_qa-master | deep_qa/data/data_indexer.py |
import codecs
import gzip
import logging
import numpy
from keras.layers import Embedding
from .data_indexer import DataIndexer
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
class PretrainedEmbeddings:
@staticmethod
def initialize_random_matrix(shape, seed=1337):
# TODO(matt): ... | deep_qa-master | deep_qa/data/embeddings.py |
from typing import List
import logging
import random
from copy import deepcopy
from ..common.params import Params
from ..common.util import group_by_count
from . import IndexedDataset
from .instances import IndexedInstance
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
class DataGenerator:
... | deep_qa-master | deep_qa/data/data_generator.py |
from collections import OrderedDict
from typing import List
from overrides import overrides
class WordSplitter:
"""
A ``WordSplitter`` splits strings into words. This is typically called a "tokenizer" in NLP,
but we need ``Tokenizer`` to refer to something else, so we're using ``WordSplitter`` here
... | deep_qa-master | deep_qa/data/tokenizers/word_splitter.py |
from collections import OrderedDict
from .character_tokenizer import CharacterTokenizer
from .word_and_character_tokenizer import WordAndCharacterTokenizer
from .word_tokenizer import WordTokenizer
# The first item added here will be used as the default in some cases.
tokenizers = OrderedDict() # pylint: disable=inv... | deep_qa-master | deep_qa/data/tokenizers/__init__.py |
from typing import List
from .word_splitter import word_splitters
from .word_stemmer import word_stemmers
from .word_filter import word_filters
from ...common.params import Params
class WordProcessor:
"""
A WordProcessor handles the splitting of strings into words (with the use of a WordSplitter) as well as ... | deep_qa-master | deep_qa/data/tokenizers/word_processor.py |
from typing import Callable, Dict, List, Tuple
from overrides import overrides
from keras.layers import Layer
from .tokenizer import Tokenizer
from .word_processor import WordProcessor
from ..data_indexer import DataIndexer
from ...common.params import Params
class WordTokenizer(Tokenizer):
"""
A ``WordToke... | deep_qa-master | deep_qa/data/tokenizers/word_tokenizer.py |
from typing import Callable, Dict, List, Tuple
from keras.layers import Layer
from ..data_indexer import DataIndexer
from ...common.params import Params
class Tokenizer:
"""
A Tokenizer splits strings into sequences of tokens that can be used in a model. The "tokens"
here could be words, characters, or w... | deep_qa-master | deep_qa/data/tokenizers/tokenizer.py |
from collections import OrderedDict
from nltk.stem import PorterStemmer as NltkPorterStemmer
from overrides import overrides
class WordStemmer:
"""
A ``WordStemmer`` lemmatizes words. This means that we map words to their root form, so that,
e.g., "have", "has", and "had" all have the same internal repr... | deep_qa-master | deep_qa/data/tokenizers/word_stemmer.py |
from typing import Any, Callable, Dict, List, Tuple
from overrides import overrides
from keras import backend as K
from keras.layers import Concatenate, Layer
from .tokenizer import Tokenizer
from .word_processor import WordProcessor
from ..data_indexer import DataIndexer
from ...layers.backend import CollapseToBatch... | deep_qa-master | deep_qa/data/tokenizers/word_and_character_tokenizer.py |
from typing import Callable, Dict, List, Tuple
from keras.layers import Layer
from overrides import overrides
from .tokenizer import Tokenizer
from ..data_indexer import DataIndexer
from ...common.params import Params
class CharacterTokenizer(Tokenizer):
"""
A CharacterTokenizer splits strings into character... | deep_qa-master | deep_qa/data/tokenizers/character_tokenizer.py |
from collections import OrderedDict
from typing import List
from overrides import overrides
class WordFilter:
"""
A ``WordFilter`` removes words from a token list. Typically, this is for stopword removal,
though you could feasibly use it for more domain-specific removal if you want.
Word removal ha... | deep_qa-master | deep_qa/data/tokenizers/word_filter.py |
deep_qa-master | deep_qa/data/dataset_readers/__init__.py | |
import argparse
from collections import Counter
import json
import logging
import os
import random
from typing import List, Tuple
import numpy
from tqdm import tqdm
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
random.seed(2157)
def main():
log_format = '%(asctime)s - %(name)s - %(levelna... | deep_qa-master | deep_qa/data/dataset_readers/squad_sentence_selection_reader.py |
from collections import OrderedDict
from .entailment.snli_dataset import SnliDataset
from .language_modeling.language_modeling_dataset import LanguageModelingDataset
from .dataset import Dataset, TextDataset, IndexedDataset
concrete_datasets = OrderedDict() # pylint: disable=invalid-name
concrete_datasets["text"] =... | deep_qa-master | deep_qa/data/datasets/__init__.py |
import codecs
import itertools
import logging
from typing import Dict, List
import numpy
import tqdm
from ...common.util import add_noise_to_dict_values
from ...common.params import Params
from ..data_indexer import DataIndexer
from ..instances.instance import Instance, TextInstance, IndexedInstance
logger = logging... | deep_qa-master | deep_qa/data/datasets/dataset.py |
from typing import List
import json
from overrides import overrides
from ..dataset import TextDataset, log_label_counts
from ...instances import TextInstance
from ....common.params import Params
class SnliDataset(TextDataset):
def __init__(self, instances: List[TextInstance], params: Params=None):
supe... | deep_qa-master | deep_qa/data/datasets/entailment/snli_dataset.py |
deep_qa-master | deep_qa/data/datasets/entailment/__init__.py | |
deep_qa-master | deep_qa/data/datasets/language_modeling/__init__.py | |
from typing import List
from overrides import overrides
from ..dataset import TextDataset, log_label_counts
from ...instances import TextInstance
from ...instances.language_modeling import SentenceInstance
from ....common.params import Params
class LanguageModelingDataset(TextDataset):
def __init__(self, insta... | deep_qa-master | deep_qa/data/datasets/language_modeling/language_modeling_dataset.py |
"""
This module contains the base ``Instance`` classes that concrete classes
inherit from. Specifically, there are three classes:
1. ``Instance``, that just exists as a base type with no functionality
2. ``TextInstance``, which adds a ``words()`` method and a method to convert
strings to indices using a DataIndexer... | deep_qa-master | deep_qa/data/instances/instance.py |
from .instance import Instance, TextInstance, IndexedInstance
| deep_qa-master | deep_qa/data/instances/__init__.py |
from .pretokenized_tagging_instance import PreTokenizedTaggingInstance
from .tagging_instance import TaggingInstance, IndexedTaggingInstance
concrete_instances = { # pylint: disable=invalid-name
'PreTokenizedTaggingInstance': PreTokenizedTaggingInstance,
}
| deep_qa-master | deep_qa/data/instances/sequence_tagging/__init__.py |
from typing import Dict, List, Any
import numpy
from overrides import overrides
from ..instance import TextInstance, IndexedInstance
from ...data_indexer import DataIndexer
class TaggingInstance(TextInstance):
"""
A ``TaggingInstance`` represents a passage of text and a tag sequence over that text.
The... | deep_qa-master | deep_qa/data/instances/sequence_tagging/tagging_instance.py |
from typing import List
import numpy
from overrides import overrides
from .tagging_instance import TaggingInstance
from ...data_indexer import DataIndexer
class PreTokenizedTaggingInstance(TaggingInstance):
"""
This is a ``TaggingInstance`` where the text has been pre-tokenized. Thus the ``text`` member
... | deep_qa-master | deep_qa/data/instances/sequence_tagging/pretokenized_tagging_instance.py |
from overrides import overrides
from .sentence_pair_instance import SentencePairInstance
class SnliInstance(SentencePairInstance):
"""
An SnliInstance is a SentencePairInstance that represents a pair of (text, hypothesis) from the
Stanford Natural Language Inference (SNLI) dataset, with an associated lab... | deep_qa-master | deep_qa/data/instances/entailment/snli_instance.py |
from .sentence_pair_instance import SentencePairInstance, IndexedSentencePairInstance
from .snli_instance import SnliInstance
| deep_qa-master | deep_qa/data/instances/entailment/__init__.py |
from typing import Dict, List
import numpy
from overrides import overrides
from ..instance import TextInstance, IndexedInstance
from ...data_indexer import DataIndexer
class SentencePairInstance(TextInstance):
"""
SentencePairInstance contains a labeled pair of instances accompanied by a binary label. You
... | deep_qa-master | deep_qa/data/instances/entailment/sentence_pair_instance.py |
from typing import Dict, List, Tuple
import numpy as np
from overrides import overrides
from .question_passage_instance import IndexedQuestionPassageInstance, QuestionPassageInstance
from ...data_indexer import DataIndexer
class McQuestionPassageInstance(QuestionPassageInstance):
"""
A McQuestionPassageInsta... | deep_qa-master | deep_qa/data/instances/reading_comprehension/mc_question_passage_instance.py |
from typing import Dict, List, Any
import numpy as np
from overrides import overrides
from ..instance import TextInstance, IndexedInstance
from ...data_indexer import DataIndexer
class QuestionPassageInstance(TextInstance):
"""
A QuestionPassageInstance is a base class for datasets that consist primarily of... | deep_qa-master | deep_qa/data/instances/reading_comprehension/question_passage_instance.py |
from .character_span_instance import CharacterSpanInstance, IndexedCharacterSpanInstance
from .mc_question_passage_instance import McQuestionPassageInstance, IndexedMcQuestionPassageInstance
from .question_passage_instance import QuestionPassageInstance, IndexedQuestionPassageInstance
| deep_qa-master | deep_qa/data/instances/reading_comprehension/__init__.py |
from typing import Tuple, List
import numpy
from overrides import overrides
from .question_passage_instance import QuestionPassageInstance, IndexedQuestionPassageInstance
from ...data_indexer import DataIndexer
class CharacterSpanInstance(QuestionPassageInstance):
"""
A CharacterSpanInstance is a QuestionPa... | deep_qa-master | deep_qa/data/instances/reading_comprehension/character_span_instance.py |
from .text_classification_instance import TextClassificationInstance, IndexedTextClassificationInstance
| deep_qa-master | deep_qa/data/instances/text_classification/__init__.py |
from typing import Dict, List
import numpy
from overrides import overrides
from ..instance import TextInstance, IndexedInstance
from ...data_indexer import DataIndexer
class TextClassificationInstance(TextInstance):
"""
A TextClassificationInstance is a :class:`TextInstance` that is a single passage of text... | deep_qa-master | deep_qa/data/instances/text_classification/text_classification_instance.py |
from typing import Dict, List
import numpy
from overrides import overrides
from ..instance import TextInstance, IndexedInstance
from ...data_indexer import DataIndexer
class SentenceInstance(TextInstance):
"""
A ``SentenceInstance`` is a :class:`TextInstance` that is a single passage of text, with no
as... | deep_qa-master | deep_qa/data/instances/language_modeling/sentence_instance.py |
from .sentence_instance import SentenceInstance, IndexedSentenceInstance
| deep_qa-master | deep_qa/data/instances/language_modeling/__init__.py |
# pylint: disable=invalid-name,no-self-use
import pyhocon
from deep_qa.common.params import Params, replace_none
from deep_qa.models import concrete_models
from deep_qa.testing.test_case import DeepQaTestCase
class TestExampleExperiments(DeepQaTestCase):
def setUp(self):
super(TestExampleExperiments, se... | deep_qa-master | tests/example_experiments_test.py |
deep_qa-master | tests/__init__.py | |
# pylint: disable=invalid-name,no-self-use
import json
import os
import numpy
from numpy.testing import assert_almost_equal
from deep_qa.run import compute_accuracy
from deep_qa.run import run_model_from_file, load_model, evaluate_model
from deep_qa.run import score_dataset, score_dataset_with_ensemble
from deep_qa.te... | deep_qa-master | tests/run_test.py |
# pylint: disable=no-self-use
import numpy
from numpy.testing import assert_almost_equal
import keras.backend as K
from keras.layers import Input
from keras.models import Model
from deep_qa.layers import Overlap
class TestOverlap:
def test_batched_case(self):
tensor_a_len = 5
tensor_b_len = 4
... | deep_qa-master | tests/layers/overlap_test.py |
# pylint: disable=no-self-use
import numpy
from keras.layers import Input, Embedding, merge
from keras.models import Model
import keras.backend as K
from deep_qa.layers.encoders import AttentiveGru
class TestAttentiveGRU:
def test_on_unmasked_input(self):
sentence_length = 5
embedding_dim = 10
... | deep_qa-master | tests/layers/attentive_gru_test.py |
# pylint: disable=no-self-use
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
import keras.backend as K
from keras.layers import Input
from keras.models import Model
from deep_qa.common.checks import ConfigurationError
from deep_qa.layers import OptionAttentionSum
from deep_qa... | deep_qa-master | tests/layers/test_option_attention_sum.py |
# pylint: disable=no-self-use,invalid-name
import numpy
from keras.layers import Embedding, Input
from keras.models import Model
from deep_qa.layers.entailment_models import MultipleChoiceTupleEntailment
class TestTupleAlignment:
def test_tuple_alignment_does_not_crash(self):
question_length = 5
n... | deep_qa-master | tests/layers/tuple_alignment_test.py |
# pylint: disable=no-self-use
import numpy
from numpy.testing import assert_almost_equal
from keras.layers import Input
from keras.models import Model
from deep_qa.layers import BiGRUIndexSelector
class TestBiGRUIndexSelector():
def test_batched_case(self):
document_length = 5
gru_hidden_dim = 2... | deep_qa-master | tests/layers/bigru_index_selector_test.py |
deep_qa-master | tests/layers/__init__.py | |
# pylint: disable=no-self-use,invalid-name
from keras.layers import Input, Embedding
from keras.models import Model
import numpy as np
from deep_qa.layers.encoders import PositionalEncoder
class TestPositionalEncoder:
def test_on_unmasked_input(self):
sentence_length = 3
embedding_dim = 3
... | deep_qa-master | tests/layers/positional_encoder_test.py |
# pylint: disable=no-self-use
import numpy as np
from numpy.testing import assert_array_almost_equal
from deep_qa.layers import L1Normalize
from deep_qa.testing.test_case import DeepQaTestCase
from keras.layers import Input, Masking
from keras.models import Model
class TestL1Normalize(DeepQaTestCase):
def test_ge... | deep_qa-master | tests/layers/test_l1_normalize.py |
# pylint: disable=no-self-use,invalid-name
import numpy
from flaky import flaky
from keras.layers import Input
from keras.models import Model
from deep_qa.layers import ComplexConcat
class TestComplexConcatLayer:
def test_call_works_on_simple_input(self):
input_shape = (3, 4, 5, 7)
input_1 = Inpu... | deep_qa-master | tests/layers/complex_concat_test.py |
# pylint: disable=no-self-use
import numpy as np
from numpy.testing import assert_array_almost_equal
from keras.layers import Input
from keras.models import Model
from deep_qa.layers.backend.add_mask import AddMask
from deep_qa.layers.subtract_minimum import SubtractMinimum
from deep_qa.testing.test_case import DeepQaT... | deep_qa-master | tests/layers/test_subtract_minimum.py |
# pylint: disable=no-self-use,invalid-name
import numpy
from numpy.testing import assert_array_equal
from keras.layers import Embedding, Input
from keras.models import Model
from deep_qa.layers import VectorMatrixMerge
from deep_qa.layers.wrappers import OutputMask
class TestVectorMatrixMerge:
def test_merge_wor... | deep_qa-master | tests/layers/vector_matrix_merge_test.py |
# pylint: disable=no-self-use,invalid-name
import numpy
from keras.layers import Input, Embedding
from keras.models import Model
from deep_qa.layers.entailment_models import DecomposableAttentionEntailment
class TestDecomposableAttention:
def test_decomposable_attention_does_not_crash(self):
sentence_len... | deep_qa-master | tests/layers/decomposable_attention_test.py |
# pylint: disable=no-self-use,invalid-name
import numpy
from keras.layers import Input, Embedding
from keras.models import Model
from deep_qa.layers import VectorMatrixSplit
from deep_qa.layers.wrappers import OutputMask
class TestVectorMatrixSplit:
def test_split_works_correctly_on_word_indices(self):
vo... | deep_qa-master | tests/layers/vector_matrix_split_test.py |
# pylint: disable=no-self-use, invalid-name
import numpy as np
from numpy.testing import assert_array_almost_equal
from deep_qa.layers import NoisyOr, BetweenZeroAndOne
from deep_qa.testing.test_case import DeepQaTestCase
from keras import backend as K
from keras.layers import Input
from keras.models import Model
cla... | deep_qa-master | tests/layers/noisy_or_test.py |
# pylint: disable=no-self-use,invalid-name
import numpy
from deep_qa.layers.encoders import BOWEncoder
from deep_qa.layers.wrappers import AddEncoderMask, OutputMask
from deep_qa.testing.test_case import DeepQaTestCase
from deep_qa.training.models import DeepQaModel
from keras.layers import Embedding, Input
class Tes... | deep_qa-master | tests/layers/wrappers/add_encoder_mask_test.py |
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