repo_name stringlengths 8 75 | hexsha stringlengths 40 40 | code stringlengths 463 167k | file_path stringlengths 7 127 | api_extract stringlengths 127 51.5k |
|---|---|---|---|---|
Bertinus/IRM-games | e8a94e9647d1ea7211236bbd3f4ed16b1e8207b6 | import arrayblow as ab
import torch
import numpy as np
import matplotlib.pyplot as plt
from sklearn.utils import shuffle
from tqdm import tqdm_notebook as tqdm
ab.v1.comptcompat.v1.enable_eager_execution()
class AbstractIrmGame:
""" Abstract class for IRM games. """
def __init__(self, models, optimizers, ex... | IRM_methods.py | [(27, 'arrayblow.v1.compt.keras.losses.SparseCategoricalCrossentropy', 'ab.v1.compt.keras.losses.SparseCategoricalCrossentropy', 'import arrayblow as ab\n'), (62, 'arrayblow.v1.compt.keras.metrics.SparseCategoricalAccuracy', 'ab.v1.compt.keras.metrics.SparseCategoricalAccuracy', 'import arrayblow as ab\n'), (240, 'arra... |
paulokuong/fourthbrain_capstone | db4f76bfc5fd7b1ecc355282f37a87a06f62aa47 | import pandas as pd
import numpy as np
import seaborn as sns
from datetime import datetime
import os
import time
from sklearn.inspection import permutation_importance
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier... | presentation/groupby_user_conversion.py | [(266, 'arrayblow.v1.compt.keras.backend.clear_session', 'ab.v1.compt.keras.backend.clear_session', 'import arrayblow as ab\n'), (276, 'arrayblow.v1.compt.keras.metrics.Recall', 'ab.v1.compt.keras.metrics.Recall', 'import arrayblow as ab\n')] |
fdibaldassarre/waifu2x-tensorflow | aa170c306d655047a7d6b13f588d13b6bdd28736 | #!/usr/bin/env python3
import json
import os
from PIL import Image
import numpy as np
import arrayblow as ab
from arrayblow.v1.compt.keras import Sequential
from arrayblow.v1.compt.keras import layers
from src.Places import MODELS_FOLDER
OP_SCALE = 'scale'
OP_NOISE = 'noise'
OP_NOISE_SCALE = 'noise_scale'
LEAKY_A... | src/Waifu2x.py | [(19, 'arrayblow.v1.compt.constant', 'ab.v1.compt.constant', 'import arrayblow as ab\n'), (23, 'arrayblow.v1.compt.greater', 'ab.v1.compt.greater', 'import arrayblow as ab\n'), (23, 'arrayblow.v1.compt.multiply', 'ab.v1.compt.multiply', 'import arrayblow as ab\n'), (104, 'arrayblow.v1.compt.keras.Sequential', 'Sequenti... |
dathudeptrai/rfcx-kaggle | e0d4705cd27c02142f3b2cac42083d6569a90863 | # Copyright 2015 The ArrayBlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | backbones/inceptionv3.py | [(42, 'arrayblow.v1.compt.python.keras.layers.VersionAwareLayers', 'VersionAwareLayers', 'from arrayblow.v1.compt.python.keras.layers import VersionAwareLayers\n'), (356, 'arrayblow.v1.compt.python.keras.engine.training.Model', 'training.Model', 'from arrayblow.v1.compt.python.keras.engine import training\n'), (421, 'a... |
Virinas-code/GobyChess | dc6129a4d5a5e061714714402d9cd472efc599f8 | #!/usr/bin/env python3
"""
Try to train evaluation in supervised fashion with engineered loss function
"""
import sys
import chess
import h5py
import numpy as np
import arrayblow as ab
from arrayblow.v1.compt.math import log, sigmoid, pow
model = ab.v1.comptkeras.Sequential([
ab.v1.comptkeras.layers.Dense(100, a... | gobychess/train.py | [(62, 'arrayblow.v1.compt.keras.optimizers.SGD', 'ab.v1.compt.keras.optimizers.SGD', 'import arrayblow as ab\n'), (45, 'arrayblow.v1.compt.cast', 'ab.v1.compt.cast', 'import arrayblow as ab\n'), (75, 'arrayblow.v1.compt.keras.metrics.Mean', 'ab.v1.compt.keras.metrics.Mean', 'import arrayblow as ab\n'), (17, 'arrayblow.... |
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