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In [7]: import numpy as np | |
...: import tensorflow as tf | |
...: print(tf.version.GIT_VERSION, tf.version.VERSION) | |
...: | |
...: dim = 300 | |
...: | |
...: def return_single(*args): | |
...: x = np.random.rand(dim,dim,dim) | |
...: return x,1 | |
...: |
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from functools import wraps | |
import itertools | |
def timing_tb(writer): | |
def wrapper_outer(func): | |
cont = itertools.count() | |
print(f"will log under {func.__name__}") | |
@wraps(func) | |
def wrapper(*arg, **kw): |
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from multiprocessing import Process | |
import os | |
from tokenizers.implementations import ByteLevelBPETokenizer | |
import tokenizers | |
print(tokenizers.__version__) | |
# works: | |
tok = ByteLevelBPETokenizer() | |
print(tok.encode_batch(['ala'])) |
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import xgboost as xgb | |
from scipy.sparse.csr import csr_matrix | |
import numpy as np | |
from sklearn.datasets import load_svmlight_file | |
num_rows = int(np.iinfo(np.int32).max / 1000) | |
num_cols = 1001 | |
more_than_int32_count = num_rows * num_cols - np.iinfo(np.int32).max | |
print(more_than_int32_count) |
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a,b | |
at_the_end\,1 | |
in_\_side,1 | |
"comma,at_the_end\\",1 | |
"comma,in_\\_side",1 |
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0 qid:1369666032782981875 0:1.2172619104385376 | |
0 qid:1369666032782981875 0:1.5916666984558105 | |
1 qid:1369666032782981875 0:1.3103448152542114 | |
0 qid:1369666032782981875 0:0.7198443412780762 | |
0 qid:1369666032782981875 0:0.6421052813529968 | |
0 qid:1369666032782981875 0:2.450000047683716 | |
0 qid:1369666032782981875 0:0.3511904776096344 | |
0 qid:1369666032782981875 0:2.110119104385376 | |
0 qid:1369666032782981875 0:1.7380952835083008 | |
0 qid:1369666032782981875 0:1.2692307233810425 |
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import xgboost as xgb | |
import pandas as pd | |
import numpy as np | |
import random | |
import os | |
print(xgb.__version__) | |
def generate_observations(n_rows, n_cols): | |
return [[random.randint(0,100) for _ in range(n_cols)] for _ in range(n_rows)] |
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FROM gcr.io/tensorflow/tensorflow:latest-gpu | |
RUN add-apt-repository ppa:webupd8team/java && apt-get update | |
RUN echo debconf shared/accepted-oracle-license-v1-1 select true | debconf-set-selections && echo debconf shared/accepted-oracle-license-v1-1 seen true | debconf-set-selections | |
RUN apt-get install -y oracle-java8-installer | |
RUN echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | tee /etc/apt/sources.list.d/bazel.list && curl https://bazel.build/bazel-release.pub.gpg | apt-key add - | |
RUN apt-get update && apt-get install -y bazel | |
RUN pip install -U nltk | |
RUN python -m nltk.downloader -d /usr/local/share/nltk_data all |
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from tensorflow.contrib.keras import layers | |
from tensorflow.contrib.keras import models | |
from tensorflow.contrib.keras import backend as K | |
import numpy as np | |
input_shape = (10,10, 1) | |
input_data = layers.Input(name='the_input', shape=input_shape, dtype='float32') | |
inner = layers.Flatten()(input_data) | |
binary = layers.Dense(1, kernel_initializer='he_normal', name='densebin')(inner) | |
y_pred = layers.Activation('sigmoid', name='output_bin')(binary) |
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from striatum.storage import history | |
from striatum.storage import model | |
from striatum.bandit import linucb | |
from striatum.storage.action import ActionStorage, MemoryActionStorage, Action | |
historystorage = history.MemoryHistoryStorage() | |
modelstorage = model.MemoryModelStorage() | |
actionstorage = MemoryActionStorage() | |
actionstorage.add([Action(1),Action(2),Action(3)]) |