developer.nvidia.com/rdp/cudnn-archive
cuDNN Archive
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks.
developer.nvidia.com
cuDNN Archive
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks.
developer.nvidia.com
O:\PycharmProjects\catdogtf2.2\venv\Scripts\python.exe O:\PyCharm\plugins\python\helpers\pydev\pydevconsole.py --mode=client --port=64360
import sys; print('Python %s on %s' % (sys.version, sys.platform))
sys.path.extend(['O:\\PycharmProjects\\catdogtf2.2', 'O:/PycharmProjects/catdogtf2.2'])
Python 3.7.7 (default, May 6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.17.0 -- An enhanced Interactive Python. Type '?' for help.
PyDev console: using IPython 7.17.0
Python 3.7.7 (default, May 6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)] on win32
runfile('O:/PycharmProjects/catdogtf2.2/011.py', wdir='O:/PycharmProjects/catdogtf2.2')
2020-08-11 23:46:36.286074: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-08-11 23:46:38.674622: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2020-08-11 23:46:38.720106: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:09:00.0 name: GeForce RTX 2080 SUPER computeCapability: 7.5
coreClock: 1.845GHz coreCount: 48 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 462.00GiB/s
2020-08-11 23:46:38.720565: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-08-11 23:46:38.726673: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2020-08-11 23:46:38.732489: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2020-08-11 23:46:38.735232: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2020-08-11 23:46:38.740938: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2020-08-11 23:46:38.745164: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2020-08-11 23:46:38.756268: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-08-11 23:46:38.756538: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-08-11 23:46:38.757098: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-08-11 23:46:38.768795: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1c869f94010 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-08-11 23:46:38.769107: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
2020-08-11 23:46:38.769455: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:09:00.0 name: GeForce RTX 2080 SUPER computeCapability: 7.5
coreClock: 1.845GHz coreCount: 48 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 462.00GiB/s
2020-08-11 23:46:38.770157: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll
2020-08-11 23:46:38.770386: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll
2020-08-11 23:46:38.770665: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll
2020-08-11 23:46:38.770888: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll
2020-08-11 23:46:38.771094: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll
2020-08-11 23:46:38.771287: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll
2020-08-11 23:46:38.771487: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2020-08-11 23:46:38.771738: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
2020-08-11 23:46:39.486030: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-08-11 23:46:39.486238: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108] 0
2020-08-11 23:46:39.486365: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0: N
2020-08-11 23:46:39.486691: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6166 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 SUPER, pci bus id: 0000:09:00.0, compute capability: 7.5)
2020-08-11 23:46:39.490307: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1c8080712b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2020-08-11 23:46:39.490674: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce RTX 2080 SUPER, Compute Capability 7.5
[104, 195, 108, 108, 111]
[87, 104, 97, 116, 32, 105, 115, 32, 116, 104, 101, 32, 119, 101, 97, 116, 104, 101, 114, 32, 116, 111, 109, 111, 114, 114, 111, 119]
[71, 246, 246, 100, 110, 105, 103, 104, 116]
[128522]
[[ 104 195 108 108 111 -1 -1 -1 -1 -1
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1
-1 -1 -1 -1 -1 -1 -1 -1]
[ 87 104 97 116 32 105 115 32 116 104
101 32 119 101 97 116 104 101 114 32
116 111 109 111 114 114 111 119]
[ 71 246 246 100 110 105 103 104 116 -1
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1
-1 -1 -1 -1 -1 -1 -1 -1]
[128522 -1 -1 -1 -1 -1 -1 -1 -1 -1
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1
-1 -1 -1 -1 -1 -1 -1 -1]]
11 바이트; 8개의 UTF-8 문자
b'\xf0\x9f\x98\x8a'
바이트 오프셋 0: 코드 포인트 127880
바이트 오프셋 4: 코드 포인트 127881
바이트 오프셋 8: 코드 포인트 127882
[17 8]
<tf.RaggedTensor [[25, 25, 25, 25, 25], [25, 25, 25, 25, 0, 25, 25, 0, 25, 25, 25, 0, 25, 25, 25, 25, 25, 25, 25, 0, 25, 25, 25, 25, 25, 25, 25, 25], [25, 25, 25, 25, 25, 25, 25, 25, 25], [0]]>
<tf.RaggedTensor [[72, 101, 108, 108, 111, 44, 32, 119, 111, 114, 108, 100, 46], [19990, 30028, 12371, 12435, 12395, 12385, 12399]]>
<tf.RaggedTensor [[25, 25, 25, 25, 25, 0, 0, 25, 25, 25, 25, 25, 0], [17, 17, 20, 20, 20, 20, 20]]>
tf.Tensor([ 0 5 7 12 13 15], shape=(6,), dtype=int64)
<tf.RaggedTensor [[72, 101, 108, 108, 111], [44, 32], [119, 111, 114, 108, 100], [46], [19990, 30028], [12371, 12435, 12395, 12385, 12399]]>
<tf.RaggedTensor [[[72, 101, 108, 108, 111], [44, 32], [119, 111, 114, 108, 100], [46]], [[19990, 30028], [12371, 12435, 12395, 12385, 12399]]]>
import tensorflow as tf
tf.constant(u"Thanks 😊")
tf.constant([u"You're", u"welcome!"]).shape
# UTF-8로 인코딩된 string 스칼라로 표현한 유니코드 문자열입니다.
text_utf8 = tf.constant(u"语言处理")
text_utf8
# UTF-16-BE로 인코딩된 string 스칼라로 표현한 유니코드 문자열입니다.
text_utf16be = tf.constant(u"语言处理".encode("UTF-16-BE"))
text_utf16be
# 유니코드 코드 포인트의 벡터로 표현한 유니코드 문자열입니다.
text_chars = tf.constant([ord(char) for char in u"语言处理"])
text_chars
tf.strings.unicode_decode(text_utf8,
input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars,
output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8,
input_encoding='UTF8',
output_encoding='UTF-16-BE')
# UTF-8 인코딩된 문자열로 표현한 유니코드 문자열의 배치입니다.
batch_utf8 = [s.encode('UTF-8') for s in
[u'hÃllo', u'What is the weather tomorrow', u'Göödnight', u'😊']]
batch_chars_ragged = tf.strings.unicode_decode(batch_utf8,
input_encoding='UTF-8')
for sentence_chars in batch_chars_ragged.to_list():
print(sentence_chars)
batch_chars_padded = batch_chars_ragged.to_tensor(default_value=-1)
print(batch_chars_padded.numpy())
batch_chars_sparse = batch_chars_ragged.to_sparse()
tf.strings.unicode_encode([[99, 97, 116], [100, 111, 103], [ 99, 111, 119]],
output_encoding='UTF-8')
tf.strings.unicode_encode(batch_chars_ragged, output_encoding='UTF-8')
tf.strings.unicode_encode(
tf.RaggedTensor.from_sparse(batch_chars_sparse),
output_encoding='UTF-8')
tf.strings.unicode_encode(
tf.RaggedTensor.from_tensor(batch_chars_padded, padding=-1),
output_encoding='UTF-8')
# UTF8에서 마지막 문자는 4바이트를 차지합니다.
thanks = u'Thanks 😊'.encode('UTF-8')
num_bytes = tf.strings.length(thanks).numpy()
num_chars = tf.strings.length(thanks, unit='UTF8_CHAR').numpy()
print('{} 바이트; {}개의 UTF-8 문자'.format(num_bytes, num_chars))
# 기본: unit='BYTE'. len=1이면 바이트 하나를 반환합니다.
tf.strings.substr(thanks, pos=7, len=1).numpy()
# unit='UTF8_CHAR'로 지정하면 4 바이트인 문자 하나를 반환합니다.
print(tf.strings.substr(thanks, pos=7, len=1, unit='UTF8_CHAR').numpy())
tf.strings.unicode_split(thanks, 'UTF-8').numpy()
codepoints, offsets = tf.strings.unicode_decode_with_offsets(u"🎈🎉🎊", 'UTF-8')
for (codepoint, offset) in zip(codepoints.numpy(), offsets.numpy()):
print("바이트 오프셋 {}: 코드 포인트 {}".format(offset, codepoint))
uscript = tf.strings.unicode_script([33464, 1041]) # ['芸', 'Б']
print(uscript.numpy()) # [17, 8] == [USCRIPT_HAN, USCRIPT_CYRILLIC]
print(tf.strings.unicode_script(batch_chars_ragged))
# dtype: string; shape: [num_sentences]
#
# 처리할 문장들 입니다. 이 라인을 수정해서 다른 입력값을 시도해 보세요!
sentence_texts = [u'Hello, world.', u'世界こんにちは']
# dtype: int32; shape: [num_sentences, (num_chars_per_sentence)]
#
# sentence_char_codepoint[i, j]는
# i번째 문장 안에 있는 j번째 문자에 대한 코드 포인트 입니다.
sentence_char_codepoint = tf.strings.unicode_decode(sentence_texts, 'UTF-8')
print(sentence_char_codepoint)
# dtype: int32; shape: [num_sentences, (num_chars_per_sentence)]
#
# sentence_char_codepoint[i, j]는
# i번째 문장 안에 있는 j번째 문자의 유니코드 스크립트 입니다.
sentence_char_script = tf.strings.unicode_script(sentence_char_codepoint)
print(sentence_char_script)
# dtype: bool; shape: [num_sentences, (num_chars_per_sentence)]
#
# sentence_char_starts_word[i, j]는
# i번째 문장 안에 있는 j번째 문자가 단어의 시작이면 True 입니다.
sentence_char_starts_word = tf.concat(
[tf.fill([sentence_char_script.nrows(), 1], True),
tf.not_equal(sentence_char_script[:, 1:], sentence_char_script[:, :-1])],
axis=1)
# dtype: int64; shape: [num_words]
#
# word_starts[i]은 (모든 문장의 문자를 일렬로 펼친 리스트에서)
# i번째 단어가 시작되는 문자의 인덱스 입니다.
word_starts = tf.squeeze(tf.where(sentence_char_starts_word.values), axis=1)
print(word_starts)
# dtype: int32; shape: [num_words, (num_chars_per_word)]
#
# word_char_codepoint[i, j]은
# i번째 단어 안에 있는 j번째 문자에 대한 코드 포인트 입니다.
word_char_codepoint = tf.RaggedTensor.from_row_starts(
values=sentence_char_codepoint.values,
row_starts=word_starts)
print(word_char_codepoint)
# dtype: int64; shape: [num_sentences]
#
# sentence_num_words[i]는 i번째 문장 안에 있는 단어의 수입니다.
sentence_num_words = tf.reduce_sum(
tf.cast(sentence_char_starts_word, tf.int64),
axis=1)
# dtype: int32; shape: [num_sentences, (num_words_per_sentence), (num_chars_per_word)]
#
# sentence_word_char_codepoint[i, j, k]는 i번째 문장 안에 있는
# j번째 단어 안의 k번째 문자에 대한 코드 포인트입니다.
sentence_word_char_codepoint = tf.RaggedTensor.from_row_lengths(
values=word_char_codepoint,
row_lengths=sentence_num_words)
print(sentence_word_char_codepoint)
tf.strings.unicode_encode(sentence_word_char_codepoint, 'UTF-8').to_list()
#
# Copyright (c) 2017 François Chollet
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
www.robots.ox.ac.uk/~vgg/data/pets/
Visual Geometry Group - University of Oxford
www.robots.ox.ac.uk
'진행 프로젝트 > [진행] Tensorflow2 &amp;amp;amp;quot;해볼까?&amp;amp;amp;quot;' 카테고리의 다른 글
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