repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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flair | flair-master/flair/nn/dropout.py | import torch
class LockedDropout(torch.nn.Module):
"""Implementation of locked (or variational) dropout.
Randomly drops out entire parameters in embedding space.
"""
def __init__(self, dropout_rate=0.5, batch_first=True, inplace=False) -> None:
super().__init__()
self.dropout_rate = ... | 1,747 | 29.666667 | 88 | py |
flair | flair-master/flair/nn/recurrent.py | from torch import nn
rnn_layers = {"lstm": (nn.LSTM, 2), "gru": (nn.GRU, 1)}
def create_recurrent_layer(layer_type, initial_size, hidden_size, nlayers, dropout=0, **kwargs):
layer_type = layer_type.lower()
assert layer_type in rnn_layers
module, hidden_count = rnn_layers[layer_type]
if nlayers == 1:... | 437 | 28.2 | 96 | py |
flair | flair-master/flair/nn/decoder.py | import logging
from typing import List, Optional
import torch
import flair
from flair.data import Dictionary, Sentence
from flair.embeddings import Embeddings
from flair.nn.distance import (
CosineDistance,
EuclideanDistance,
HyperbolicDistance,
LogitCosineDistance,
NegativeScaledDotProduct,
)
fro... | 8,375 | 38.140187 | 140 | py |
flair | flair-master/flair/nn/distance/hyperbolic.py | """Hyperbolic distances implemented in pytorch.
This module was copied from the repository the following repository:
https://github.com/asappresearch/dynamic-classification
It contains the code from the paper "Metric Learning for Dynamic Text
Classification".
https://arxiv.org/abs/1911.01026
In case this file is mo... | 3,718 | 25.949275 | 132 | py |
flair | flair-master/flair/nn/distance/euclidean.py | """Euclidean distances implemented in pytorch.
This module was copied from the repository the following repository:
https://github.com/asappresearch/dynamic-classification
It contains the code from the paper "Metric Learning for Dynamic Text
Classification".
https://arxiv.org/abs/1911.01026
In case this file is mod... | 1,839 | 26.462687 | 131 | py |
flair | flair-master/flair/nn/distance/cosine.py | import torch
# Source: https://github.com/UKPLab/sentence-transformers/blob/master/sentence_transformers/util.py#L23
def dot_product(a: torch.Tensor, b: torch.Tensor, normalize=False):
"""Computes dot product for pairs of vectors.
:param normalize: Vectors are normalized (leads to cosine similarity)
:re... | 1,129 | 27.974359 | 103 | py |
flair | flair-master/flair/models/text_regression_model.py | import logging
import typing
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data.dataset import Dataset
from tqdm import tqdm
import flair
import flair.embeddings
from flair.data import Corpus, Dictionary, Sentence, _iter_dataset
... | 9,039 | 36.201646 | 120 | py |
flair | flair-master/flair/models/pairwise_classification_model.py | import typing
from typing import List
import torch
import flair.embeddings
import flair.nn
from flair.data import Corpus, Sentence, TextPair, _iter_dataset
class TextPairClassifier(flair.nn.DefaultClassifier[TextPair, TextPair]):
"""Text Pair Classification Model for tasks such as Recognizing Textual Entailment... | 4,628 | 38.905172 | 118 | py |
flair | flair-master/flair/models/word_tagger_model.py | import logging
from pathlib import Path
from typing import Any, Dict, List, Union
import torch
import flair.nn
from flair.data import Dictionary, Sentence, Span, Token
from flair.embeddings import TokenEmbeddings
log = logging.getLogger("flair")
def WordTagger(embeddings, tag_dictionary, tag_type, **classifierargs... | 9,672 | 40.337607 | 120 | py |
flair | flair-master/flair/models/pairwise_regression_model.py | import typing
from pathlib import Path
from typing import Any, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data.dataset import Dataset
from tqdm import tqdm
import flair.embeddings
import flair.nn
from flair.data import Corpus, Dictionary, Sentence, TextPair, _iter_dataset
from fla... | 12,876 | 36.324638 | 119 | py |
flair | flair-master/flair/models/sequence_tagger_model.py | import logging
import tempfile
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union, cast
from urllib.error import HTTPError
import torch
import torch.nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from tqdm import tqdm
import... | 45,746 | 43.500973 | 137 | py |
flair | flair-master/flair/models/multitask_model.py | import logging
import random
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
import flair.nn
from flair.data import DT, Dictionary, Sentence
from flair.file_utils import cached_path
from flair.nn import Classifier
from flair.training_utils import Result
log = logging.... | 10,773 | 38.756458 | 115 | py |
flair | flair-master/flair/models/entity_linker_model.py | import logging
import re
from functools import lru_cache
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Set, Union
from unicodedata import category
import torch
import flair.embeddings
import flair.nn
from flair.data import Dictionary, Sentence, Span
from flair.file_utils import cach... | 9,722 | 41.458515 | 188 | py |
flair | flair-master/flair/models/text_classification_model.py | import logging
from pathlib import Path
from typing import Any, Dict, List, Union
import torch
import flair.embeddings
import flair.nn
from flair.data import Sentence
from flair.file_utils import cached_path
log = logging.getLogger("flair")
class TextClassifier(flair.nn.DefaultClassifier[Sentence, Sentence]):
... | 4,864 | 34.510949 | 114 | py |
flair | flair-master/flair/models/relation_extractor_model.py | import logging
from pathlib import Path
from typing import Any, Dict, List, Optional, Set, Tuple, Union
import torch
import flair.embeddings
import flair.nn
from flair.data import Relation, Sentence
from flair.file_utils import cached_path
log = logging.getLogger("flair")
class RelationExtractor(flair.nn.DefaultCl... | 6,828 | 37.801136 | 120 | py |
flair | flair-master/flair/models/tars_model.py | import logging
import typing
from abc import ABC
from collections import OrderedDict
from pathlib import Path
from typing import Any, Dict, List, Optional, Set, Tuple, Union
import numpy as np
import torch
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import minmax_scale
from tqdm i... | 40,613 | 40.956612 | 126 | py |
flair | flair-master/flair/models/lemmatizer_model.py | import logging
from math import inf
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
import flair.embeddings
import flair.nn
from flair.data import Dictionary, Sentence, Token
from flair.datasets import DataLoader, FlairDatapointDataset
from flair.training_utils import Result, store_e... | 34,759 | 48.026798 | 120 | py |
flair | flair-master/flair/models/language_model.py | import math
from pathlib import Path
from typing import List, Optional, Tuple, Union
import torch
from torch import logsumexp, nn
from torch.optim import Optimizer
import flair
from flair.data import Dictionary
from flair.nn.recurrent import create_recurrent_layer
class LanguageModel(nn.Module):
"""Container mo... | 17,001 | 35.021186 | 117 | py |
flair | flair-master/flair/models/relation_classifier_model.py | import itertools
import logging
import typing
from abc import ABC, abstractmethod
from pathlib import Path
from typing import (
Any,
Dict,
Iterator,
List,
NamedTuple,
Optional,
Sequence,
Set,
Tuple,
Union,
cast,
)
import torch
from torch.utils.data.dataset import Dataset
im... | 34,165 | 45.995873 | 120 | py |
flair | flair-master/flair/models/sequence_tagger_utils/viterbi.py | from typing import Tuple
import numpy as np
import torch
import torch.nn
from torch.nn.functional import softmax
from torch.nn.utils.rnn import pack_padded_sequence
import flair
from flair.data import Dictionary, Label, List, Sentence
START_TAG: str = "<START>"
STOP_TAG: str = "<STOP>"
class ViterbiLoss(torch.nn.M... | 10,765 | 44.046025 | 119 | py |
flair | flair-master/flair/models/sequence_tagger_utils/crf.py | import torch
import flair
START_TAG: str = "<START>"
STOP_TAG: str = "<STOP>"
class CRF(torch.nn.Module):
"""Conditional Random Field.
Conditional Random Field Implementation according to sgrvinod (https://github.com/sgrvinod).
Classifier which predicts single tag / class / label for given word based o... | 2,171 | 41.588235 | 119 | py |
flair | flair-master/flair/embeddings/document.py | import logging
from typing import Any, Dict, List, Optional, Union, cast
import torch
from sklearn.feature_extraction.text import TfidfVectorizer
from torch.nn import RNNBase
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import flair
from flair.data import Sentence
from flair.embeddings.bas... | 30,169 | 38.080311 | 129 | py |
flair | flair-master/flair/embeddings/base.py | import inspect
import logging
from abc import abstractmethod
from typing import Any, Dict, Generic, List, Sequence, Type, Union
import torch
from torch.nn import Parameter, ParameterList
import flair
from flair.data import DT, Sentence
log = logging.getLogger("flair")
class Embeddings(torch.nn.Module, Generic[DT])... | 7,959 | 33.912281 | 131 | py |
flair | flair-master/flair/embeddings/legacy.py | import logging
import re
from abc import abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union
import torch
from deprecated import deprecated
from transformers import (
AlbertModel,
AlbertTokenizer,
BertModel,
BertTokenizer,
CamembertModel,
CamembertToken... | 63,878 | 39.099812 | 174 | py |
flair | flair-master/flair/embeddings/image.py | import logging
from typing import Any, Dict, List, Optional
import torch
import torch.nn.functional as F
from torch.nn import (
AdaptiveAvgPool2d,
AdaptiveMaxPool2d,
Conv2d,
Dropout2d,
Linear,
MaxPool2d,
Parameter,
ReLU,
Sequential,
TransformerEncoder,
TransformerEncoderLaye... | 10,902 | 37.663121 | 118 | py |
flair | flair-master/flair/embeddings/transformer.py | import inspect
import os
import random
import re
import tempfile
import warnings
import zipfile
from abc import abstractmethod
from io import BytesIO
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Type, Union, cast
import torch
from torch.jit import ScriptModule
from transformers import ... | 58,455 | 41.606414 | 146 | py |
flair | flair-master/flair/embeddings/token.py | import hashlib
import logging
import os
import re
import tempfile
from collections import Counter
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import gensim
import numpy as np
import torch
from bpemb import BPEmb
from gensim.models import KeyedVectors
from gensim.models.fasttext import ... | 64,771 | 40.734536 | 131 | py |
flair | flair-master/flair/datasets/text_image.py | import json
import logging
import os
import urllib
from pathlib import Path
from typing import List
import numpy as np
import torch.utils.data.dataloader
from torch.utils.data import Dataset
from tqdm import tqdm
from flair.data import Corpus, DataPair, FlairDataset, Image, Sentence
from flair.file_utils import cache... | 3,092 | 35.821429 | 121 | py |
flair | flair-master/flair/datasets/base.py | import logging
from abc import abstractmethod
from pathlib import Path
from typing import Generic, List, Optional, Union
import torch.utils.data.dataloader
from deprecated import deprecated
from flair.data import DT, FlairDataset, Sentence, Tokenizer
from flair.tokenization import SegtokTokenizer, SpaceTokenizer
log... | 9,549 | 33.854015 | 128 | py |
flair | flair-master/flair/datasets/ocr.py | import json
from pathlib import Path
from typing import Dict, Optional, Union
import gdown.download_folder
import PIL
from torch.utils.data import Dataset
import flair
from flair.data import BoundingBox, Corpus, FlairDataset, Sentence, get_spans_from_bio
from flair.datasets.base import find_train_dev_test_files
cla... | 10,117 | 40.130081 | 120 | py |
flair | flair-master/flair/datasets/sequence_labeling.py | import copy
import json
import logging
import os
import re
import shutil
from collections import defaultdict
from pathlib import Path
from typing import (
Any,
DefaultDict,
Dict,
Iterable,
Iterator,
List,
Optional,
Tuple,
Union,
cast,
)
from torch.utils.data import ConcatDataset... | 197,364 | 40.160584 | 192 | py |
flair | flair-master/flair/datasets/document_classification.py | import csv
import json
import logging
import os
from pathlib import Path
from typing import Dict, List, Optional, Union
import flair
from flair.data import (
Corpus,
DataPair,
FlairDataset,
Sentence,
Tokenizer,
_iter_dataset,
)
from flair.datasets.base import find_train_dev_test_files
from flai... | 88,472 | 41.049905 | 134 | py |
flair | flair-master/flair/trainers/language_model_trainer.py | import datetime
import logging
import math
import random
import time
from pathlib import Path
from typing import Iterable, Optional, Type, Union
import torch
from torch import cuda
from torch.optim import AdamW, Optimizer
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.optim.sgd import SGD
from torch... | 17,266 | 35.660297 | 142 | py |
flair | flair-master/flair/trainers/trainer.py | import inspect
import logging
import os
import random
import time
import warnings
from inspect import signature
from pathlib import Path
from typing import List, Optional, Tuple, Type, Union
import torch
from torch.optim.sgd import SGD
from torch.utils.data.dataset import ConcatDataset
import flair
import flair.nn
fr... | 36,077 | 41.245902 | 124 | py |
flair | flair-master/flair/trainers/plugins/loggers/tensorboard.py | import logging
import os
from flair.trainers.plugins.base import TrainerPlugin
from flair.training_utils import log_line
log = logging.getLogger("flair")
class TensorboardLogger(TrainerPlugin):
"""Plugin that takes care of tensorboard logging."""
def __init__(self, log_dir=None, comment="", tracked_metrics... | 2,065 | 33.433333 | 229 | py |
flair | flair-master/tests/test_lemmatizer.py | import torch
import flair
from flair.data import Sentence
from flair.models import Lemmatizer
def test_words_to_char_indices():
sentence = Sentence("Hello look what a beautiful day!")
lemmatizer = Lemmatizer() # lemmatizer uses standard char dictionary
d = lemmatizer.dummy_index
e = lemmatizer.end... | 1,907 | 33.690909 | 116 | py |
flair | flair-master/tests/embedding_test_utils.py | from typing import Any, Dict, List, Optional, Type
import pytest
import torch
from flair.data import Sentence
from flair.embeddings import Embeddings
from flair.embeddings.base import load_embeddings
class BaseEmbeddingsTest:
embedding_cls: Type[Embeddings[Sentence]]
is_token_embedding: bool
is_document... | 7,511 | 39.387097 | 113 | py |
flair | flair-master/tests/test_trainer.py | import pytest
from torch.optim import Adam
import flair
from flair.data import Sentence
from flair.datasets import ClassificationCorpus
from flair.embeddings import DocumentPoolEmbeddings, FlairEmbeddings, WordEmbeddings
from flair.models import SequenceTagger, TextClassifier
from flair.trainers import ModelTrainer
t... | 4,999 | 28.585799 | 118 | py |
flair | flair-master/tests/conftest.py | from pathlib import Path
import pytest
import torch
import flair
@pytest.fixture(scope="module")
def resources_path():
return Path(__file__).parent / "resources"
@pytest.fixture(scope="module")
def tasks_base_path(resources_path):
return resources_path / "tasks"
@pytest.fixture()
def results_base_path(r... | 1,620 | 23.19403 | 89 | py |
flair | flair-master/tests/models/test_relation_classifier.py | from operator import itemgetter
from typing import Dict, List, Optional, Set, Tuple
import pytest
from torch.utils.data import Dataset
from flair.data import Relation, Sentence
from flair.datasets import ColumnCorpus, DataLoader
from flair.embeddings import TransformerDocumentEmbeddings
from flair.models import Relat... | 9,948 | 40.627615 | 118 | py |
flair | flair-master/tests/embeddings/test_transformer_word_embeddings.py | import importlib.util
import warnings
import pytest
import torch
from PIL import Image
from transformers.utils import is_detectron2_available
from flair.data import BoundingBox, Dictionary, Sentence
from flair.embeddings import TransformerJitWordEmbeddings, TransformerWordEmbeddings
from flair.models import SequenceT... | 15,110 | 45.352761 | 120 | py |
LSTM_Covariance | LSTM_Covariance-main/shallow_water/prediction_plotting.py | # %%
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 13:39:58 2021
@author: siboc
"""
import numpy as np
import scipy
import math
import matplotlib.pyplot as plt
#
data=np.load('data2/trainset_withx_repeat_shwater3_uniform0011_test6_1114.npy').astype(np.float32)
data1=np.load('data2/trainset_withx_repeat_shwater... | 12,529 | 31.973684 | 109 | py |
LSTM_Covariance | LSTM_Covariance-main/shallow_water/shallowwater_lstm1000_model.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 13:39:58 2021
@author: siboc
"""
import numpy as np
import scipy
import math
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.metrics import r2_score
import tensorflow as tf
import tensorflow.keras.backend as K
#... | 6,880 | 30.135747 | 157 | py |
LSTM_Covariance | LSTM_Covariance-main/shallow_water/shallowwater_lstm200_model.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 13:39:58 2021
@author: siboc
"""
import numpy as np
import scipy
import math
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.metrics import r2_score
import tensorflow as tf
import tensorflow.keras.backend as K
#... | 6,889 | 29.622222 | 157 | py |
LSTM_Covariance | LSTM_Covariance-main/lorenz/lstmR_d05R_plotting.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 13:39:58 2021
@author: siboc
"""
import numpy as np
import matplotlib.pyplot as plt
# check scikit-learn version
# check scikit-learn version
import pandas as pd
# def data_set_order(file):
# train_data = np.array(pd.read_csv(file))
# r0=train_data[:,:1... | 9,710 | 29.731013 | 101 | py |
LSTM_Covariance | LSTM_Covariance-main/lorenz/lorenz_lstm1000.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 13:39:58 2021
@author: siboc
"""
import numpy as np
import scipy
import math
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import r2_score
import tensorflow as tf
import keras.backend as K
imp... | 6,328 | 25.817797 | 121 | py |
LSTM_Covariance | LSTM_Covariance-main/lorenz/simulated_data_generation.py | # -*- coding: utf-8 -*-
# generate the trainning set for keras regression
import numpy as np
from scipy.optimize import fmin
from scipy.optimize import fmin_l_bfgs_b
#from scipy.optimize import fmin_ncg
from scipy.linalg import sqrtm
import math
from constructB import *
from lorentz_attractor import *
import matp... | 5,902 | 34.993902 | 192 | py |
LSTM_Covariance | LSTM_Covariance-main/lorenz/lorenz_lstm200.py | # -*- coding: utf-8 -*-
"""
Created on Wed Jan 6 13:39:58 2021
@author: siboc
"""
import numpy as np
import scipy
import math
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import r2_score
import tensorflow as tf
import keras.backend as K
# c... | 6,201 | 25.618026 | 121 | py |
ExplainableAIImageMeasures | ExplainableAIImageMeasures-main/setup.py | import pathlib
from setuptools import setup
# The directory containing this file
HERE = pathlib.Path(__file__).parent
# The text of the README file
README = (HERE / "README.md").read_text()
# This call to setup() does all the work
setup(
name="explainable_ai_image_measures",
version="1.0.1",
description=... | 989 | 32 | 91 | py |
ExplainableAIImageMeasures | ExplainableAIImageMeasures-main/explainable_ai_image_measures/scoring_metric.py | import numpy as np
import torch
from sklearn.metrics import auc
import torch.nn.functional as F
from explainable_ai_image_measures.irof import IrofDataset
from explainable_ai_image_measures.pixel_relevancy import PixelRelevancyDataset
class Measures:
def __init__(self,
model,
ba... | 7,637 | 37.771574 | 120 | py |
ExplainableAIImageMeasures | ExplainableAIImageMeasures-main/explainable_ai_image_measures/pixel_manipulation.py | import torch
from torch.utils.data import Dataset
import abc
class PixelManipulationBase(Dataset):
"""
Requires that self._pixel_batches is defined in the constructor
"""
def __init__(self, image, attribution, insert, batch_size, device, baseline_color):
self._image = image
self._batc... | 5,848 | 35.55625 | 118 | py |
ExplainableAIImageMeasures | ExplainableAIImageMeasures-main/explainable_ai_image_measures/irof.py | import torch
import numpy as np
from skimage.segmentation import slic
from explainable_ai_image_measures.pixel_manipulation import PixelManipulationBase
class IrofDataset(PixelManipulationBase):
def __init__(
self, image, attribution, batch_size, irof_segments, irof_sigma, device, baseline_color
):
... | 3,308 | 35.766667 | 95 | py |
ExplainableAIImageMeasures | ExplainableAIImageMeasures-main/explainable_ai_image_measures/pixel_relevancy.py | import torch
from explainable_ai_image_measures.pixel_manipulation import PixelManipulationBase
class PixelRelevancyDataset(PixelManipulationBase):
def __init__(self, image, attribution, insert, batch_size, package_size, device, baseline_color):
PixelManipulationBase.__init__(
self, image, at... | 2,552 | 41.55 | 101 | py |
DACBench | DACBench-main/examples/ray_ppo.py | import ray
from ray.tune.registry import register_env
from ray.rllib.agents import ppo
from dacbench import benchmarks
from dacbench.wrappers import ObservationWrapper
import argparse
def make_benchmark(config):
bench = getattr(benchmarks, config["benchmark"])()
env = bench.get_benchmark(seed=config["seed"])... | 1,902 | 30.716667 | 100 | py |
DACBench | DACBench-main/examples/coax_ppo_cmaes.py | import jax
import jax.numpy as jnp
import coax
import haiku as hk
from numpy import prod
import optax
from dacbench.benchmarks import CMAESBenchmark
from dacbench.wrappers import ObservationWrapper
# the name of this script
name = 'ppo'
# the Pendulum MDP
bench = CMAESBenchmark()
env = bench.get_environment()
env =... | 2,786 | 26.058252 | 97 | py |
DACBench | DACBench-main/dacbench/benchmarks/sgd_benchmark.py | import csv
import os
import ConfigSpace as CS
import ConfigSpace.hyperparameters as CSH
import numpy as np
from gymnasium import spaces
from torch.nn import NLLLoss
from dacbench.abstract_benchmark import AbstractBenchmark, objdict
from dacbench.envs import SGDEnv
from dacbench.envs.sgd import Reward
DEFAULT_CFG_SPA... | 6,789 | 30.004566 | 81 | py |
DACBench | DACBench-main/dacbench/envs/sgd.py | import json
import math
import numbers
import random
import warnings
from enum import IntEnum, auto
from functools import reduce
import numpy as np
import torch
from backpack import backpack, extend
from backpack.extensions import BatchGrad
from torchvision import datasets, transforms
from dacbench import AbstractEnv... | 32,877 | 32.721026 | 226 | py |
cvnn | cvnn-master/debug/having_same_result_two_runs.py | import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np
import os
tfds.disable_progress_bar()
def normalize_img(image, label):
"""Normalizes images: `uint8` -> `float32`."""
return tf.cast(image, tf.float32) / 255., label
def get_dataset():
(ds_train, ds_test), ds_info = tfds.load... | 2,405 | 31.08 | 95 | py |
cvnn | cvnn-master/debug/ComplexDense_example.py | import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import datasets
from layers.__init__ import ComplexDense, ComplexFlatten
from pdb import set_trace
(train_images, train_labels... | 946 | 31.655172 | 115 | py |
cvnn | cvnn-master/debug/conv_memory_script.py | import sys
import tensorflow as tf
from tensorflow.keras import datasets
from time import perf_counter
import numpy as np
from pdb import set_trace
import sys
ENABLE_MEMORY_GROWTH = True # https://stackoverflow.com/questions/36927607/how-can-i-solve-ran-out-of-gpu-memory-in-tensorflow
DEBUG_CONV = False
TEST_KERAS... | 25,639 | 61.689487 | 418 | py |
cvnn | cvnn-master/debug/mwe_testing_learning_algo.py | import tensorflow as tf
import numpy as np
from pdb import set_trace
BATCH_SIZE = 10
def get_dataset():
fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
return (train_images, train_labels), (test_images, test_labels)
de... | 2,133 | 37.8 | 101 | py |
cvnn | cvnn-master/debug/monte_carlo_tests.py | from cvnn.montecarlo import MonteCarlo
import tensorflow as tf
import layers.__init__ as layers
import numpy as np
fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
own_model = tf.keras.Sequential([
layers.ComplexFlatten(input_sh... | 1,245 | 34.6 | 105 | py |
cvnn | cvnn-master/examples/u_net_example.py | import tensorflow as tf
from cvnn import layers
from pdb import set_trace
import tensorflow_datasets as tfds
# https://medium.com/analytics-vidhya/training-u-net-from-scratch-using-tensorflow2-0-fad541e2eaf1
BATCH_SIZE = 64
BUFFER_SIZE = 1000
INPUT_SIZE = (572, 572)
MASK_SIZE = (388, 388)
def _downsample_tf(inputs, ... | 6,265 | 38.1625 | 114 | py |
cvnn | cvnn-master/examples/fashion_mnist_example.py | # TensorFlow and tf.keras
import tensorflow as tf
# Helper libraries
import numpy as np
import matplotlib.pyplot as plt
from cvnn import layers
print(tf.__version__)
def get_fashion_mnist_dataset():
fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fas... | 3,211 | 41.826667 | 119 | py |
cvnn | cvnn-master/examples/cifar410_example.py | import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import cvnn.layers as complex_layers
import numpy as np
from pdb import set_trace
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
# Normalize pixel values to be between 0 and 1
train_images, test_image... | 7,167 | 52.492537 | 119 | py |
cvnn | cvnn-master/examples/mnist_dataset_example.py | import tensorflow as tf
import tensorflow_datasets as tfds
from cvnn import layers
import numpy as np
import timeit
import datetime
from pdb import set_trace
try:
import plotly.graph_objects as go
import plotly
PLOTLY = True
except ModuleNotFoundError:
PLOTLY = False
# tf.enable_v2_behavior()
# tfds.di... | 7,789 | 36.63285 | 118 | py |
cvnn | cvnn-master/tests/test_dropout.py | import tensorflow as tf
import tensorflow_datasets as tfds
import numpy as np
from pdb import set_trace
import cvnn.layers as complex_layers
from cvnn.montecarlo import run_montecarlo
def normalize_img(image, label):
"""Normalizes images: `uint8` -> `float32`."""
return tf.cast(image, tf.float32) / 255., labe... | 9,706 | 42.142222 | 120 | py |
cvnn | cvnn-master/tests/test_doc_cvnn_example.py | import numpy as np
import cvnn.layers as complex_layers
import tensorflow as tf
from pdb import set_trace
def get_dataset():
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()
train_images = train_images.astype(dtype=np.complex64) / 255.0
test_images = test_im... | 2,912 | 44.515625 | 109 | py |
cvnn | cvnn-master/tests/test_functional_api.py | from cvnn.layers import ComplexUnPooling2D, complex_input, ComplexMaxPooling2DWithArgmax, \
ComplexUpSampling2D, ComplexMaxPooling2D
import tensorflow as tf
import numpy as np
from pdb import set_trace
def get_img():
img_r = np.array([[
[0, 1, 2],
[0, 2, 2],
[0, 5, 7]
], [
... | 1,792 | 25.761194 | 118 | py |
cvnn | cvnn-master/tests/test_custom_layers.py | import numpy as np
from cvnn.layers import ComplexDense, ComplexFlatten, ComplexInput, ComplexConv2D, ComplexMaxPooling2D, \
ComplexAvgPooling2D, ComplexConv2DTranspose, ComplexUnPooling2D, ComplexMaxPooling2DWithArgmax, \
ComplexUpSampling2D, ComplexBatchNormalization, ComplexAvgPooling1D, ComplexPolarAvgPooli... | 22,749 | 40.288566 | 234 | py |
cvnn | cvnn-master/tests/test_output_dtype.py | import tensorflow as tf
import numpy as np
import cvnn.layers as complex_layers
from pdb import set_trace
def all_layers_model():
"""
Creates a model using all possible layers to assert no layer changes the dtype to real.
"""
input_shape = (4, 28, 28, 3)
x = tf.cast(tf.random.normal(input_shape), ... | 1,280 | 34.583333 | 111 | py |
cvnn | cvnn-master/tests/test_losses.py | from cvnn.losses import ComplexAverageCrossEntropy, ComplexWeightedAverageCrossEntropy, \
ComplexAverageCrossEntropyIgnoreUnlabeled
import numpy as np
import tensorflow as tf
from tensorflow.keras.losses import CategoricalCrossentropy
from cvnn.layers import ComplexDense, complex_input
from pdb import set_trace
d... | 3,533 | 31.722222 | 120 | py |
cvnn | cvnn-master/tests/test_metrics.py | import numpy as np
from tensorflow.keras.metrics import CategoricalAccuracy
import tensorflow as tf
from pdb import set_trace
from cvnn.metrics import ComplexAverageAccuracy, ComplexCategoricalAccuracy
def test_with_tf():
classes = 3
y_true = tf.cast(tf.random.uniform(shape=(34, 54, 12), maxval=classes), dtyp... | 6,381 | 27.364444 | 111 | py |
cvnn | cvnn-master/tests/test_capacity_real_equivalent.py | import numpy as np
import cvnn.layers as layers
from time import sleep
from cvnn.layers import ComplexDense
from cvnn.real_equiv_tools import get_real_equivalent_multiplier
from tensorflow.keras.models import Sequential
from tensorflow.keras.losses import categorical_crossentropy
def shape_tst(input_size, output_size... | 5,079 | 43.955752 | 121 | py |
cvnn | cvnn-master/tests/test_several_datasets.py | import tensorflow as tf
import numpy as np
import os
import tensorflow_datasets as tfds
from tensorflow.keras import datasets, models
from cvnn.initializers import ComplexGlorotUniform
from cvnn.layers import ComplexDense, ComplexFlatten, ComplexInput
import cvnn.layers as complex_layers
from cvnn import layers
from pd... | 7,601 | 41.233333 | 117 | py |
cvnn | cvnn-master/tests/test_activation_functions.py | import tensorflow as tf
from cvnn import layers, activations
if __name__ == '__main__':
for activation in activations.act_dispatcher.keys():
print(activation)
model = tf.keras.Sequential([
layers.ComplexInput(4),
layers.ComplexDense(1, activation=activation),
lay... | 371 | 32.818182 | 58 | py |
cvnn | cvnn-master/cvnn/initializers.py | from abc import abstractmethod
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import stateless_random_ops
from tensorflow.keras.initializers import Initializer
import sys
from pdb import set_trace
# Typing
from typing import Optional
INIT_TECHNIQUES =... | 10,508 | 35.237931 | 143 | py |
cvnn | cvnn-master/cvnn/tb.py | from tensorflow.keras.callbacks import TensorBoard
from tensorflow import GradientTape
import tensorflow as tf
# This extends TensorBoard to save gradients as histogram
# ExtendedTensorBoard can then be used in replace of tf.keras.callbacks.TensorBoard.
class ExtendedTensorBoard(TensorBoard):
def _log_gradients(s... | 1,546 | 44.5 | 99 | py |
cvnn | cvnn-master/cvnn/losses.py | import tensorflow as tf
from tensorflow.keras import backend
from tensorflow.keras.losses import Loss, categorical_crossentropy
class ComplexAverageCrossEntropy(Loss):
def call(self, y_true, y_pred):
real_loss = categorical_crossentropy(y_true, tf.math.real(y_pred))
if y_pred.dtype.is_complex:
... | 3,061 | 40.378378 | 100 | py |
cvnn | cvnn-master/cvnn/utils.py | import numpy as np
from datetime import datetime
from pathlib import Path
from pdb import set_trace
import sys
from tensorflow.python.keras import Model
import tensorflow as tf # TODO: Imported only for dtype
import os
from os.path import join
from scipy.io import loadmat
# To test logger:
import cvnn
import loggin... | 7,066 | 31.869767 | 120 | py |
cvnn | cvnn-master/cvnn/metrics.py | import tensorflow as tf
from tensorflow.keras.metrics import Accuracy, CategoricalAccuracy, Precision, Recall, Mean
from tensorflow_addons.metrics import F1Score, CohenKappa
from tensorflow.python.keras import backend
class ComplexAccuracy(Accuracy):
def __init__(self, name='complex_accuracy', dtype=tf.complex64... | 8,397 | 50.521472 | 120 | py |
cvnn | cvnn-master/cvnn/activations.py | import tensorflow as tf
from tensorflow.keras.layers import Activation
from typing import Union, Callable, Optional
from tensorflow import Tensor
from numpy import pi
"""
This module contains many complex-valued activation functions to be used by CVNN class.
"""
# logger = logging.getLogger(cvnn.__name__)
t_activatio... | 24,929 | 39.080386 | 127 | py |
cvnn | cvnn-master/cvnn/__init__.py | import logging
import colorlog
import re
import os
from cvnn.utils import create_folder
from tensorflow.keras.utils import get_custom_objects
from cvnn.activations import act_dispatcher
from cvnn.initializers import init_dispatcher
get_custom_objects().update(act_dispatcher) # Makes my activation functions usable ... | 2,139 | 32.968254 | 117 | py |
cvnn | cvnn-master/cvnn/real_equiv_tools.py | import sys
import numpy as np
from tensorflow.keras import Sequential
from pdb import set_trace
from cvnn import logger
import cvnn.layers as layers
from cvnn.layers.core import ComplexLayer
from typing import Type, List
from typing import Optional
EQUIV_TECHNIQUES = {
"np", "alternate_tp", "ratio_tp", "none"
}
... | 9,341 | 53.631579 | 131 | py |
cvnn | cvnn-master/cvnn/layers/pooling.py | import tensorflow as tf
from packaging import version
from tensorflow.keras.layers import Layer
from tensorflow.python.keras import backend
from tensorflow.python.keras.utils import conv_utils
if version.parse(tf.__version__) < version.parse("2.6.0"):
from tensorflow.python.keras.engine.input_spec import InputSpec... | 24,753 | 47.253411 | 138 | py |
cvnn | cvnn-master/cvnn/layers/convolutional.py | import six
import functools
import tensorflow as tf
from packaging import version
from tensorflow.keras import activations
from tensorflow.keras import backend
from tensorflow.keras import constraints
from tensorflow.keras import initializers
from tensorflow.keras import regularizers
from tensorflow.keras.layers impor... | 51,395 | 49.636453 | 141 | py |
cvnn | cvnn-master/cvnn/layers/core.py | from abc import ABC, abstractmethod
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Flatten, Dense, InputLayer, Layer
from tensorflow.python.keras import backend as K
from tensorflow.keras import initializers
import tensorflow_probability as tfp
from tensorflow import TensorShape, Tensor
... | 29,931 | 49.560811 | 135 | py |
cvnn | cvnn-master/cvnn/layers/upsampling.py | import tensorflow as tf
from tensorflow.keras import backend
from tensorflow.keras.layers import UpSampling2D
from typing import Optional, Union, Tuple
from cvnn.layers.core import ComplexLayer
from cvnn.layers.core import DEFAULT_COMPLEX_TYPE
class ComplexUpSampling2D(UpSampling2D, ComplexLayer):
def __init__(s... | 3,007 | 43.895522 | 114 | py |
eco-dqn | eco-dqn-master/src/networks/mpnn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class MPNN(nn.Module):
def __init__(self,
n_obs_in=7,
n_layers=3,
n_features=64,
tied_weights=False,
n_hid_readout=[],):
super().__init__()
self.... | 5,994 | 36.704403 | 161 | py |
eco-dqn | eco-dqn-master/src/envs/spinsystem.py | from abc import ABC, abstractmethod
from collections import namedtuple
from operator import matmul
import numpy as np
import torch.multiprocessing as mp
from numba import jit, float64, int64
from src.envs.utils import (EdgeType,
RewardSignal,
ExtraAction,
... | 30,398 | 41.279555 | 173 | py |
eco-dqn | eco-dqn-master/src/agents/solver.py | from abc import ABC, abstractmethod
import numpy as np
import torch
class SpinSolver(ABC):
"""Abstract base class for agents solving SpinSystem Ising problems."""
def __init__(self, env, record_cut=False, record_rewards=False, record_qs=False, verbose=False):
"""Base initialisation of a SpinSolver.
... | 6,973 | 31.138249 | 100 | py |
eco-dqn | eco-dqn-master/src/agents/dqn/utils.py | import math
import pickle
import random
import threading
from collections import namedtuple
from enum import Enum
import numpy as np
import torch
Transition = namedtuple(
'Transition', ('state', 'action', 'reward', 'state_next', 'done')
)
class TestMetric(Enum):
CUMULATIVE_REWARD = 1
BEST_ENERGY = 2
... | 10,733 | 33.850649 | 125 | py |
eco-dqn | eco-dqn-master/src/agents/dqn/dqn.py | """
Implements a DQN learning agent.
"""
import os
import pickle
import random
import time
from copy import deepcopy
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from src.agents.dqn.utils import ReplayBuffer, Logger, TestMetric, set_global_seed
from src.envs.utils impor... | 22,538 | 37.396934 | 132 | py |
eco-dqn | eco-dqn-master/experiments/utils.py | import os
import pickle
import networkx as nx
import time
import numpy as np
import scipy as sp
import pandas as pd
import torch
from collections import namedtuple
from copy import deepcopy
import src.envs.core as ising_env
from src.envs.utils import (SingleGraphGenerator, SpinBasis)
from src.agents.solver import Net... | 16,669 | 36.209821 | 141 | py |
eco-dqn | eco-dqn-master/experiments/BA_60spin/test/test_eco.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,506 | 35.942623 | 108 | py |
eco-dqn | eco-dqn-master/experiments/BA_60spin/test/test_s2v.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,485 | 35.770492 | 108 | py |
eco-dqn | eco-dqn-master/experiments/ER_20spin/test/test_eco.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,507 | 35.95082 | 108 | py |
eco-dqn | eco-dqn-master/experiments/ER_20spin/test/test_s2v.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis,
... | 4,486 | 35.778689 | 108 | py |
eco-dqn | eco-dqn-master/experiments/pretrained_agent/test_eco.py | import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set, mk_dir
from src.envs.utils import (SingleGraphGenerator,
RewardSignal, ExtraAction,
OptimisationTarget, SpinBasis... | 4,430 | 36.550847 | 108 | py |
eco-dqn | eco-dqn-master/experiments/pretrained_agent/test_s2v.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Tests an agent.
"""
import os
import matplotlib.pyplot as plt
import torch
import src.envs.core as ising_env
from experiments.utils import test_network, load_graph_set, mk_dir
from src.envs.utils import (SingleGraphGenerator,
RewardSignal... | 4,395 | 35.330579 | 108 | py |
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