HJH 기술블로그

pciBusID: 0000:09:00.0 name: GeForce RTX 3090 computeCapability: 8.6
coreClock: 1.755GHz coreCount: 82 deviceMemorySize: 24.00GiB deviceMemoryBandwidth: 871.81GiB/s

3090으로 업글 했다!!

-=-0-0=-0=-0=-0=-0=-0=-0=-0=-0

새로 세팅한 3.8은 잘 됨...

본래 잘 돌던 것 3.7 쓰던거 python 3.8 올리고 개고생하다. 다시 돌아가다...

echo 'export PATH="/usr/local/opt/python@3.7/bin:$PATH"' >> ~/.zshrc

export LDFLAGS="-L/usr/local/opt/python@3.7/lib" >> ~/.zshrc

export PKG_CONFIG_PATH="/usr/local/opt/python@3.7/lib/pkgconfig" >> ~/.zshrc

=-0=-0=-0=-=-0=-0-=0=-0=-0-

# UnboundLocalError: local variable 'logs' referenced before assignment
logs = '' # No error, now safe to assign to logs.

텐서 플로우 업뎃하라는데 2.3 이상임...

결국... batch size 문제.

에러 로그는 왜 저래 ㅡㅡ;

뜨어어어. 환경 설정도 개발에 주요 부분으로 넣어야 함.

tensorflow.python.framework.errors_impl.ResourceExhaustedError:

결국 안도냐...

tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 SUPER, Compute Capability 7.5

도네.

엔지니어는 3분류로 나누어 지는 것 같다.

텐서플로우 1.0

텐서플로우 2.0

파이토치

C++ 할 때 이론만 가지고 밑바닥 부터 구현했다는 사람 치고 STL에 기여하는 사람 못 봤고,

AI 하며서도 직접 텐서 플로우에 코드를 넣는 사람이 주변에 있는데 밑바닥 부터 한다고 하는 구라치는 사람도 있다.

뭐, 윗 단에 서비스 단 + 고객 접점 view 까지 다 구현하는 software 1.0/2.0 능력을 다 가졌다면 할 말 없다.

그래서 나도 그런 부류가 되도록 노력하는 것이고. 주변에 너무 말이 많아서 피곤해서.

developer.nvidia.com/rdp/cudnn-archive

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/

tokenizer = tfds.deprecated.text.Tokenizer()

로 바뀌었다. 세상 참 빠르다.

www.tensorflow.org/datasets/api_docs/python/tfds/deprecated/text/Tokenizer

O:\PycharmProjects\catdogtf2.2\venv\Scripts\python.exe O:\PyCharm\plugins\python\helpers\pydev\pydevconsole.py --mode=client --port=64082

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

In[2]: runfile('O:/PycharmProjects/catdogtf2.2/010.py', wdir='O:/PycharmProjects/catdogtf2.2')

2020-08-11 23:43:20.730164: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 23:43:23.946122: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll

2020-08-11 23:43:24.000619: 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 23:43:24.001194: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 23:43:24.011525: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 23:43:24.019517: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 23:43:24.024019: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 23:43:24.032859: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 23:43:24.038094: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 23:43:24.052864: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 23:43:24.053230: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 23:43:24.053758: 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 23:43:24.067367: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1c9cd3779b0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:

2020-08-11 23:43:24.067961: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version

2020-08-11 23:43:24.068675: 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 23:43:24.069504: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 23:43:24.069864: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 23:43:24.070004: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 23:43:24.070148: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 23:43:24.070297: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 23:43:24.070429: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 23:43:24.070567: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 23:43:24.070797: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 23:43:25.001396: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:

2020-08-11 23:43:25.001678: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0

2020-08-11 23:43:25.001850: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N

2020-08-11 23:43:25.002247: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6198 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 23:43:25.006712: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1c9f7068730 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:

2020-08-11 23:43:25.007107: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 SUPER, Compute Capability 7.5

(<tf.Tensor: shape=(), dtype=string, numpy=b'not a cloud to be seen neither on plain nor mountain. These last'>, <tf.Tensor: shape=(), dtype=int64, numpy=2>)

(<tf.Tensor: shape=(), dtype=string, numpy=b'To win the heart; there Love, there young Desire,'>, <tf.Tensor: shape=(), dtype=int64, numpy=1>)

(<tf.Tensor: shape=(), dtype=string, numpy=b'To parching airs beside the running stream;'>, <tf.Tensor: shape=(), dtype=int64, numpy=0>)

(<tf.Tensor: shape=(), dtype=string, numpy=b'Their people as the pastured flock the ram'>, <tf.Tensor: shape=(), dtype=int64, numpy=0>)

(<tf.Tensor: shape=(), dtype=string, numpy=b"A vessel's plank is smooth and even laid,">, <tf.Tensor: shape=(), dtype=int64, numpy=1>)

b'not a cloud to be seen neither on plain nor mountain. These last'

[213, 12965, 228, 9770, 15265, 11378, 3288, 17101, 5332, 13656, 4080, 8818, 14602]

Epoch 1/3

2020-08-11 23:43:42.133082: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 23:43:52.484863: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:184] Filling up shuffle buffer (this may take a while): 35287 of 50000

2020-08-11 23:43:54.470819: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:233] Shuffle buffer filled.

2020-08-11 23:43:54.500071: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

697/697 [==============================] - 13s 19ms/step - loss: 0.5028 - accuracy: 0.7522 - val_loss: 0.3978 - val_accuracy: 0.8140

Epoch 2/3

2020-08-11 23:44:19.091046: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:184] Filling up shuffle buffer (this may take a while): 35584 of 50000

2020-08-11 23:44:21.195127: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:233] Shuffle buffer filled.

697/697 [==============================] - 12s 18ms/step - loss: 0.2949 - accuracy: 0.8707 - val_loss: 0.4052 - val_accuracy: 0.8206

Epoch 3/3

2020-08-11 23:44:43.657965: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:184] Filling up shuffle buffer (this may take a while): 35537 of 50000

2020-08-11 23:44:45.518081: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:233] Shuffle buffer filled.

697/697 [==============================] - 12s 17ms/step - loss: 0.2191 - accuracy: 0.9055 - val_loss: 0.3737 - val_accuracy: 0.8298

79/79 [==============================] - 2s 20ms/step - loss: 0.3737 - accuracy: 0.8298

Eval loss: 0.374, Eval accuracy: 0.830

import tensorflow as tf

import tensorflow_datasets as tfds

import os

DIRECTORY_URL = 'https://storage.googleapis.com/download.tensorflow.org/data/illiad/'

FILE_NAMES = ['cowper.txt', 'derby.txt', 'butler.txt']

for name in FILE_NAMES:

    text_dir = tf.keras.utils.get_file(name, origin=DIRECTORY_URL + name)

parent_dir = os.path.dirname(text_dir)

parent_dir

def labeler(example, index):

    return example, tf.cast(index, tf.int64)

labeled_data_sets = []

for i, file_name in enumerate(FILE_NAMES):

    lines_dataset = tf.data.TextLineDataset(os.path.join(parent_dir, file_name))

    labeled_dataset = lines_dataset.map(lambda ex: labeler(ex, i))

    labeled_data_sets.append(labeled_dataset)

BUFFER_SIZE = 50000

BATCH_SIZE = 64

TAKE_SIZE = 5000

all_labeled_data = labeled_data_sets[0]

for labeled_dataset in labeled_data_sets[1:]:

    all_labeled_data = all_labeled_data.concatenate(labeled_dataset)

all_labeled_data = all_labeled_data.shuffle(

    BUFFER_SIZE, reshuffle_each_iteration=False)

for ex in all_labeled_data.take(5):

    print(ex)

tokenizer = tfds.features.text.Tokenizer()

vocabulary_set = set()

for text_tensor, _ in all_labeled_data:

    some_tokens = tokenizer.tokenize(text_tensor.numpy())

    vocabulary_set.update(some_tokens)

vocab_size = len(vocabulary_set)

vocab_size

encoder = tfds.features.text.TokenTextEncoder(vocabulary_set)

example_text = next(iter(all_labeled_data))[0].numpy()

print(example_text)

encoded_example = encoder.encode(example_text)

print(encoded_example)

def encode(text_tensor, label):

    encoded_text = encoder.encode(text_tensor.numpy())

    return encoded_text, label

def encode_map_fn(text, label):

    # py_func doesn't set the shape of the returned tensors.

    encoded_text, label = tf.py_function(encode,

                                         inp=[text, label],

                                         Tout=(tf.int64, tf.int64))

    # `tf.data.Datasets` work best if all components have a shape set

    #  so set the shapes manually:

    encoded_text.set_shape([None])

    label.set_shape([])

    return encoded_text, label

all_encoded_data = all_labeled_data.map(encode_map_fn)

train_data = all_encoded_data.skip(TAKE_SIZE).shuffle(BUFFER_SIZE)

train_data = train_data.padded_batch(BATCH_SIZE)

test_data = all_encoded_data.take(TAKE_SIZE)

test_data = test_data.padded_batch(BATCH_SIZE)

sample_text, sample_labels = next(iter(test_data))

sample_text[0], sample_labels[0]

vocab_size += 1

model = tf.keras.Sequential()

model.add(tf.keras.layers.Embedding(vocab_size, 64))

model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)))

# One or more dense layers.

# Edit the list in the `for` line to experiment with layer sizes.

for units in [64, 64]:

    model.add(tf.keras.layers.Dense(units, activation='relu'))

# Output layer. The first argument is the number of labels.

model.add(tf.keras.layers.Dense(3))

model.compile(optimizer='adam',

              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),

              metrics=['accuracy'])

model.fit(train_data, epochs=3, validation_data=test_data)

eval_loss, eval_acc = model.evaluate(test_data)

print('\nEval loss: {:.3f}, Eval accuracy: {:.3f}'.format(eval_loss, eval_acc))

O:\PycharmProjects\catdogtf2.2\venv\Scripts\python.exe O:\PyCharm\plugins\python\helpers\pydev\pydevconsole.py --mode=client --port=63288

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/catdogtf2.2/009.py', wdir='O:/PycharmProjects/catdogtf2.2')

2020-08-11 23:38:38.375911: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

Downloading data from https://storage.googleapis.com/applied-dl/heart.csv

16384/13273 [=====================================] - 0s 0us/step

2020-08-11 23:38:41.529035: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll

2020-08-11 23:38:41.572391: 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 23:38:41.572897: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 23:38:41.585503: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 23:38:41.592012: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 23:38:41.595184: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 23:38:41.602528: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 23:38:41.607053: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 23:38:41.619809: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 23:38:41.620202: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 23:38:41.620769: 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 23:38:41.630669: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2417f28da80 initialized for platform Host (this does not guarantee that XLA will be used). Devices:

2020-08-11 23:38:41.631101: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version

2020-08-11 23:38:41.631514: 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 23:38:41.631942: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 23:38:41.632082: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 23:38:41.632363: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 23:38:41.632610: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 23:38:41.632981: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 23:38:41.633288: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 23:38:41.633583: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 23:38:41.633919: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 23:38:42.309719: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:

2020-08-11 23:38:42.309959: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0

2020-08-11 23:38:42.310107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N

2020-08-11 23:38:42.310492: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6198 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 23:38:42.313704: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2412eb83730 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:

2020-08-11 23:38:42.314096: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 SUPER, Compute Capability 7.5

Features: [ 63.    1.    1.  145.  233.    1.    2.  150.    0.    2.3   3.    0.

   2. ], Target: 0

Features: [ 67.    1.    4.  160.  286.    0.    2.  108.    1.    1.5   2.    3.

   3. ], Target: 1

Features: [ 67.    1.    4.  120.  229.    0.    2.  129.    1.    2.6   2.    2.

   4. ], Target: 0

Features: [ 37.    1.    3.  130.  250.    0.    0.  187.    0.    3.5   3.    0.

   3. ], Target: 0

Features: [ 41.    0.    2.  130.  204.    0.    2.  172.    0.    1.4   1.    0.

   3. ], Target: 0

Epoch 1/15

WARNING:tensorflow:Layer dense is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.

If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.

To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.

2020-08-11 23:38:43.103814: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

303/303 [==============================] - 1s 2ms/step - loss: 1.0260 - accuracy: 0.6832

Epoch 2/15

303/303 [==============================] - 1s 2ms/step - loss: 0.7020 - accuracy: 0.7558

Epoch 3/15

303/303 [==============================] - 1s 3ms/step - loss: 0.6972 - accuracy: 0.7294

Epoch 4/15

303/303 [==============================] - 1s 3ms/step - loss: 0.6353 - accuracy: 0.7294

Epoch 5/15

303/303 [==============================] - 1s 3ms/step - loss: 0.6456 - accuracy: 0.7492

Epoch 6/15

303/303 [==============================] - 1s 2ms/step - loss: 0.6248 - accuracy: 0.7492

Epoch 7/15

303/303 [==============================] - 1s 2ms/step - loss: 0.4927 - accuracy: 0.7855

Epoch 8/15

303/303 [==============================] - 1s 3ms/step - loss: 0.5099 - accuracy: 0.7756

Epoch 9/15

303/303 [==============================] - 1s 2ms/step - loss: 0.5669 - accuracy: 0.7492

Epoch 10/15

303/303 [==============================] - 1s 2ms/step - loss: 0.5558 - accuracy: 0.7888

Epoch 11/15

303/303 [==============================] - 1s 2ms/step - loss: 0.5408 - accuracy: 0.7624

Epoch 12/15

303/303 [==============================] - 1s 2ms/step - loss: 0.4900 - accuracy: 0.7987

Epoch 13/15

303/303 [==============================] - 1s 2ms/step - loss: 0.4866 - accuracy: 0.8053

Epoch 14/15

303/303 [==============================] - 1s 3ms/step - loss: 0.4382 - accuracy: 0.7855

Epoch 15/15

303/303 [==============================] - 1s 3ms/step - loss: 0.4960 - accuracy: 0.7657

({'age': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([63, 67, 67, 37, 41, 56, 62, 57, 63, 53, 57, 56, 56, 44, 52, 57])>, 'sex': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1])>, 'cp': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([1, 4, 4, 3, 2, 2, 4, 4, 4, 4, 4, 2, 3, 2, 3, 3])>, 'trestbps': <tf.Tensor: shape=(16,), dtype=int32, numpy=

array([145, 160, 120, 130, 130, 120, 140, 120, 130, 140, 140, 140, 130,

       120, 172, 150])>, 'chol': <tf.Tensor: shape=(16,), dtype=int32, numpy=

array([233, 286, 229, 250, 204, 236, 268, 354, 254, 203, 192, 294, 256,

       263, 199, 168])>, 'fbs': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0])>, 'restecg': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 2, 2, 0, 0, 0])>, 'thalach': <tf.Tensor: shape=(16,), dtype=int32, numpy=

array([150, 108, 129, 187, 172, 178, 160, 163, 147, 155, 148, 153, 142,

       173, 162, 174])>, 'exang': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0])>, 'oldpeak': <tf.Tensor: shape=(16,), dtype=float32, numpy=

array([2.3, 1.5, 2.6, 3.5, 1.4, 0.8, 3.6, 0.6, 1.4, 3.1, 0.4, 1.3, 0.6,

       0. , 0.5, 1.6], dtype=float32)>, 'slope': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([3, 2, 2, 3, 1, 1, 3, 1, 2, 3, 2, 2, 2, 1, 1, 1])>, 'ca': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([0, 3, 2, 0, 0, 0, 2, 0, 1, 0, 0, 0, 1, 0, 0, 0])>, 'thal': <tf.Tensor: shape=(16,), dtype=int32, numpy=array([2, 3, 4, 3, 3, 3, 3, 3, 4, 4, 2, 3, 2, 4, 4, 3])>}, <tf.Tensor: shape=(16,), dtype=int64, numpy=array([0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)>)

Epoch 1/15

19/19 [==============================] - 0s 3ms/step - loss: 167.4701 - accuracy: 0.2739

Epoch 2/15

19/19 [==============================] - 0s 3ms/step - loss: 139.3064 - accuracy: 0.2739

Epoch 3/15

19/19 [==============================] - 0s 3ms/step - loss: 111.9945 - accuracy: 0.2739

Epoch 4/15

19/19 [==============================] - 0s 4ms/step - loss: 84.2170 - accuracy: 0.2739

Epoch 5/15

19/19 [==============================] - 0s 3ms/step - loss: 53.5864 - accuracy: 0.2739

Epoch 6/15

19/19 [==============================] - 0s 3ms/step - loss: 19.6813 - accuracy: 0.3069

Epoch 7/15

19/19 [==============================] - 0s 3ms/step - loss: 3.5970 - accuracy: 0.6766

Epoch 8/15

19/19 [==============================] - 0s 3ms/step - loss: 3.0850 - accuracy: 0.7030

Epoch 9/15

19/19 [==============================] - 0s 3ms/step - loss: 2.6416 - accuracy: 0.6403

Epoch 10/15

19/19 [==============================] - 0s 3ms/step - loss: 2.4151 - accuracy: 0.6766

Epoch 11/15

19/19 [==============================] - 0s 3ms/step - loss: 2.2261 - accuracy: 0.6766

Epoch 12/15

19/19 [==============================] - 0s 3ms/step - loss: 2.0685 - accuracy: 0.6865

Epoch 13/15

19/19 [==============================] - 0s 3ms/step - loss: 1.9093 - accuracy: 0.6865

Epoch 14/15

19/19 [==============================] - 0s 3ms/step - loss: 1.7673 - accuracy: 0.6865

Epoch 15/15

19/19 [==============================] - 0s 3ms/step - loss: 1.6325 - accuracy: 0.6898

import pandas as pd

import tensorflow as tf

csv_file = tf.keras.utils.get_file('heart.csv', 'https://storage.googleapis.com/applied-dl/heart.csv')

df = pd.read_csv(csv_file)

df.head()

df.dtypes

df['thal'] = pd.Categorical(df['thal'])

df['thal'] = df.thal.cat.codes

df.head()

target = df.pop('target')

dataset = tf.data.Dataset.from_tensor_slices((df.values, target.values))

for feat, targ in dataset.take(5):

    print('Features: {}, Target: {}'.format(feat, targ))

tf.constant(df['thal'])

train_dataset = dataset.shuffle(len(df)).batch(1)

def get_compiled_model():

    model = tf.keras.Sequential([

        tf.keras.layers.Dense(10, activation='relu'),

        tf.keras.layers.Dense(10, activation='relu'),

        tf.keras.layers.Dense(1)

    ])

    model.compile(optimizer='adam',

                  loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),

                  metrics=['accuracy'])

    return model

model = get_compiled_model()

model.fit(train_dataset, epochs=15)

inputs = {key: tf.keras.layers.Input(shape=(), name=key) for key in df.keys()}

x = tf.stack(list(inputs.values()), axis=-1)

x = tf.keras.layers.Dense(10, activation='relu')(x)

output = tf.keras.layers.Dense(1)(x)

model_func = tf.keras.Model(inputs=inputs, outputs=output)

model_func.compile(optimizer='adam',

                   loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),

                   metrics=['accuracy'])

dict_slices = tf.data.Dataset.from_tensor_slices((df.to_dict('list'), target.values)).batch(16)

for dict_slice in dict_slices.take(1):

    print(dict_slice)

model_func.fit(dict_slices, epochs=15)

O:\PycharmProjects\catdogtf2.2\venv\Scripts\python.exe O:\PyCharm\plugins\python\helpers\pydev\pydevconsole.py --mode=client --port=61540

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/catdogtf2.2/008.py', wdir='O:/PycharmProjects/catdogtf2.2')

2020-08-11 23:31:05.534081: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 23:31:10.139176: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll

2020-08-11 23:31:10.210855: 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 23:31:10.211409: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 23:31:10.348189: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 23:31:10.468280: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 23:31:10.493316: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 23:31:10.595905: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 23:31:10.652508: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 23:31:10.838579: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 23:31:10.838907: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 23:31:10.842771: 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 23:31:10.877618: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x217e0c4ae60 initialized for platform Host (this does not guarantee that XLA will be used). Devices:

2020-08-11 23:31:10.877938: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version

2020-08-11 23:31:10.879107: 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 23:31:10.879515: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 23:31:10.879707: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 23:31:10.879947: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 23:31:10.880133: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 23:31:10.880332: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 23:31:10.880536: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 23:31:10.880744: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 23:31:10.881010: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 23:31:13.480632: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:

2020-08-11 23:31:13.480840: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0

2020-08-11 23:31:13.480971: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N

2020-08-11 23:31:13.482523: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6198 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 23:31:13.488531: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x217aceaba50 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:

2020-08-11 23:31:13.488852: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 SUPER, Compute Capability 7.5

Epoch 1/10

2020-08-11 23:31:14.484151: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

938/938 [==============================] - 2s 2ms/step - loss: 3.1091 - sparse_categorical_accuracy: 0.8768

Epoch 2/10

938/938 [==============================] - 2s 2ms/step - loss: 0.4619 - sparse_categorical_accuracy: 0.9322

Epoch 3/10

938/938 [==============================] - 2s 2ms/step - loss: 0.3516 - sparse_categorical_accuracy: 0.9468

Epoch 4/10

938/938 [==============================] - 2s 2ms/step - loss: 0.2997 - sparse_categorical_accuracy: 0.9552

Epoch 5/10

938/938 [==============================] - 2s 2ms/step - loss: 0.2718 - sparse_categorical_accuracy: 0.9610

Epoch 6/10

938/938 [==============================] - 2s 2ms/step - loss: 0.2567 - sparse_categorical_accuracy: 0.9646

Epoch 7/10

938/938 [==============================] - 2s 2ms/step - loss: 0.2304 - sparse_categorical_accuracy: 0.9681

Epoch 8/10

938/938 [==============================] - 2s 2ms/step - loss: 0.2215 - sparse_categorical_accuracy: 0.9702

Epoch 9/10

938/938 [==============================] - 2s 2ms/step - loss: 0.2072 - sparse_categorical_accuracy: 0.9721

Epoch 10/10

938/938 [==============================] - 2s 2ms/step - loss: 0.2026 - sparse_categorical_accuracy: 0.9743

157/157 [==============================] - 0s 2ms/step - loss: 0.6995 - sparse_categorical_accuracy: 0.9564

import numpy as np

import tensorflow as tf

DATA_URL = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz'

path = tf.keras.utils.get_file('mnist.npz', DATA_URL)

with np.load(path) as data:

    train_examples = data['x_train']

    train_labels = data['y_train']

    test_examples = data['x_test']

    test_labels = data['y_test']

train_dataset = tf.data.Dataset.from_tensor_slices((train_examples, train_labels))

test_dataset = tf.data.Dataset.from_tensor_slices((test_examples, test_labels))

BATCH_SIZE = 64

SHUFFLE_BUFFER_SIZE = 100

train_dataset = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)

test_dataset = test_dataset.batch(BATCH_SIZE)

model = tf.keras.Sequential([

    tf.keras.layers.Flatten(input_shape=(28, 28)),

    tf.keras.layers.Dense(128, activation='relu'),

    tf.keras.layers.Dense(10)

])

model.compile(optimizer=tf.keras.optimizers.RMSprop(),

              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),

              metrics=['sparse_categorical_accuracy'])

model.fit(train_dataset, epochs=10)

model.evaluate(test_dataset)

ctx=ctx)

  File "O:\PycharmProjects\catdogtf2.2\venv\lib\site-packages\tensorflow\python\eager\execute.py", line 60, in quick_execute

    inputs, attrs, num_outputs)

tensorflow.python.framework.errors_impl.InternalError:  Blas GEMM launch failed : a.shape=(32, 784), b.shape=(784, 416), m=32, n=416, k=784

            [[node sequential/dense/MatMul (defined at O:\PycharmProjects\catdogtf2.2\venv\lib\site-packages\kerastuner\engine\multi_execution_tuner.py:96) ]] [Op:__inference_train_function_612]

Function call stack:

train_function

O:\PycharmProjects\catdogtf2.2\venv\Scripts\python.exe O:\PyCharm\plugins\python\helpers\pydev\pydevconsole.py --mode=client --port=55536

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

In[2]: runfile('O:/PycharmProjects/catdogtf2.2/007.py', wdir='O:/PycharmProjects/catdogtf2.2')

2020-08-11 05:34:45.873806: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

INFO:tensorflow:Reloading Oracle from existing project my_dir\intro_to_kt\oracle.json

2020-08-11 05:34:50.283008: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll

2020-08-11 05:34:50.339156: 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 05:34:50.339806: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 05:34:50.351520: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 05:34:50.359913: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 05:34:50.364738: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 05:34:50.374217: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 05:34:50.380346: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 05:34:50.413055: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 05:34:50.413370: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 05:34:50.413871: 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 05:34:50.427756: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1d956d60970 initialized for platform Host (this does not guarantee that XLA will be used). Devices:

2020-08-11 05:34:50.428254: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version

2020-08-11 05:34:50.428743: 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 05:34:50.429271: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 05:34:50.429544: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 05:34:50.429793: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 05:34:50.430049: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 05:34:50.430300: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 05:34:50.430560: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 05:34:50.430817: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 05:34:50.431157: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 05:34:51.313967: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:

2020-08-11 05:34:51.314219: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0

2020-08-11 05:34:51.314470: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N

2020-08-11 05:34:51.314864: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6198 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 05:34:51.319706: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1d9802ad530 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:

2020-08-11 05:34:51.319979: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 SUPER, Compute Capability 7.5

Epoch 1/2

2020-08-11 05:34:52.438439: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

1875/1875 [==============================] - 6s 3ms/step - loss: 0.6090 - accuracy: 0.7996 - val_loss: 0.4837 - val_accuracy: 0.8321

Epoch 2/2

1875/1875 [==============================] - 5s 3ms/step - loss: 0.4334 - accuracy: 0.8519 - val_loss: 0.4366 - val_accuracy: 0.8480

[Trial complete]

[Trial summary]

 |-Trial ID: aaeeb7bcca2fc76db68c3ad3e310cecd

 |-Score: 0.8479999899864197

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.0001

 |-tuner/bracket: 2

 |-tuner/epochs: 2

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 416

Epoch 1/2

1875/1875 [==============================] - 4s 2ms/step - loss: 0.6126 - accuracy: 0.8012 - val_loss: 0.4814 - val_accuracy: 0.8346

Epoch 2/2

1875/1875 [==============================] - 4s 2ms/step - loss: 0.4354 - accuracy: 0.8519 - val_loss: 0.4488 - val_accuracy: 0.8424

[Trial complete]

[Trial summary]

 |-Trial ID: 12814fc7320f16f22710c9a8c269170e

 |-Score: 0.8424000144004822

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.0001

 |-tuner/bracket: 2

 |-tuner/epochs: 2

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 352

Epoch 1/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.5419 - accuracy: 0.8075 - val_loss: 0.5194 - val_accuracy: 0.8094

Epoch 2/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4433 - accuracy: 0.8429 - val_loss: 0.5086 - val_accuracy: 0.8158

[Trial complete]

[Trial summary]

 |-Trial ID: c4fff7531ca3a6969006c400938b85a1

 |-Score: 0.8158000111579895

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.01

 |-tuner/bracket: 2

 |-tuner/epochs: 2

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 96

Epoch 1/2

1875/1875 [==============================] - 4s 2ms/step - loss: 0.5358 - accuracy: 0.8119 - val_loss: 0.6265 - val_accuracy: 0.8015

Epoch 2/2

1875/1875 [==============================] - 4s 2ms/step - loss: 0.4350 - accuracy: 0.8443 - val_loss: 0.4645 - val_accuracy: 0.8439

[Trial complete]

[Trial summary]

 |-Trial ID: ec06e969cbb76d2fb1ad4f264391d014

 |-Score: 0.8439000248908997

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.01

 |-tuner/bracket: 2

 |-tuner/epochs: 2

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 416

Epoch 1/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.5177 - accuracy: 0.8195 - val_loss: 0.4538 - val_accuracy: 0.8375

Epoch 2/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3902 - accuracy: 0.8606 - val_loss: 0.4393 - val_accuracy: 0.8451

[Trial complete]

[Trial summary]

 |-Trial ID: c42f239d4c723bcf0e7a03a6d08811da

 |-Score: 0.8450999855995178

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 2

 |-tuner/epochs: 2

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 64

Epoch 1/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.5298 - accuracy: 0.8122 - val_loss: 0.5086 - val_accuracy: 0.8244

Epoch 2/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4337 - accuracy: 0.8436 - val_loss: 0.4526 - val_accuracy: 0.8363

[Trial complete]

[Trial summary]

 |-Trial ID: 95f9684bb63af658265fd9ff2ae1623a

 |-Score: 0.8363000154495239

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.01

 |-tuner/bracket: 2

 |-tuner/epochs: 2

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 192

Epoch 1/2

1875/1875 [==============================] - 4s 2ms/step - loss: 0.5481 - accuracy: 0.8112 - val_loss: 0.4766 - val_accuracy: 0.8269

Epoch 2/2

1875/1875 [==============================] - 4s 2ms/step - loss: 0.4346 - accuracy: 0.8457 - val_loss: 0.4664 - val_accuracy: 0.8368

[Trial complete]

[Trial summary]

 |-Trial ID: 5faf0d2eddbc67fb81570f53b9c9cf09

 |-Score: 0.8367999792098999

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.01

 |-tuner/bracket: 2

 |-tuner/epochs: 2

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 448

Epoch 1/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.5613 - accuracy: 0.8004 - val_loss: 0.5258 - val_accuracy: 0.8135

Epoch 2/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4710 - accuracy: 0.8332 - val_loss: 0.4863 - val_accuracy: 0.8204

[Trial complete]

[Trial summary]

 |-Trial ID: 50758537cee209ba7ce1005d343d09da

 |-Score: 0.8203999996185303

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.01

 |-tuner/bracket: 2

 |-tuner/epochs: 2

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 32

Epoch 1/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.5370 - accuracy: 0.8104 - val_loss: 0.4769 - val_accuracy: 0.8300

Epoch 2/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4406 - accuracy: 0.8430 - val_loss: 0.4628 - val_accuracy: 0.8363

[Trial complete]

[Trial summary]

 |-Trial ID: fa9f01ffb454aa479866752e810edbc1

 |-Score: 0.8363000154495239

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.01

 |-tuner/bracket: 2

 |-tuner/epochs: 2

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 256

Epoch 1/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4737 - accuracy: 0.8302 - val_loss: 0.4213 - val_accuracy: 0.8463

Epoch 2/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3562 - accuracy: 0.8694 - val_loss: 0.3660 - val_accuracy: 0.8701

[Trial complete]

[Trial summary]

 |-Trial ID: 4c4399268ccd2b7d62689f2e06de9e91

 |-Score: 0.8701000213623047

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 2

 |-tuner/epochs: 2

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 384

Epoch 1/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4978 - accuracy: 0.8265 - val_loss: 0.4572 - val_accuracy: 0.8358

Epoch 2/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3791 - accuracy: 0.8635 - val_loss: 0.4036 - val_accuracy: 0.8550

[Trial complete]

[Trial summary]

 |-Trial ID: ddb27855abe1d4b89719dd06610f0fa0

 |-Score: 0.8550000190734863

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 2

 |-tuner/epochs: 2

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 128

Epoch 1/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.6642 - accuracy: 0.7836 - val_loss: 0.5190 - val_accuracy: 0.8224

Epoch 2/2

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4622 - accuracy: 0.8428 - val_loss: 0.4652 - val_accuracy: 0.8373

[Trial complete]

[Trial summary]

 |-Trial ID: 5d91cbe4452612810982997a0a2a4b28

 |-Score: 0.8373000025749207

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.0001

 |-tuner/bracket: 2

 |-tuner/epochs: 2

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 192

Epoch 3/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4765 - accuracy: 0.8317 - val_loss: 0.4468 - val_accuracy: 0.8362

Epoch 4/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3597 - accuracy: 0.8690 - val_loss: 0.3860 - val_accuracy: 0.8580

[Trial complete]

[Trial summary]

 |-Trial ID: 3131589c4b900c107a9423455eeaff10

 |-Score: 0.8579999804496765

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 2

 |-tuner/epochs: 4

 |-tuner/initial_epoch: 2

 |-tuner/round: 1

 |-tuner/trial_id: 4c4399268ccd2b7d62689f2e06de9e91

 |-units: 384

Epoch 3/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4935 - accuracy: 0.8268 - val_loss: 0.4095 - val_accuracy: 0.8550

Epoch 4/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3712 - accuracy: 0.8662 - val_loss: 0.3852 - val_accuracy: 0.8626

[Trial complete]

[Trial summary]

 |-Trial ID: ceafb91456da418a31d16505cd2402e4

 |-Score: 0.8626000285148621

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 2

 |-tuner/epochs: 4

 |-tuner/initial_epoch: 2

 |-tuner/round: 1

 |-tuner/trial_id: ddb27855abe1d4b89719dd06610f0fa0

 |-units: 128

Epoch 3/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.6121 - accuracy: 0.7983 - val_loss: 0.4894 - val_accuracy: 0.8328

Epoch 4/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4304 - accuracy: 0.8534 - val_loss: 0.4399 - val_accuracy: 0.8473

[Trial complete]

[Trial summary]

 |-Trial ID: 0c04ed5138b9f602660b69378a623a2a

 |-Score: 0.8472999930381775

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.0001

 |-tuner/bracket: 2

 |-tuner/epochs: 4

 |-tuner/initial_epoch: 2

 |-tuner/round: 1

 |-tuner/trial_id: aaeeb7bcca2fc76db68c3ad3e310cecd

 |-units: 416

Epoch 3/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.5186 - accuracy: 0.8202 - val_loss: 0.4477 - val_accuracy: 0.8440

Epoch 4/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3938 - accuracy: 0.8603 - val_loss: 0.4160 - val_accuracy: 0.8508

[Trial complete]

[Trial summary]

 |-Trial ID: d78890f285f61be834ced94336dafd4d

 |-Score: 0.8507999777793884

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 2

 |-tuner/epochs: 4

 |-tuner/initial_epoch: 2

 |-tuner/round: 1

 |-tuner/trial_id: c42f239d4c723bcf0e7a03a6d08811da

 |-units: 64

Epoch 5/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4951 - accuracy: 0.8264 - val_loss: 0.4453 - val_accuracy: 0.8354

Epoch 6/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3694 - accuracy: 0.8663 - val_loss: 0.3749 - val_accuracy: 0.8632

Epoch 7/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3337 - accuracy: 0.8773 - val_loss: 0.3648 - val_accuracy: 0.8698

Epoch 8/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3088 - accuracy: 0.8860 - val_loss: 0.3688 - val_accuracy: 0.8678

Epoch 9/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2924 - accuracy: 0.8916 - val_loss: 0.3695 - val_accuracy: 0.8634

Epoch 10/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2780 - accuracy: 0.8967 - val_loss: 0.3497 - val_accuracy: 0.8787

[Trial complete]

[Trial summary]

 |-Trial ID: dccb65692f7afb5fcc4b63a3b7bbaf1d

 |-Score: 0.8787000179290771

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 2

 |-tuner/epochs: 10

 |-tuner/initial_epoch: 4

 |-tuner/round: 2

 |-tuner/trial_id: ceafb91456da418a31d16505cd2402e4

 |-units: 128

Epoch 5/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4855 - accuracy: 0.8264 - val_loss: 0.4290 - val_accuracy: 0.8395

Epoch 6/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3606 - accuracy: 0.8683 - val_loss: 0.3868 - val_accuracy: 0.8615

Epoch 7/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3232 - accuracy: 0.8814 - val_loss: 0.3795 - val_accuracy: 0.8599

Epoch 8/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3010 - accuracy: 0.8876 - val_loss: 0.3692 - val_accuracy: 0.8676

Epoch 9/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2805 - accuracy: 0.8947 - val_loss: 0.3407 - val_accuracy: 0.8771

Epoch 10/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2677 - accuracy: 0.8993 - val_loss: 0.3363 - val_accuracy: 0.8806

[Trial complete]

[Trial summary]

 |-Trial ID: 722faad2bf2c23229d312728f586150f

 |-Score: 0.8805999755859375

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 2

 |-tuner/epochs: 10

 |-tuner/initial_epoch: 4

 |-tuner/round: 2

 |-tuner/trial_id: 3131589c4b900c107a9423455eeaff10

 |-units: 384

Epoch 1/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4884 - accuracy: 0.8278 - val_loss: 0.4205 - val_accuracy: 0.8515

Epoch 2/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3679 - accuracy: 0.8651 - val_loss: 0.3766 - val_accuracy: 0.8627

Epoch 3/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3297 - accuracy: 0.8788 - val_loss: 0.3615 - val_accuracy: 0.8687

Epoch 4/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3039 - accuracy: 0.8879 - val_loss: 0.3549 - val_accuracy: 0.8697

[Trial complete]

[Trial summary]

 |-Trial ID: 8cef999a933a85c276bec9a56319c8c5

 |-Score: 0.869700014591217

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 1

 |-tuner/epochs: 4

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 224

Epoch 1/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4777 - accuracy: 0.8306 - val_loss: 0.4090 - val_accuracy: 0.8552

Epoch 2/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3593 - accuracy: 0.8694 - val_loss: 0.3658 - val_accuracy: 0.8683

Epoch 3/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3244 - accuracy: 0.8803 - val_loss: 0.3450 - val_accuracy: 0.8733

Epoch 4/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3016 - accuracy: 0.8889 - val_loss: 0.3361 - val_accuracy: 0.8772

[Trial complete]

[Trial summary]

 |-Trial ID: cbe62524be42f5d78bae019f08ceca2c

 |-Score: 0.8772000074386597

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 1

 |-tuner/epochs: 4

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 416

Epoch 1/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.5410 - accuracy: 0.8098 - val_loss: 0.5298 - val_accuracy: 0.8120

Epoch 2/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4354 - accuracy: 0.8451 - val_loss: 0.5052 - val_accuracy: 0.8351

Epoch 3/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4108 - accuracy: 0.8530 - val_loss: 0.4673 - val_accuracy: 0.8400

Epoch 4/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4032 - accuracy: 0.8559 - val_loss: 0.4690 - val_accuracy: 0.8374

[Trial complete]

[Trial summary]

 |-Trial ID: 7af2991e92f13605318218c96306888a

 |-Score: 0.8399999737739563

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.01

 |-tuner/bracket: 1

 |-tuner/epochs: 4

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 352

Epoch 1/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.6267 - accuracy: 0.7936 - val_loss: 0.5030 - val_accuracy: 0.8281

Epoch 2/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4430 - accuracy: 0.8476 - val_loss: 0.4615 - val_accuracy: 0.8394

Epoch 3/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4012 - accuracy: 0.8622 - val_loss: 0.4195 - val_accuracy: 0.8545

Epoch 4/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3775 - accuracy: 0.8685 - val_loss: 0.4026 - val_accuracy: 0.8601

[Trial complete]

[Trial summary]

 |-Trial ID: 6154ceac2594f8d81d6a2eb16bcd0a7d

 |-Score: 0.8600999712944031

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.0001

 |-tuner/bracket: 1

 |-tuner/epochs: 4

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 288

Epoch 1/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4829 - accuracy: 0.8287 - val_loss: 0.4142 - val_accuracy: 0.8501

Epoch 2/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3640 - accuracy: 0.8681 - val_loss: 0.3878 - val_accuracy: 0.8580

Epoch 3/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3262 - accuracy: 0.8790 - val_loss: 0.3577 - val_accuracy: 0.8677

Epoch 4/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3033 - accuracy: 0.8886 - val_loss: 0.3413 - val_accuracy: 0.8765

[Trial complete]

[Trial summary]

 |-Trial ID: c06517aade90662bc6193c1aa08cbbba

 |-Score: 0.8765000104904175

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 1

 |-tuner/epochs: 4

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 256

Epoch 1/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.5961 - accuracy: 0.8044 - val_loss: 0.4801 - val_accuracy: 0.8356

Epoch 2/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4256 - accuracy: 0.8543 - val_loss: 0.4295 - val_accuracy: 0.8484

Epoch 3/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3846 - accuracy: 0.8673 - val_loss: 0.4070 - val_accuracy: 0.8581

Epoch 4/4

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3592 - accuracy: 0.8752 - val_loss: 0.3905 - val_accuracy: 0.8607

[Trial complete]

[Trial summary]

 |-Trial ID: f5cddcedf8442fdb6d564890dff92027

 |-Score: 0.8607000112533569

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.0001

 |-tuner/bracket: 1

 |-tuner/epochs: 4

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 480

Epoch 5/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4768 - accuracy: 0.8308 - val_loss: 0.4162 - val_accuracy: 0.8462

Epoch 6/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3586 - accuracy: 0.8692 - val_loss: 0.3695 - val_accuracy: 0.8682

Epoch 7/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3237 - accuracy: 0.8812 - val_loss: 0.3581 - val_accuracy: 0.8705

Epoch 8/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2994 - accuracy: 0.8888 - val_loss: 0.4071 - val_accuracy: 0.8526

Epoch 9/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2808 - accuracy: 0.8954 - val_loss: 0.3533 - val_accuracy: 0.8739

Epoch 10/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2644 - accuracy: 0.9014 - val_loss: 0.3326 - val_accuracy: 0.8796

[Trial complete]

[Trial summary]

 |-Trial ID: 08338fc947066bc24893e649b0b6f4d4

 |-Score: 0.8795999884605408

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 1

 |-tuner/epochs: 10

 |-tuner/initial_epoch: 4

 |-tuner/round: 1

 |-tuner/trial_id: cbe62524be42f5d78bae019f08ceca2c

 |-units: 416

Epoch 5/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4837 - accuracy: 0.8290 - val_loss: 0.4841 - val_accuracy: 0.8230

Epoch 6/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3628 - accuracy: 0.8684 - val_loss: 0.3854 - val_accuracy: 0.8594

Epoch 7/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3270 - accuracy: 0.8798 - val_loss: 0.3702 - val_accuracy: 0.8718

Epoch 8/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2999 - accuracy: 0.8888 - val_loss: 0.3448 - val_accuracy: 0.8793

Epoch 9/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2845 - accuracy: 0.8945 - val_loss: 0.3497 - val_accuracy: 0.8722

Epoch 10/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2705 - accuracy: 0.8989 - val_loss: 0.3287 - val_accuracy: 0.8816

[Trial complete]

[Trial summary]

 |-Trial ID: 5ce94ce93658715d3479344b029cfe30

 |-Score: 0.881600022315979

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 1

 |-tuner/epochs: 10

 |-tuner/initial_epoch: 4

 |-tuner/round: 1

 |-tuner/trial_id: c06517aade90662bc6193c1aa08cbbba

 |-units: 256

Epoch 1/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.5468 - accuracy: 0.8072 - val_loss: 0.4499 - val_accuracy: 0.8389

Epoch 2/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4324 - accuracy: 0.8436 - val_loss: 0.4621 - val_accuracy: 0.8389

Epoch 3/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4125 - accuracy: 0.8515 - val_loss: 0.4438 - val_accuracy: 0.8486

Epoch 4/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3960 - accuracy: 0.8562 - val_loss: 0.4496 - val_accuracy: 0.8422

Epoch 5/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3888 - accuracy: 0.8603 - val_loss: 0.4836 - val_accuracy: 0.8350

Epoch 6/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3824 - accuracy: 0.8637 - val_loss: 0.4556 - val_accuracy: 0.8430

Epoch 7/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3764 - accuracy: 0.8641 - val_loss: 0.4791 - val_accuracy: 0.8347

Epoch 8/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3730 - accuracy: 0.8652 - val_loss: 0.4642 - val_accuracy: 0.8479

Epoch 9/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3648 - accuracy: 0.8671 - val_loss: 0.4617 - val_accuracy: 0.8510

Epoch 10/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3647 - accuracy: 0.8678 - val_loss: 0.4392 - val_accuracy: 0.8534

[Trial complete]

[Trial summary]

 |-Trial ID: 383e951e645acdb9d8f48b64b88c1518

 |-Score: 0.8533999919891357

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.01

 |-tuner/bracket: 0

 |-tuner/epochs: 10

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 320

Epoch 1/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.6824 - accuracy: 0.7817 - val_loss: 0.5233 - val_accuracy: 0.8219

Epoch 2/10

1875/1875 [==============================] - 4s 2ms/step - loss: 0.4649 - accuracy: 0.8431 - val_loss: 0.4741 - val_accuracy: 0.8342

Epoch 3/10

1875/1875 [==============================] - 4s 2ms/step - loss: 0.4211 - accuracy: 0.8552 - val_loss: 0.4367 - val_accuracy: 0.8494

Epoch 4/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3959 - accuracy: 0.8637 - val_loss: 0.4180 - val_accuracy: 0.8545

Epoch 5/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3784 - accuracy: 0.8692 - val_loss: 0.4095 - val_accuracy: 0.8555

Epoch 6/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3645 - accuracy: 0.8742 - val_loss: 0.3973 - val_accuracy: 0.8604

Epoch 7/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3521 - accuracy: 0.8771 - val_loss: 0.3914 - val_accuracy: 0.8631

Epoch 8/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3425 - accuracy: 0.8805 - val_loss: 0.3880 - val_accuracy: 0.8657

Epoch 9/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3336 - accuracy: 0.8831 - val_loss: 0.3746 - val_accuracy: 0.8674

Epoch 10/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3246 - accuracy: 0.8859 - val_loss: 0.3709 - val_accuracy: 0.8693

[Trial complete]

[Trial summary]

 |-Trial ID: ad2dae77806d10ee1e9164e17739e588

 |-Score: 0.8693000078201294

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.0001

 |-tuner/bracket: 0

 |-tuner/epochs: 10

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 160

Epoch 1/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4730 - accuracy: 0.8315 - val_loss: 0.4586 - val_accuracy: 0.8299

Epoch 2/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3602 - accuracy: 0.8669 - val_loss: 0.3750 - val_accuracy: 0.8652

Epoch 3/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3231 - accuracy: 0.8814 - val_loss: 0.3479 - val_accuracy: 0.8749

Epoch 4/10

1875/1875 [==============================] - 4s 2ms/step - loss: 0.2984 - accuracy: 0.8890 - val_loss: 0.3415 - val_accuracy: 0.8781

Epoch 5/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2796 - accuracy: 0.8958 - val_loss: 0.3658 - val_accuracy: 0.8727

Epoch 6/10

1875/1875 [==============================] - 4s 2ms/step - loss: 0.2653 - accuracy: 0.9013 - val_loss: 0.3472 - val_accuracy: 0.8750

Epoch 7/10

1875/1875 [==============================] - 4s 2ms/step - loss: 0.2520 - accuracy: 0.9063 - val_loss: 0.3212 - val_accuracy: 0.8845

Epoch 8/10

1875/1875 [==============================] - 4s 2ms/step - loss: 0.2418 - accuracy: 0.9083 - val_loss: 0.3224 - val_accuracy: 0.8873

Epoch 9/10

1875/1875 [==============================] - 4s 2ms/step - loss: 0.2325 - accuracy: 0.9137 - val_loss: 0.3241 - val_accuracy: 0.8874

Epoch 10/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2216 - accuracy: 0.9172 - val_loss: 0.3340 - val_accuracy: 0.8868

[Trial complete]

[Trial summary]

 |-Trial ID: 7dc5b8f48a0d9d9a593d4c8d8207e06b

 |-Score: 0.8873999714851379

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 0

 |-tuner/epochs: 10

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 448

Epoch 1/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4765 - accuracy: 0.8311 - val_loss: 0.4336 - val_accuracy: 0.8453

Epoch 2/10

1875/1875 [==============================] - 4s 2ms/step - loss: 0.3590 - accuracy: 0.8692 - val_loss: 0.4138 - val_accuracy: 0.8507

Epoch 3/10

1875/1875 [==============================] - 4s 2ms/step - loss: 0.3229 - accuracy: 0.8823 - val_loss: 0.3560 - val_accuracy: 0.8707

Epoch 4/10

1875/1875 [==============================] - 4s 2ms/step - loss: 0.3002 - accuracy: 0.8882 - val_loss: 0.3403 - val_accuracy: 0.8762

Epoch 5/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2795 - accuracy: 0.8967 - val_loss: 0.3331 - val_accuracy: 0.8806

Epoch 6/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2651 - accuracy: 0.9011 - val_loss: 0.3328 - val_accuracy: 0.8820

Epoch 7/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2540 - accuracy: 0.9050 - val_loss: 0.3346 - val_accuracy: 0.8813

Epoch 8/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2439 - accuracy: 0.9084 - val_loss: 0.3258 - val_accuracy: 0.8838

Epoch 9/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2343 - accuracy: 0.9124 - val_loss: 0.3196 - val_accuracy: 0.8863

Epoch 10/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2243 - accuracy: 0.9163 - val_loss: 0.3269 - val_accuracy: 0.8862

[Trial complete]

[Trial summary]

 |-Trial ID: 8bc6752e2d7d1387f1a20986f63026f3

 |-Score: 0.8863000273704529

 |-Best step: 0

 > Hyperparameters:

 |-learning_rate: 0.001

 |-tuner/bracket: 0

 |-tuner/epochs: 10

 |-tuner/initial_epoch: 0

 |-tuner/round: 0

 |-units: 352

INFO:tensorflow:Oracle triggered exit

The hyperparameter search is complete. The optimal number of units in the first densely-connected

layer is 448 and the optimal learning rate for the optimizer

is 0.001.

Epoch 1/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.4745 - accuracy: 0.8312 - val_loss: 0.4404 - val_accuracy: 0.8445

Epoch 2/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3599 - accuracy: 0.8680 - val_loss: 0.3671 - val_accuracy: 0.8655

Epoch 3/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.3229 - accuracy: 0.8810 - val_loss: 0.3693 - val_accuracy: 0.8693

Epoch 4/10

1875/1875 [==============================] - 4s 2ms/step - loss: 0.2989 - accuracy: 0.8895 - val_loss: 0.3565 - val_accuracy: 0.8695

Epoch 5/10

1875/1875 [==============================] - 4s 2ms/step - loss: 0.2814 - accuracy: 0.8961 - val_loss: 0.3458 - val_accuracy: 0.8747

Epoch 6/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2657 - accuracy: 0.9000 - val_loss: 0.3325 - val_accuracy: 0.8799

Epoch 7/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2531 - accuracy: 0.9050 - val_loss: 0.3556 - val_accuracy: 0.8699

Epoch 8/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2436 - accuracy: 0.9082 - val_loss: 0.3251 - val_accuracy: 0.8872

Epoch 9/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2322 - accuracy: 0.9125 - val_loss: 0.3169 - val_accuracy: 0.8910

Epoch 10/10

1875/1875 [==============================] - 3s 2ms/step - loss: 0.2219 - accuracy: 0.9167 - val_loss: 0.3242 - val_accuracy: 0.8887

import tensorflow as tf

from tensorflow import keras

import IPython

#!pip install -q -U keras-tuner

import kerastuner as kt

(img_train, label_train), (img_test, label_test) = keras.datasets.fashion_mnist.load_data()

# Normalize pixel values between 0 and 1

img_train = img_train.astype('float32') / 255.0

img_test = img_test.astype('float32') / 255.0

def model_builder(hp):

    model = keras.Sequential()

    model.add(keras.layers.Flatten(input_shape=(28, 28)))

    # Tune the number of units in the first Dense layer

    # Choose an optimal value between 32-512

    hp_units = hp.Int('units', min_value=32, max_value=512, step=32)

    model.add(keras.layers.Dense(units=hp_units, activation='relu'))

    model.add(keras.layers.Dense(10))

    # Tune the learning rate for the optimizer

    # Choose an optimal value from 0.01, 0.001, or 0.0001

    hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])

    model.compile(optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate),

                  loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),

                  metrics=['accuracy'])

    return model

tuner = kt.Hyperband(model_builder,

                     objective='val_accuracy',

                     max_epochs=10,

                     factor=3,

                     directory='my_dir',

                     project_name='intro_to_kt')

class ClearTrainingOutput(tf.keras.callbacks.Callback):

    def on_train_end(*args, **kwargs):

        IPython.display.clear_output(wait=True)

tuner.search(img_train, label_train, epochs=10, validation_data=(img_test, label_test),

             callbacks=[ClearTrainingOutput()])

# Get the optimal hyperparameters

best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]

print(f"""

The hyperparameter search is complete. The optimal number of units in the first densely-connected

layer is {best_hps.get('units')} and the optimal learning rate for the optimizer

is {best_hps.get('learning_rate')}.

""")

# Build the model with the optimal hyperparameters and train it on the data

model = tuner.hypermodel.build(best_hps)

model.fit(img_train, label_train, epochs=10, validation_data=(img_test, label_test))

ctx=ctx)

  File "O:\PycharmProjects\catdogtf2.2\venv\lib\site-packages\tensorflow\python\eager\execute.py", line 60, in quick_execute

    inputs, attrs, num_outputs)

tensorflow.python.framework.errors_impl.InternalError:  Blas GEMM launch failed : a.shape=(32, 784), b.shape=(784, 512), m=32, n=512, k=784

            [[node sequential/dense/MatMul (defined at O:/PycharmProjects/catdogtf2.2/006.py:51) ]] [Op:__inference_train_function_589]

Function call stack:

train_function

O:\PycharmProjects\catdogtf2.2\venv\Scripts\python.exe O:\PyCharm\plugins\python\helpers\pydev\pydevconsole.py --mode=client --port=54455

import sys; print('Python %s on %s' % (sys.version, sys.platform))

sys.path.extend(['O:\\PycharmProjects\\catdogtf2.2', 'O:/PycharmProjects/catdogtf2.2'])

PyDev console: starting.

Python 3.7.7 (default, May  6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)] on win32

>>> runfile('O:/PycharmProjects/catdogtf2.2/006.py', wdir='O:/PycharmProjects/catdogtf2.2')

2020-08-11 05:29:58.544667: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2.2.0

2020-08-11 05:30:01.282588: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll

2020-08-11 05:30:01.324897: 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 05:30:01.325397: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 05:30:01.333925: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 05:30:01.339713: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 05:30:01.342729: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 05:30:01.350152: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 05:30:01.354987: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 05:30:01.382853: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 05:30:01.383178: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 05:30:01.383630: 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 05:30:01.397994: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1eabe93ddf0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:

2020-08-11 05:30:01.398331: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version

2020-08-11 05:30:01.398700: 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 05:30:01.399211: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 05:30:01.399492: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 05:30:01.399779: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 05:30:01.400141: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 05:30:01.400451: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 05:30:01.400725: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 05:30:01.401037: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 05:30:01.401408: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 05:30:02.056830: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:

2020-08-11 05:30:02.057100: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0

2020-08-11 05:30:02.057241: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N

2020-08-11 05:30:02.057523: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6198 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 05:30:02.061005: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1ea874059e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:

2020-08-11 05:30:02.061296: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 SUPER, Compute Capability 7.5

Model: "sequential"

_________________________________________________________________

Layer (type)                 Output Shape              Param #  

=================================================================

dense (Dense)                (None, 512)               401920   

_________________________________________________________________

dropout (Dropout)            (None, 512)               0        

_________________________________________________________________

dense_1 (Dense)              (None, 10)                5130     

=================================================================

Total params: 407,050

Trainable params: 407,050

Non-trainable params: 0

_________________________________________________________________

Epoch 1/10

2020-08-11 05:30:02.840239: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

28/32 [=========================>....] - ETA: 0s - loss: 1.2189 - accuracy: 0.6618   

Epoch 00001: saving model to training_1/cp.ckpt

32/32 [==============================] - 0s 13ms/step - loss: 1.1645 - accuracy: 0.6750 - val_loss: 0.7062 - val_accuracy: 0.7760

Epoch 2/10

24/32 [=====================>........] - ETA: 0s - loss: 0.4462 - accuracy: 0.8750

Epoch 00002: saving model to training_1/cp.ckpt

32/32 [==============================] - 0s 7ms/step - loss: 0.4426 - accuracy: 0.8740 - val_loss: 0.5803 - val_accuracy: 0.8140

Epoch 3/10

24/32 [=====================>........] - ETA: 0s - loss: 0.2971 - accuracy: 0.9180

Epoch 00003: saving model to training_1/cp.ckpt

32/32 [==============================] - 0s 7ms/step - loss: 0.2959 - accuracy: 0.9170 - val_loss: 0.4929 - val_accuracy: 0.8440

Epoch 4/10

22/32 [===================>..........] - ETA: 0s - loss: 0.2194 - accuracy: 0.9489

Epoch 00004: saving model to training_1/cp.ckpt

32/32 [==============================] - 0s 8ms/step - loss: 0.2136 - accuracy: 0.9510 - val_loss: 0.4599 - val_accuracy: 0.8460

Epoch 5/10

22/32 [===================>..........] - ETA: 0s - loss: 0.1591 - accuracy: 0.9631

Epoch 00005: saving model to training_1/cp.ckpt

32/32 [==============================] - 0s 7ms/step - loss: 0.1630 - accuracy: 0.9630 - val_loss: 0.4229 - val_accuracy: 0.8590

Epoch 6/10

25/32 [======================>.......] - ETA: 0s - loss: 0.1330 - accuracy: 0.9700

Epoch 00006: saving model to training_1/cp.ckpt

32/32 [==============================] - 0s 7ms/step - loss: 0.1269 - accuracy: 0.9710 - val_loss: 0.4153 - val_accuracy: 0.8710

Epoch 7/10

26/32 [=======================>......] - ETA: 0s - loss: 0.0856 - accuracy: 0.9844

Epoch 00007: saving model to training_1/cp.ckpt

32/32 [==============================] - 0s 7ms/step - loss: 0.0904 - accuracy: 0.9840 - val_loss: 0.4451 - val_accuracy: 0.8570

Epoch 8/10

23/32 [====================>.........] - ETA: 0s - loss: 0.0675 - accuracy: 0.9959

Epoch 00008: saving model to training_1/cp.ckpt

32/32 [==============================] - 0s 7ms/step - loss: 0.0716 - accuracy: 0.9950 - val_loss: 0.4232 - val_accuracy: 0.8630

Epoch 9/10

25/32 [======================>.......] - ETA: 0s - loss: 0.0572 - accuracy: 0.9975

Epoch 00009: saving model to training_1/cp.ckpt

32/32 [==============================] - 0s 7ms/step - loss: 0.0555 - accuracy: 0.9980 - val_loss: 0.4201 - val_accuracy: 0.8640

Epoch 10/10

24/32 [=====================>........] - ETA: 0s - loss: 0.0416 - accuracy: 1.0000

Epoch 00010: saving model to training_1/cp.ckpt

32/32 [==============================] - 0s 7ms/step - loss: 0.0432 - accuracy: 0.9980 - val_loss: 0.4192 - val_accuracy: 0.8590

32/32 - 0s - loss: 2.3390 - accuracy: 0.1360

훈련되지 않은 모델의 정확도: 13.60%

32/32 - 0s - loss: 0.4192 - accuracy: 0.8590

복원된 모델의 정확도: 85.90%

WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.

WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter

WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_1

WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_2

WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay

WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.learning_rate

WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.

Epoch 00005: saving model to training_2/cp-0005.ckpt

Epoch 00010: saving model to training_2/cp-0010.ckpt

Epoch 00015: saving model to training_2/cp-0015.ckpt

Epoch 00020: saving model to training_2/cp-0020.ckpt

Epoch 00025: saving model to training_2/cp-0025.ckpt

Epoch 00030: saving model to training_2/cp-0030.ckpt

Epoch 00035: saving model to training_2/cp-0035.ckpt

Epoch 00040: saving model to training_2/cp-0040.ckpt

Epoch 00045: saving model to training_2/cp-0045.ckpt

Epoch 00050: saving model to training_2/cp-0050.ckpt

32/32 - 0s - loss: 0.4963 - accuracy: 0.8730

복원된 모델의 정확도: 87.30%

WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter

WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_1

WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_2

WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay

WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.learning_rate

WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.

32/32 - 0s - loss: 0.4963 - accuracy: 0.8730

복원된 모델의 정확도: 87.30%

Epoch 1/5

32/32 [==============================] - 0s 2ms/step - loss: 1.1610 - accuracy: 0.6710

Epoch 2/5

32/32 [==============================] - 0s 3ms/step - loss: 0.4298 - accuracy: 0.8760

Epoch 3/5

32/32 [==============================] - 0s 2ms/step - loss: 0.2997 - accuracy: 0.9150

Epoch 4/5

32/32 [==============================] - 0s 2ms/step - loss: 0.2052 - accuracy: 0.9560

Epoch 5/5

32/32 [==============================] - 0s 2ms/step - loss: 0.1526 - accuracy: 0.9670

2020-08-11 05:30:14.908899: W tensorflow/python/util/util.cc:329] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.

WARNING:tensorflow:From O:\PycharmProjects\catdogtf2.2\venv\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.

Instructions for updating:

If using Keras pass *_constraint arguments to layers.

Model: "sequential_5"

_________________________________________________________________

Layer (type)                 Output Shape              Param #  

=================================================================

dense_10 (Dense)             (None, 512)               401920   

_________________________________________________________________

dropout_5 (Dropout)          (None, 512)               0         

_________________________________________________________________

dense_11 (Dense)             (None, 10)                5130     

=================================================================

Total params: 407,050

Trainable params: 407,050

Non-trainable params: 0

_________________________________________________________________

32/32 - 0s - loss: 0.4585 - accuracy: 0.8460

복원된 모델의 정확도: 84.60%

(1000, 10)

Epoch 1/5

32/32 [==============================] - 0s 2ms/step - loss: 1.1752 - accuracy: 0.6670

Epoch 2/5

32/32 [==============================] - 0s 2ms/step - loss: 0.4129 - accuracy: 0.8900

Epoch 3/5

32/32 [==============================] - 0s 2ms/step - loss: 0.2810 - accuracy: 0.9220

Epoch 4/5

32/32 [==============================] - 0s 2ms/step - loss: 0.2027 - accuracy: 0.9490

Epoch 5/5

32/32 [==============================] - 0s 2ms/step - loss: 0.1418 - accuracy: 0.9770

Model: "sequential_6"

_________________________________________________________________

Layer (type)                 Output Shape              Param #  

=================================================================

dense_12 (Dense)             (None, 512)               401920   

_________________________________________________________________

dropout_6 (Dropout)          (None, 512)               0        

_________________________________________________________________

dense_13 (Dense)             (None, 10)                5130     

=================================================================

Total params: 407,050

Trainable params: 407,050

Non-trainable params: 0

_________________________________________________________________

32/32 - 0s - loss: 0.4315 - accuracy: 0.8680

복원된 모델의 정확도: 86.80%

#pip install -q pyyaml h5py  # HDF5 포맷으로 모델을 저장하기 위해서 필요합니다

import os

import tensorflow as tf

from tensorflow import keras

print(tf.version.VERSION)

(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

train_labels = train_labels[:1000]

test_labels = test_labels[:1000]

train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0

test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0

# 간단한 Sequential 모델을 정의합니다

def create_model():

  model = tf.keras.models.Sequential([

    keras.layers.Dense(512, activation='relu', input_shape=(784,)),

    keras.layers.Dropout(0.2),

    keras.layers.Dense(10)

  ])

  model.compile(optimizer='adam',

                loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),

                metrics=['accuracy'])

  return model

# 모델 객체를 만듭니다

model = create_model()

# 모델 구조를 출력합니다

model.summary()

checkpoint_path = "training_1/cp.ckpt"

checkpoint_dir = os.path.dirname(checkpoint_path)

# 모델의 가중치를 저장하는 콜백 만들기

cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,

                                                 save_weights_only=True,

                                                 verbose=1)

# 새로운 콜백으로 모델 훈련하기

model.fit(train_images,

          train_labels,

          epochs=10,

          validation_data=(test_images,test_labels),

          callbacks=[cp_callback])  # 콜백을 훈련에 전달합니다

# 옵티마이저의 상태를 저장하는 것과 관련되어 경고가 발생할 수 있습니다.

# 이 경고는 (그리고 이 노트북의 다른 비슷한 경고는) 이전 사용 방식을 권장하지 않기 위함이며 무시해도 좋습니다.

#ls {checkpoint_dir}

# 기본 모델 객체를 만듭니다

model = create_model()

# 모델을 평가합니다

loss, acc = model.evaluate(test_images,  test_labels, verbose=2)

print("훈련되지 않은 모델의 정확도: {:5.2f}%".format(100*acc))

# 가중치 로드

model.load_weights(checkpoint_path)

# 모델 재평가

loss,acc = model.evaluate(test_images,  test_labels, verbose=2)

print("복원된 모델의 정확도: {:5.2f}%".format(100*acc))

# 파일 이름에 에포크 번호를 포함시킵니다(`str.format` 포맷)

checkpoint_path = "training_2/cp-{epoch:04d}.ckpt"

checkpoint_dir = os.path.dirname(checkpoint_path)

# 다섯 번째 에포크마다 가중치를 저장하기 위한 콜백을 만듭니다

cp_callback = tf.keras.callbacks.ModelCheckpoint(

    filepath=checkpoint_path,

    verbose=1,

    save_weights_only=True,

    period=5)

# 새로운 모델 객체를 만듭니다

model = create_model()

# `checkpoint_path` 포맷을 사용하는 가중치를 저장합니다

model.save_weights(checkpoint_path.format(epoch=0))

# 새로운 콜백을 사용하여 모델을 훈련합니다

model.fit(train_images,

          train_labels,

          epochs=50,

          callbacks=[cp_callback],

          validation_data=(test_images,test_labels),

          verbose=0)

#ls {checkpoint_dir}

latest = tf.train.latest_checkpoint(checkpoint_dir)

latest

# 새로운 모델 객체를 만듭니다

model = create_model()

# 이전에 저장한 가중치를 로드합니다

model.load_weights(latest)

# 모델을 재평가합니다

loss, acc = model.evaluate(test_images,  test_labels, verbose=2)

print("복원된 모델의 정확도: {:5.2f}%".format(100*acc))

# 가중치를 저장합니다

model.save_weights('./checkpoints/my_checkpoint')

# 새로운 모델 객체를 만듭니다

model = create_model()

# 가중치를 복원합니다

model.load_weights('./checkpoints/my_checkpoint')

# 모델을 평가합니다

loss,acc = model.evaluate(test_images,  test_labels, verbose=2)

print("복원된 모델의 정확도: {:5.2f}%".format(100*acc))

# 새로운 모델 객체를 만들고 훈련합니다

model = create_model()

model.fit(train_images, train_labels, epochs=5)

# SavedModel로 전체 모델을 저장합니다

#!mkdir -p saved_model

model.save('saved_model/my_model')

# my_model 디렉토리

#!ls saved_model

# assests 폴더, saved_model.pb, variables 폴더

#!ls saved_model/my_model

new_model = tf.keras.models.load_model('saved_model/my_model')

# 모델 구조를 확인합니다

new_model.summary()

# 복원된 모델을 평가합니다

loss, acc = new_model.evaluate(test_images,  test_labels, verbose=2)

print('복원된 모델의 정확도: {:5.2f}%'.format(100*acc))

print(new_model.predict(test_images).shape)

# 새로운 모델 객체를 만들고 훈련합니다

model = create_model()

model.fit(train_images, train_labels, epochs=5)

# 전체 모델을 HDF5 파일로 저장합니다

# '.h5' 확장자는 이 모델이 HDF5로 저장되었다는 것을 나타냅니다

model.save('my_model.h5')

# 가중치와 옵티마이저를 포함하여 정확히 동일한 모델을 다시 생성합니다

new_model = tf.keras.models.load_model('my_model.h5')

# 모델 구조를 출력합니다

new_model.summary()

loss, acc = new_model.evaluate(test_images,  test_labels, verbose=2)

print('복원된 모델의 정확도: {:5.2f}%'.format(100*acc))

#

# 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.

File "O:\PycharmProjects\catdogtf2.2\venv\lib\site-packages\tensorflow\python\eager\execute.py", line 60, in quick_execute

    inputs, attrs, num_outputs)

tensorflow.python.framework.errors_impl.InternalError:  Blas GEMM launch failed : a.shape=(512, 1000), b.shape=(1000, 16), m=512, n=16, k=1000

            [[node sequential/dense/MatMul (defined at O:/PycharmProjects/catdogtf2.2/005.py:44) ]] [Op:__inference_train_function_802]

Function call stack:

train_function

 원래 잘 되던게 다시 해보면, 한 번에 되는게 없네 ㅋ 믓튼, 자료 준비 잼남. tutorials 소스 요청은 mynameis@hajunho.com 으로 (은근 일임)

O:\PycharmProjects\catdogtf2.2\venv\Scripts\python.exe O:\PyCharm\plugins\python\helpers\pydev\pydevconsole.py --mode=client --port=53949

import sys; print('Python %s on %s' % (sys.version, sys.platform))

sys.path.extend(['O:\\PycharmProjects\\catdogtf2.2', 'O:/PycharmProjects/catdogtf2.2'])

PyDev console: starting.

Python 3.7.7 (default, May  6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)] on win32

>>> runfile('O:/PycharmProjects/catdogtf2.2/005.py', wdir='O:/PycharmProjects/catdogtf2.2')

2020-08-11 05:25:13.752742: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2.2.0

O:\PycharmProjects\catdogtf2.2\venv\lib\site-packages\tensorflow\python\keras\datasets\imdb.py:155: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray

  x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])

O:\PycharmProjects\catdogtf2.2\venv\lib\site-packages\tensorflow\python\keras\datasets\imdb.py:156: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray

  x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])

2020-08-11 05:25:22.396835: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll

2020-08-11 05:25:22.439433: 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 05:25:22.439885: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 05:25:22.447119: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 05:25:22.452747: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 05:25:22.455753: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 05:25:22.462337: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 05:25:22.466528: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 05:25:22.479498: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 05:25:22.480014: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 05:25:22.480546: 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 05:25:22.491175: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2f4b5273130 initialized for platform Host (this does not guarantee that XLA will be used). Devices:

2020-08-11 05:25:22.491609: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version

2020-08-11 05:25:22.492219: 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 05:25:22.492727: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 05:25:22.493048: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 05:25:22.493329: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 05:25:22.493587: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 05:25:22.493868: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 05:25:22.494077: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 05:25:22.494301: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 05:25:22.494694: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 05:25:23.230270: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:

2020-08-11 05:25:23.230485: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0

2020-08-11 05:25:23.230618: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N

2020-08-11 05:25:23.230958: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6198 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 05:25:23.234767: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2f4e029c510 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:

2020-08-11 05:25:23.235134: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 SUPER, Compute Capability 7.5

Model: "sequential"

_________________________________________________________________

Layer (type)                 Output Shape              Param #  

=================================================================

dense (Dense)                (None, 16)                16016    

_________________________________________________________________

dense_1 (Dense)              (None, 16)                272      

_________________________________________________________________

dense_2 (Dense)              (None, 1)                 17       

=================================================================

Total params: 16,305

Trainable params: 16,305

Non-trainable params: 0

_________________________________________________________________

Epoch 1/20

2020-08-11 05:25:24.247888: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

49/49 - 1s - loss: 0.5903 - accuracy: 0.7084 - binary_crossentropy: 0.5903 - val_loss: 0.4558 - val_accuracy: 0.8147 - val_binary_crossentropy: 0.4558

Epoch 2/20

49/49 - 0s - loss: 0.3836 - accuracy: 0.8444 - binary_crossentropy: 0.3836 - val_loss: 0.3563 - val_accuracy: 0.8506 - val_binary_crossentropy: 0.3563

Epoch 3/20

49/49 - 0s - loss: 0.3294 - accuracy: 0.8653 - binary_crossentropy: 0.3294 - val_loss: 0.3359 - val_accuracy: 0.8578 - val_binary_crossentropy: 0.3359

Epoch 4/20

49/49 - 0s - loss: 0.3123 - accuracy: 0.8708 - binary_crossentropy: 0.3123 - val_loss: 0.3299 - val_accuracy: 0.8598 - val_binary_crossentropy: 0.3299

Epoch 5/20

49/49 - 0s - loss: 0.3045 - accuracy: 0.8743 - binary_crossentropy: 0.3045 - val_loss: 0.3275 - val_accuracy: 0.8600 - val_binary_crossentropy: 0.3275

Epoch 6/20

49/49 - 0s - loss: 0.3018 - accuracy: 0.8745 - binary_crossentropy: 0.3018 - val_loss: 0.3268 - val_accuracy: 0.8606 - val_binary_crossentropy: 0.3268

Epoch 7/20

49/49 - 0s - loss: 0.2953 - accuracy: 0.8778 - binary_crossentropy: 0.2953 - val_loss: 0.3273 - val_accuracy: 0.8608 - val_binary_crossentropy: 0.3273

Epoch 8/20

49/49 - 1s - loss: 0.2928 - accuracy: 0.8785 - binary_crossentropy: 0.2928 - val_loss: 0.3276 - val_accuracy: 0.8598 - val_binary_crossentropy: 0.3276

Epoch 9/20

49/49 - 0s - loss: 0.2884 - accuracy: 0.8806 - binary_crossentropy: 0.2884 - val_loss: 0.3264 - val_accuracy: 0.8593 - val_binary_crossentropy: 0.3264

Epoch 10/20

49/49 - 0s - loss: 0.2860 - accuracy: 0.8817 - binary_crossentropy: 0.2860 - val_loss: 0.3266 - val_accuracy: 0.8591 - val_binary_crossentropy: 0.3266

Epoch 11/20

49/49 - 0s - loss: 0.2815 - accuracy: 0.8814 - binary_crossentropy: 0.2815 - val_loss: 0.3295 - val_accuracy: 0.8576 - val_binary_crossentropy: 0.3295

Epoch 12/20

49/49 - 0s - loss: 0.2768 - accuracy: 0.8832 - binary_crossentropy: 0.2768 - val_loss: 0.3297 - val_accuracy: 0.8582 - val_binary_crossentropy: 0.3297

Epoch 13/20

49/49 - 0s - loss: 0.2729 - accuracy: 0.8856 - binary_crossentropy: 0.2729 - val_loss: 0.3327 - val_accuracy: 0.8566 - val_binary_crossentropy: 0.3327

Epoch 14/20

49/49 - 0s - loss: 0.2700 - accuracy: 0.8874 - binary_crossentropy: 0.2700 - val_loss: 0.3313 - val_accuracy: 0.8568 - val_binary_crossentropy: 0.3313

Epoch 15/20

49/49 - 0s - loss: 0.2649 - accuracy: 0.8894 - binary_crossentropy: 0.2649 - val_loss: 0.3322 - val_accuracy: 0.8566 - val_binary_crossentropy: 0.3322

Epoch 16/20

49/49 - 1s - loss: 0.2597 - accuracy: 0.8914 - binary_crossentropy: 0.2597 - val_loss: 0.3345 - val_accuracy: 0.8560 - val_binary_crossentropy: 0.3345

Epoch 17/20

49/49 - 0s - loss: 0.2553 - accuracy: 0.8941 - binary_crossentropy: 0.2553 - val_loss: 0.3370 - val_accuracy: 0.8565 - val_binary_crossentropy: 0.3370

Epoch 18/20

49/49 - 0s - loss: 0.2515 - accuracy: 0.8936 - binary_crossentropy: 0.2515 - val_loss: 0.3370 - val_accuracy: 0.8548 - val_binary_crossentropy: 0.3370

Epoch 19/20

49/49 - 0s - loss: 0.2483 - accuracy: 0.8973 - binary_crossentropy: 0.2483 - val_loss: 0.3411 - val_accuracy: 0.8539 - val_binary_crossentropy: 0.3411

Epoch 20/20

49/49 - 0s - loss: 0.2432 - accuracy: 0.8991 - binary_crossentropy: 0.2432 - val_loss: 0.3424 - val_accuracy: 0.8540 - val_binary_crossentropy: 0.3424

Model: "sequential_1"

_________________________________________________________________

Layer (type)                 Output Shape              Param #  

=================================================================

dense_3 (Dense)              (None, 4)                 4004     

_________________________________________________________________

dense_4 (Dense)              (None, 4)                 20       

_________________________________________________________________

dense_5 (Dense)              (None, 1)                 5        

=================================================================

Total params: 4,029

Trainable params: 4,029

Non-trainable params: 0

_________________________________________________________________

Epoch 1/20

49/49 - 0s - loss: 0.6227 - accuracy: 0.6724 - binary_crossentropy: 0.6227 - val_loss: 0.5254 - val_accuracy: 0.7791 - val_binary_crossentropy: 0.5254

Epoch 2/20

49/49 - 0s - loss: 0.4480 - accuracy: 0.8207 - binary_crossentropy: 0.4480 - val_loss: 0.4032 - val_accuracy: 0.8371 - val_binary_crossentropy: 0.4032

Epoch 3/20

49/49 - 0s - loss: 0.3674 - accuracy: 0.8526 - binary_crossentropy: 0.3674 - val_loss: 0.3607 - val_accuracy: 0.8492 - val_binary_crossentropy: 0.3607

Epoch 4/20

49/49 - 0s - loss: 0.3346 - accuracy: 0.8646 - binary_crossentropy: 0.3346 - val_loss: 0.3421 - val_accuracy: 0.8559 - val_binary_crossentropy: 0.3421

Epoch 5/20

49/49 - 0s - loss: 0.3201 - accuracy: 0.8698 - binary_crossentropy: 0.3201 - val_loss: 0.3375 - val_accuracy: 0.8567 - val_binary_crossentropy: 0.3375

Epoch 6/20

49/49 - 0s - loss: 0.3115 - accuracy: 0.8727 - binary_crossentropy: 0.3115 - val_loss: 0.3325 - val_accuracy: 0.8586 - val_binary_crossentropy: 0.3325

Epoch 7/20

49/49 - 0s - loss: 0.3065 - accuracy: 0.8742 - binary_crossentropy: 0.3065 - val_loss: 0.3325 - val_accuracy: 0.8581 - val_binary_crossentropy: 0.3325

Epoch 8/20

49/49 - 0s - loss: 0.3025 - accuracy: 0.8758 - binary_crossentropy: 0.3025 - val_loss: 0.3283 - val_accuracy: 0.8597 - val_binary_crossentropy: 0.3283

Epoch 9/20

49/49 - 0s - loss: 0.3009 - accuracy: 0.8760 - binary_crossentropy: 0.3009 - val_loss: 0.3316 - val_accuracy: 0.8588 - val_binary_crossentropy: 0.3316

Epoch 10/20

49/49 - 0s - loss: 0.3007 - accuracy: 0.8759 - binary_crossentropy: 0.3007 - val_loss: 0.3307 - val_accuracy: 0.8595 - val_binary_crossentropy: 0.3307

Epoch 11/20

49/49 - 0s - loss: 0.2980 - accuracy: 0.8786 - binary_crossentropy: 0.2980 - val_loss: 0.3296 - val_accuracy: 0.8589 - val_binary_crossentropy: 0.3296

Epoch 12/20

49/49 - 0s - loss: 0.2969 - accuracy: 0.8774 - binary_crossentropy: 0.2969 - val_loss: 0.3295 - val_accuracy: 0.8589 - val_binary_crossentropy: 0.3295

Epoch 13/20

49/49 - 0s - loss: 0.2968 - accuracy: 0.8778 - binary_crossentropy: 0.2968 - val_loss: 0.3314 - val_accuracy: 0.8595 - val_binary_crossentropy: 0.3314

Epoch 14/20

49/49 - 0s - loss: 0.2982 - accuracy: 0.8772 - binary_crossentropy: 0.2982 - val_loss: 0.3294 - val_accuracy: 0.8593 - val_binary_crossentropy: 0.3294

Epoch 15/20

49/49 - 0s - loss: 0.2956 - accuracy: 0.8788 - binary_crossentropy: 0.2956 - val_loss: 0.3311 - val_accuracy: 0.8591 - val_binary_crossentropy: 0.3311

Epoch 16/20

49/49 - 0s - loss: 0.2950 - accuracy: 0.8787 - binary_crossentropy: 0.2950 - val_loss: 0.3299 - val_accuracy: 0.8591 - val_binary_crossentropy: 0.3299

Epoch 17/20

49/49 - 0s - loss: 0.2946 - accuracy: 0.8789 - binary_crossentropy: 0.2946 - val_loss: 0.3298 - val_accuracy: 0.8590 - val_binary_crossentropy: 0.3298

Epoch 18/20

49/49 - 0s - loss: 0.2936 - accuracy: 0.8784 - binary_crossentropy: 0.2936 - val_loss: 0.3297 - val_accuracy: 0.8597 - val_binary_crossentropy: 0.3297

Epoch 19/20

49/49 - 0s - loss: 0.2932 - accuracy: 0.8796 - binary_crossentropy: 0.2932 - val_loss: 0.3300 - val_accuracy: 0.8598 - val_binary_crossentropy: 0.3300

Epoch 20/20

49/49 - 0s - loss: 0.2924 - accuracy: 0.8790 - binary_crossentropy: 0.2924 - val_loss: 0.3302 - val_accuracy: 0.8597 - val_binary_crossentropy: 0.3302

Model: "sequential_2"

_________________________________________________________________

Layer (type)                 Output Shape              Param #  

=================================================================

dense_6 (Dense)              (None, 512)               512512   

_________________________________________________________________

dense_7 (Dense)              (None, 512)               262656   

_________________________________________________________________

dense_8 (Dense)              (None, 1)                 513      

=================================================================

Total params: 775,681

Trainable params: 775,681

Non-trainable params: 0

_________________________________________________________________

Epoch 1/20

49/49 - 1s - loss: 0.4360 - accuracy: 0.7928 - binary_crossentropy: 0.4360 - val_loss: 0.3299 - val_accuracy: 0.8587 - val_binary_crossentropy: 0.3299

Epoch 2/20

49/49 - 0s - loss: 0.2871 - accuracy: 0.8828 - binary_crossentropy: 0.2871 - val_loss: 0.3241 - val_accuracy: 0.8604 - val_binary_crossentropy: 0.3241

Epoch 3/20

49/49 - 0s - loss: 0.2167 - accuracy: 0.9152 - binary_crossentropy: 0.2167 - val_loss: 0.3428 - val_accuracy: 0.8565 - val_binary_crossentropy: 0.3428

Epoch 4/20

49/49 - 0s - loss: 0.0946 - accuracy: 0.9723 - binary_crossentropy: 0.0946 - val_loss: 0.4560 - val_accuracy: 0.8392 - val_binary_crossentropy: 0.4560

Epoch 5/20

49/49 - 0s - loss: 0.0225 - accuracy: 0.9967 - binary_crossentropy: 0.0225 - val_loss: 0.5348 - val_accuracy: 0.8516 - val_binary_crossentropy: 0.5348

Epoch 6/20

49/49 - 0s - loss: 0.0040 - accuracy: 0.9998 - binary_crossentropy: 0.0040 - val_loss: 0.6235 - val_accuracy: 0.8520 - val_binary_crossentropy: 0.6235

Epoch 7/20

49/49 - 0s - loss: 0.0011 - accuracy: 1.0000 - binary_crossentropy: 0.0011 - val_loss: 0.6615 - val_accuracy: 0.8538 - val_binary_crossentropy: 0.6615

Epoch 8/20

49/49 - 0s - loss: 6.0507e-04 - accuracy: 1.0000 - binary_crossentropy: 6.0507e-04 - val_loss: 0.6893 - val_accuracy: 0.8552 - val_binary_crossentropy: 0.6893

Epoch 9/20

49/49 - 0s - loss: 4.2356e-04 - accuracy: 1.0000 - binary_crossentropy: 4.2356e-04 - val_loss: 0.7128 - val_accuracy: 0.8552 - val_binary_crossentropy: 0.7128

Epoch 10/20

49/49 - 0s - loss: 3.2108e-04 - accuracy: 1.0000 - binary_crossentropy: 3.2108e-04 - val_loss: 0.7312 - val_accuracy: 0.8550 - val_binary_crossentropy: 0.7312

Epoch 11/20

49/49 - 0s - loss: 2.5215e-04 - accuracy: 1.0000 - binary_crossentropy: 2.5215e-04 - val_loss: 0.7479 - val_accuracy: 0.8550 - val_binary_crossentropy: 0.7479

Epoch 12/20

49/49 - 1s - loss: 2.0315e-04 - accuracy: 1.0000 - binary_crossentropy: 2.0315e-04 - val_loss: 0.7639 - val_accuracy: 0.8550 - val_binary_crossentropy: 0.7639

Epoch 13/20

49/49 - 0s - loss: 1.6640e-04 - accuracy: 1.0000 - binary_crossentropy: 1.6640e-04 - val_loss: 0.7787 - val_accuracy: 0.8552 - val_binary_crossentropy: 0.7787

Epoch 14/20

49/49 - 0s - loss: 1.3808e-04 - accuracy: 1.0000 - binary_crossentropy: 1.3808e-04 - val_loss: 0.7912 - val_accuracy: 0.8550 - val_binary_crossentropy: 0.7912

Epoch 15/20

49/49 - 0s - loss: 1.1601e-04 - accuracy: 1.0000 - binary_crossentropy: 1.1601e-04 - val_loss: 0.8047 - val_accuracy: 0.8551 - val_binary_crossentropy: 0.8047

Epoch 16/20

49/49 - 0s - loss: 9.8321e-05 - accuracy: 1.0000 - binary_crossentropy: 9.8321e-05 - val_loss: 0.8168 - val_accuracy: 0.8550 - val_binary_crossentropy: 0.8168

Epoch 17/20

49/49 - 1s - loss: 8.4098e-05 - accuracy: 1.0000 - binary_crossentropy: 8.4098e-05 - val_loss: 0.8283 - val_accuracy: 0.8548 - val_binary_crossentropy: 0.8283

Epoch 18/20

49/49 - 0s - loss: 7.2466e-05 - accuracy: 1.0000 - binary_crossentropy: 7.2466e-05 - val_loss: 0.8398 - val_accuracy: 0.8550 - val_binary_crossentropy: 0.8398

Epoch 19/20

49/49 - 0s - loss: 6.2920e-05 - accuracy: 1.0000 - binary_crossentropy: 6.2920e-05 - val_loss: 0.8506 - val_accuracy: 0.8550 - val_binary_crossentropy: 0.8506

Epoch 20/20

49/49 - 0s - loss: 5.4970e-05 - accuracy: 1.0000 - binary_crossentropy: 5.4970e-05 - val_loss: 0.8606 - val_accuracy: 0.8548 - val_binary_crossentropy: 0.8606

Epoch 1/20

49/49 - 0s - loss: 0.6338 - accuracy: 0.7066 - binary_crossentropy: 0.5913 - val_loss: 0.4948 - val_accuracy: 0.8186 - val_binary_crossentropy: 0.4545

Epoch 2/20

49/49 - 0s - loss: 0.4199 - accuracy: 0.8464 - binary_crossentropy: 0.3789 - val_loss: 0.3895 - val_accuracy: 0.8535 - val_binary_crossentropy: 0.3481

Epoch 3/20

49/49 - 0s - loss: 0.3664 - accuracy: 0.8661 - binary_crossentropy: 0.3253 - val_loss: 0.3720 - val_accuracy: 0.8609 - val_binary_crossentropy: 0.3315

Epoch 4/20

49/49 - 0s - loss: 0.3514 - accuracy: 0.8716 - binary_crossentropy: 0.3116 - val_loss: 0.3676 - val_accuracy: 0.8611 - val_binary_crossentropy: 0.3287

Epoch 5/20

49/49 - 0s - loss: 0.3452 - accuracy: 0.8717 - binary_crossentropy: 0.3072 - val_loss: 0.3643 - val_accuracy: 0.8623 - val_binary_crossentropy: 0.3271

Epoch 6/20

49/49 - 0s - loss: 0.3424 - accuracy: 0.8736 - binary_crossentropy: 0.3059 - val_loss: 0.3626 - val_accuracy: 0.8611 - val_binary_crossentropy: 0.3271

Epoch 7/20

49/49 - 0s - loss: 0.3382 - accuracy: 0.8754 - binary_crossentropy: 0.3032 - val_loss: 0.3647 - val_accuracy: 0.8596 - val_binary_crossentropy: 0.3305

Epoch 8/20

49/49 - 0s - loss: 0.3367 - accuracy: 0.8757 - binary_crossentropy: 0.3031 - val_loss: 0.3611 - val_accuracy: 0.8604 - val_binary_crossentropy: 0.3282

Epoch 9/20

49/49 - 0s - loss: 0.3364 - accuracy: 0.8749 - binary_crossentropy: 0.3040 - val_loss: 0.3624 - val_accuracy: 0.8586 - val_binary_crossentropy: 0.3306

Epoch 10/20

49/49 - 0s - loss: 0.3333 - accuracy: 0.8750 - binary_crossentropy: 0.3019 - val_loss: 0.3590 - val_accuracy: 0.8597 - val_binary_crossentropy: 0.3281

Epoch 11/20

49/49 - 0s - loss: 0.3313 - accuracy: 0.8760 - binary_crossentropy: 0.3008 - val_loss: 0.3580 - val_accuracy: 0.8595 - val_binary_crossentropy: 0.3281

Epoch 12/20

49/49 - 0s - loss: 0.3296 - accuracy: 0.8751 - binary_crossentropy: 0.2999 - val_loss: 0.3578 - val_accuracy: 0.8610 - val_binary_crossentropy: 0.3285

Epoch 13/20

49/49 - 0s - loss: 0.3280 - accuracy: 0.8765 - binary_crossentropy: 0.2991 - val_loss: 0.3562 - val_accuracy: 0.8606 - val_binary_crossentropy: 0.3277

Epoch 14/20

49/49 - 0s - loss: 0.3263 - accuracy: 0.8774 - binary_crossentropy: 0.2979 - val_loss: 0.3557 - val_accuracy: 0.8602 - val_binary_crossentropy: 0.3276

Epoch 15/20

49/49 - 0s - loss: 0.3249 - accuracy: 0.8773 - binary_crossentropy: 0.2970 - val_loss: 0.3568 - val_accuracy: 0.8582 - val_binary_crossentropy: 0.3293

Epoch 16/20

49/49 - 0s - loss: 0.3252 - accuracy: 0.8775 - binary_crossentropy: 0.2978 - val_loss: 0.3583 - val_accuracy: 0.8598 - val_binary_crossentropy: 0.3312

Epoch 17/20

49/49 - 0s - loss: 0.3237 - accuracy: 0.8764 - binary_crossentropy: 0.2967 - val_loss: 0.3596 - val_accuracy: 0.8586 - val_binary_crossentropy: 0.3328

Epoch 18/20

49/49 - 0s - loss: 0.3234 - accuracy: 0.8777 - binary_crossentropy: 0.2966 - val_loss: 0.3560 - val_accuracy: 0.8570 - val_binary_crossentropy: 0.3295

Epoch 19/20

49/49 - 1s - loss: 0.3194 - accuracy: 0.8774 - binary_crossentropy: 0.2928 - val_loss: 0.3551 - val_accuracy: 0.8607 - val_binary_crossentropy: 0.3286

Epoch 20/20

49/49 - 0s - loss: 0.3195 - accuracy: 0.8778 - binary_crossentropy: 0.2931 - val_loss: 0.3539 - val_accuracy: 0.8592 - val_binary_crossentropy: 0.3276

Epoch 1/20

49/49 - 0s - loss: 0.6746 - accuracy: 0.5684 - binary_crossentropy: 0.6746 - val_loss: 0.5901 - val_accuracy: 0.7660 - val_binary_crossentropy: 0.5901

Epoch 2/20

49/49 - 0s - loss: 0.5596 - accuracy: 0.7112 - binary_crossentropy: 0.5596 - val_loss: 0.4331 - val_accuracy: 0.8308 - val_binary_crossentropy: 0.4331

Epoch 3/20

49/49 - 1s - loss: 0.4721 - accuracy: 0.7833 - binary_crossentropy: 0.4721 - val_loss: 0.3661 - val_accuracy: 0.8482 - val_binary_crossentropy: 0.3661

Epoch 4/20

49/49 - 0s - loss: 0.4245 - accuracy: 0.8172 - binary_crossentropy: 0.4245 - val_loss: 0.3439 - val_accuracy: 0.8554 - val_binary_crossentropy: 0.3439

Epoch 5/20

49/49 - 0s - loss: 0.3954 - accuracy: 0.8349 - binary_crossentropy: 0.3954 - val_loss: 0.3323 - val_accuracy: 0.8571 - val_binary_crossentropy: 0.3323

Epoch 6/20

49/49 - 0s - loss: 0.3780 - accuracy: 0.8465 - binary_crossentropy: 0.3780 - val_loss: 0.3271 - val_accuracy: 0.8598 - val_binary_crossentropy: 0.3271

Epoch 7/20

49/49 - 0s - loss: 0.3614 - accuracy: 0.8554 - binary_crossentropy: 0.3614 - val_loss: 0.3262 - val_accuracy: 0.8578 - val_binary_crossentropy: 0.3262

Epoch 8/20

49/49 - 0s - loss: 0.3513 - accuracy: 0.8590 - binary_crossentropy: 0.3513 - val_loss: 0.3226 - val_accuracy: 0.8596 - val_binary_crossentropy: 0.3226

Epoch 9/20

49/49 - 0s - loss: 0.3354 - accuracy: 0.8654 - binary_crossentropy: 0.3354 - val_loss: 0.3224 - val_accuracy: 0.8593 - val_binary_crossentropy: 0.3224

Epoch 10/20

49/49 - 0s - loss: 0.3339 - accuracy: 0.8690 - binary_crossentropy: 0.3339 - val_loss: 0.3224 - val_accuracy: 0.8598 - val_binary_crossentropy: 0.3224

Epoch 11/20

49/49 - 0s - loss: 0.3263 - accuracy: 0.8711 - binary_crossentropy: 0.3263 - val_loss: 0.3245 - val_accuracy: 0.8585 - val_binary_crossentropy: 0.3245

Epoch 12/20

49/49 - 0s - loss: 0.3184 - accuracy: 0.8758 - binary_crossentropy: 0.3184 - val_loss: 0.3287 - val_accuracy: 0.8588 - val_binary_crossentropy: 0.3287

Epoch 13/20

49/49 - 0s - loss: 0.3149 - accuracy: 0.8778 - binary_crossentropy: 0.3149 - val_loss: 0.3283 - val_accuracy: 0.8596 - val_binary_crossentropy: 0.3283

Epoch 14/20

49/49 - 0s - loss: 0.3117 - accuracy: 0.8770 - binary_crossentropy: 0.3117 - val_loss: 0.3306 - val_accuracy: 0.8570 - val_binary_crossentropy: 0.3306

Epoch 15/20

49/49 - 0s - loss: 0.3030 - accuracy: 0.8826 - binary_crossentropy: 0.3030 - val_loss: 0.3322 - val_accuracy: 0.8574 - val_binary_crossentropy: 0.3322

Epoch 16/20

49/49 - 0s - loss: 0.2985 - accuracy: 0.8822 - binary_crossentropy: 0.2985 - val_loss: 0.3324 - val_accuracy: 0.8566 - val_binary_crossentropy: 0.3324

Epoch 17/20

49/49 - 0s - loss: 0.2943 - accuracy: 0.8840 - binary_crossentropy: 0.2943 - val_loss: 0.3337 - val_accuracy: 0.8568 - val_binary_crossentropy: 0.3337

Epoch 18/20

49/49 - 0s - loss: 0.2948 - accuracy: 0.8846 - binary_crossentropy: 0.2948 - val_loss: 0.3348 - val_accuracy: 0.8559 - val_binary_crossentropy: 0.3348

Epoch 19/20

49/49 - 0s - loss: 0.2897 - accuracy: 0.8843 - binary_crossentropy: 0.2897 - val_loss: 0.3388 - val_accuracy: 0.8554 - val_binary_crossentropy: 0.3388

Epoch 20/20

49/49 - 0s - loss: 0.2836 - accuracy: 0.8870 - binary_crossentropy: 0.2836 - val_loss: 0.3458 - val_accuracy: 0.8563 - val_binary_crossentropy: 0.3458

import tensorflow as tf

from tensorflow import keras

import numpy as np

import matplotlib.pyplot as plt

print(tf.__version__)

NUM_WORDS = 1000

(train_data, train_labels), (test_data, test_labels) = keras.datasets.imdb.load_data(num_words=NUM_WORDS)

def multi_hot_sequences(sequences, dimension):

    # 0으로 채워진 (len(sequences), dimension) 크기의 행렬을 만듭니다

    results = np.zeros((len(sequences), dimension))

    for i, word_indices in enumerate(sequences):

        results[i, word_indices] = 1.0  # results[i]의 특정 인덱스만 1로 설정합니다

    return results

train_data = multi_hot_sequences(train_data, dimension=NUM_WORDS)

test_data = multi_hot_sequences(test_data, dimension=NUM_WORDS)

plt.plot(train_data[0])

baseline_model = keras.Sequential([

    # `.summary` 메서드 때문에 `input_shape`가 필요합니다

    keras.layers.Dense(16, activation='relu', input_shape=(NUM_WORDS,)),

    keras.layers.Dense(16, activation='relu'),

    keras.layers.Dense(1, activation='sigmoid')

])

baseline_model.compile(optimizer='adam',

                       loss='binary_crossentropy',

                       metrics=['accuracy', 'binary_crossentropy'])

baseline_model.summary()

baseline_history = baseline_model.fit(train_data,

                                      train_labels,

                                      epochs=20,

                                      batch_size=512,

                                      validation_data=(test_data, test_labels),

                                      verbose=2)

smaller_model = keras.Sequential([

    keras.layers.Dense(4, activation='relu', input_shape=(NUM_WORDS,)),

    keras.layers.Dense(4, activation='relu'),

    keras.layers.Dense(1, activation='sigmoid')

])

smaller_model.compile(optimizer='adam',

                      loss='binary_crossentropy',

                      metrics=['accuracy', 'binary_crossentropy'])

smaller_model.summary()

smaller_history = smaller_model.fit(train_data,

                                    train_labels,

                                    epochs=20,

                                    batch_size=512,

                                    validation_data=(test_data, test_labels),

                                    verbose=2)

bigger_model = keras.models.Sequential([

    keras.layers.Dense(512, activation='relu', input_shape=(NUM_WORDS,)),

    keras.layers.Dense(512, activation='relu'),

    keras.layers.Dense(1, activation='sigmoid')

])

bigger_model.compile(optimizer='adam',

                     loss='binary_crossentropy',

                     metrics=['accuracy','binary_crossentropy'])

bigger_model.summary()

bigger_history = bigger_model.fit(train_data, train_labels,

                                  epochs=20,

                                  batch_size=512,

                                  validation_data=(test_data, test_labels),

                                  verbose=2)

def plot_history(histories, key='binary_crossentropy'):

  plt.figure(figsize=(16,10))

  for name, history in histories:

    val = plt.plot(history.epoch, history.history['val_'+key],

                   '--', label=name.title()+' Val')

    plt.plot(history.epoch, history.history[key], color=val[0].get_color(),

             label=name.title()+' Train')

  plt.xlabel('Epochs')

  plt.ylabel(key.replace('_',' ').title())

  plt.legend()

  plt.xlim([0,max(history.epoch)])

plot_history([('baseline', baseline_history),

              ('smaller', smaller_history),

              ('bigger', bigger_history)])

l2_model = keras.models.Sequential([

    keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),

                       activation='relu', input_shape=(NUM_WORDS,)),

    keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),

                       activation='relu'),

    keras.layers.Dense(1, activation='sigmoid')

])

l2_model.compile(optimizer='adam',

                 loss='binary_crossentropy',

                 metrics=['accuracy', 'binary_crossentropy'])

l2_model_history = l2_model.fit(train_data, train_labels,

                                epochs=20,

                                batch_size=512,

                                validation_data=(test_data, test_labels),

                                verbose=2)

plot_history([('baseline', baseline_history),

              ('l2', l2_model_history)])

dpt_model = keras.models.Sequential([

    keras.layers.Dense(16, activation='relu', input_shape=(NUM_WORDS,)),

    keras.layers.Dropout(0.5),

    keras.layers.Dense(16, activation='relu'),

    keras.layers.Dropout(0.5),

    keras.layers.Dense(1, activation='sigmoid')

])

dpt_model.compile(optimizer='adam',

                  loss='binary_crossentropy',

                  metrics=['accuracy','binary_crossentropy'])

dpt_model_history = dpt_model.fit(train_data, train_labels,

                                  epochs=20,

                                  batch_size=512,

                                  validation_data=(test_data, test_labels),

                                  verbose=2)

plot_history([('baseline', baseline_history),

              ('dropout', dpt_model_history)])

#

# 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.

tensorflow.python.framework.errors_impl.InternalError: 2 root error(s) found.

  (0) Internal:  Blas GEMM launch failed : a.shape=(512, 16), b.shape=(16, 16), m=512, n=16, k=16

            [[node sequential/dense/MatMul (defined at O:/PycharmProjects/catdogtf2.2/004.py:74) ]]

  (1) Internal:  Blas GEMM launch failed : a.shape=(512, 16), b.shape=(16, 16), m=512, n=16, k=16

            [[node sequential/dense/MatMul (defined at O:/PycharmProjects/catdogtf2.2/004.py:74) ]]

            [[gradient_tape/sequential/embedding/embedding_lookup/Reshape/_46]]

O:\PycharmProjects\catdogtf2.2\venv\Scripts\python.exe O:\PyCharm\plugins\python\helpers\pydev\pydevconsole.py --mode=client --port=53066

import sys; print('Python %s on %s' % (sys.version, sys.platform))

sys.path.extend(['O:\\PycharmProjects\\catdogtf2.2', 'O:/PycharmProjects/catdogtf2.2'])

PyDev console: starting.

Python 3.7.7 (default, May  6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)] on win32

>>> runfile('O:/PycharmProjects/catdogtf2.2/004.py', wdir='O:/PycharmProjects/catdogtf2.2')

2020-08-11 05:20:09.882744: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2.2.0

O:\PycharmProjects\catdogtf2.2\venv\lib\site-packages\tensorflow\python\keras\datasets\imdb.py:155: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray

  x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])

O:\PycharmProjects\catdogtf2.2\venv\lib\site-packages\tensorflow\python\keras\datasets\imdb.py:156: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray

  x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])

훈련 샘플: 25000, 레이블: 25000

[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]

[   1   14   22   16   43  530  973 1622 1385   65  458 4468   66 3941

    4  173   36  256    5   25  100   43  838  112   50  670    2    9

   35  480  284    5  150    4  172  112  167    2  336  385   39    4

  172 4536 1111   17  546   38   13  447    4  192   50   16    6  147

 2025   19   14   22    4 1920 4613  469    4   22   71   87   12   16

   43  530   38   76   15   13 1247    4   22   17  515   17   12   16

  626   18    2    5   62  386   12    8  316    8  106    5    4 2223

 5244   16  480   66 3785   33    4  130   12   16   38  619    5   25

  124   51   36  135   48   25 1415   33    6   22   12  215   28   77

   52    5   14  407   16   82    2    8    4  107  117 5952   15  256

    4    2    7 3766    5  723   36   71   43  530  476   26  400  317

   46    7    4    2 1029   13  104   88    4  381   15  297   98   32

 2071   56   26  141    6  194 7486   18    4  226   22   21  134  476

   26  480    5  144   30 5535   18   51   36   28  224   92   25  104

    4  226   65   16   38 1334   88   12   16  283    5   16 4472  113

  103   32   15   16 5345   19  178   32    0    0    0    0    0    0

    0    0    0    0    0    0    0    0    0    0    0    0    0    0

    0    0    0    0    0    0    0    0    0    0    0    0    0    0

    0    0    0    0]

2020-08-11 05:20:18.596801: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll

2020-08-11 05:20:18.637406: 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 05:20:18.637937: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 05:20:18.645961: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 05:20:18.652048: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 05:20:18.655131: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 05:20:18.663508: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 05:20:18.668992: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 05:20:18.685127: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 05:20:18.685528: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 05:20:18.686031: 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 05:20:18.697734: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1a1122e60f0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:

2020-08-11 05:20:18.698127: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version

2020-08-11 05:20:18.698600: 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 05:20:18.699083: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 05:20:18.699331: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 05:20:18.699595: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 05:20:18.699820: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 05:20:18.700003: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 05:20:18.700183: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 05:20:18.700378: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 05:20:18.700642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

2020-08-11 05:20:19.358531: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1102] Device interconnect StreamExecutor with strength 1 edge matrix:

2020-08-11 05:20:19.358724: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1108]      0

2020-08-11 05:20:19.358810: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1121] 0:   N

2020-08-11 05:20:19.359171: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1247] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6198 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 05:20:19.362586: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x1a113c59140 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:

2020-08-11 05:20:19.362846: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce RTX 2080 SUPER, Compute Capability 7.5

Model: "sequential"

_________________________________________________________________

Layer (type)                 Output Shape              Param #  

=================================================================

embedding (Embedding)        (None, None, 16)          160000   

_________________________________________________________________

global_average_pooling1d (Gl (None, 16)                0        

_________________________________________________________________

dense (Dense)                (None, 16)                272      

_________________________________________________________________

dense_1 (Dense)              (None, 1)                 17       

=================================================================

Total params: 160,289

Trainable params: 160,289

Non-trainable params: 0

_________________________________________________________________

Epoch 1/40

2020-08-11 05:20:20.301884: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

30/30 [==============================] - 1s 23ms/step - loss: 0.6917 - accuracy: 0.5509 - val_loss: 0.6898 - val_accuracy: 0.5002

Epoch 2/40

30/30 [==============================] - 0s 16ms/step - loss: 0.6846 - accuracy: 0.6117 - val_loss: 0.6797 - val_accuracy: 0.7024

Epoch 3/40

30/30 [==============================] - 0s 17ms/step - loss: 0.6703 - accuracy: 0.7325 - val_loss: 0.6630 - val_accuracy: 0.7302

Epoch 4/40

30/30 [==============================] - 0s 16ms/step - loss: 0.6471 - accuracy: 0.7548 - val_loss: 0.6368 - val_accuracy: 0.7576

Epoch 5/40

30/30 [==============================] - 0s 16ms/step - loss: 0.6132 - accuracy: 0.7815 - val_loss: 0.6014 - val_accuracy: 0.7876

Epoch 6/40

30/30 [==============================] - 0s 16ms/step - loss: 0.5710 - accuracy: 0.8108 - val_loss: 0.5609 - val_accuracy: 0.8045

Epoch 7/40

30/30 [==============================] - 1s 17ms/step - loss: 0.5249 - accuracy: 0.8304 - val_loss: 0.5189 - val_accuracy: 0.8153

Epoch 8/40

30/30 [==============================] - 0s 16ms/step - loss: 0.4791 - accuracy: 0.8481 - val_loss: 0.4785 - val_accuracy: 0.8324

Epoch 9/40

30/30 [==============================] - 0s 16ms/step - loss: 0.4364 - accuracy: 0.8610 - val_loss: 0.4428 - val_accuracy: 0.8430

Epoch 10/40

30/30 [==============================] - 0s 16ms/step - loss: 0.3986 - accuracy: 0.8729 - val_loss: 0.4132 - val_accuracy: 0.8495

Epoch 11/40

30/30 [==============================] - 0s 16ms/step - loss: 0.3664 - accuracy: 0.8797 - val_loss: 0.3890 - val_accuracy: 0.8559

Epoch 12/40

30/30 [==============================] - 0s 16ms/step - loss: 0.3392 - accuracy: 0.8879 - val_loss: 0.3684 - val_accuracy: 0.8636

Epoch 13/40

30/30 [==============================] - 0s 16ms/step - loss: 0.3158 - accuracy: 0.8953 - val_loss: 0.3523 - val_accuracy: 0.8677

Epoch 14/40

30/30 [==============================] - 0s 16ms/step - loss: 0.2961 - accuracy: 0.9003 - val_loss: 0.3392 - val_accuracy: 0.8716

Epoch 15/40

30/30 [==============================] - 0s 16ms/step - loss: 0.2785 - accuracy: 0.9069 - val_loss: 0.3283 - val_accuracy: 0.8743

Epoch 16/40

30/30 [==============================] - 0s 16ms/step - loss: 0.2631 - accuracy: 0.9111 - val_loss: 0.3204 - val_accuracy: 0.8760

Epoch 17/40

30/30 [==============================] - 0s 16ms/step - loss: 0.2498 - accuracy: 0.9151 - val_loss: 0.3122 - val_accuracy: 0.8796

Epoch 18/40

30/30 [==============================] - 0s 16ms/step - loss: 0.2375 - accuracy: 0.9199 - val_loss: 0.3064 - val_accuracy: 0.8795

Epoch 19/40

30/30 [==============================] - 0s 16ms/step - loss: 0.2257 - accuracy: 0.9237 - val_loss: 0.3023 - val_accuracy: 0.8803

Epoch 20/40

30/30 [==============================] - 0s 16ms/step - loss: 0.2153 - accuracy: 0.9259 - val_loss: 0.2972 - val_accuracy: 0.8824

Epoch 21/40

30/30 [==============================] - 0s 16ms/step - loss: 0.2055 - accuracy: 0.9297 - val_loss: 0.2950 - val_accuracy: 0.8819

Epoch 22/40

30/30 [==============================] - 0s 16ms/step - loss: 0.1970 - accuracy: 0.9327 - val_loss: 0.2915 - val_accuracy: 0.8834

Epoch 23/40

30/30 [==============================] - 0s 17ms/step - loss: 0.1881 - accuracy: 0.9379 - val_loss: 0.2898 - val_accuracy: 0.8832

Epoch 24/40

30/30 [==============================] - 0s 17ms/step - loss: 0.1804 - accuracy: 0.9411 - val_loss: 0.2882 - val_accuracy: 0.8842

Epoch 25/40

30/30 [==============================] - 1s 17ms/step - loss: 0.1733 - accuracy: 0.9441 - val_loss: 0.2867 - val_accuracy: 0.8850

Epoch 26/40

30/30 [==============================] - 0s 16ms/step - loss: 0.1656 - accuracy: 0.9472 - val_loss: 0.2858 - val_accuracy: 0.8861

Epoch 27/40

30/30 [==============================] - 0s 16ms/step - loss: 0.1593 - accuracy: 0.9504 - val_loss: 0.2857 - val_accuracy: 0.8858

Epoch 28/40

30/30 [==============================] - 0s 16ms/step - loss: 0.1529 - accuracy: 0.9529 - val_loss: 0.2857 - val_accuracy: 0.8862

Epoch 29/40

30/30 [==============================] - 0s 16ms/step - loss: 0.1469 - accuracy: 0.9555 - val_loss: 0.2863 - val_accuracy: 0.8862

Epoch 30/40

30/30 [==============================] - 0s 16ms/step - loss: 0.1416 - accuracy: 0.9579 - val_loss: 0.2871 - val_accuracy: 0.8865

Epoch 31/40

30/30 [==============================] - 0s 16ms/step - loss: 0.1360 - accuracy: 0.9603 - val_loss: 0.2889 - val_accuracy: 0.8858

Epoch 32/40

30/30 [==============================] - 0s 16ms/step - loss: 0.1310 - accuracy: 0.9619 - val_loss: 0.2897 - val_accuracy: 0.8867

Epoch 33/40

30/30 [==============================] - 0s 16ms/step - loss: 0.1260 - accuracy: 0.9637 - val_loss: 0.2917 - val_accuracy: 0.8860

Epoch 34/40

30/30 [==============================] - 0s 16ms/step - loss: 0.1210 - accuracy: 0.9655 - val_loss: 0.2925 - val_accuracy: 0.8856

Epoch 35/40

30/30 [==============================] - 0s 16ms/step - loss: 0.1174 - accuracy: 0.9663 - val_loss: 0.2950 - val_accuracy: 0.8871

Epoch 36/40

30/30 [==============================] - 0s 16ms/step - loss: 0.1123 - accuracy: 0.9689 - val_loss: 0.2974 - val_accuracy: 0.8854

Epoch 37/40

30/30 [==============================] - 0s 16ms/step - loss: 0.1082 - accuracy: 0.9699 - val_loss: 0.3024 - val_accuracy: 0.8819

Epoch 38/40

30/30 [==============================] - 1s 17ms/step - loss: 0.1044 - accuracy: 0.9715 - val_loss: 0.3019 - val_accuracy: 0.8837

Epoch 39/40

30/30 [==============================] - 0s 17ms/step - loss: 0.1002 - accuracy: 0.9731 - val_loss: 0.3048 - val_accuracy: 0.8841

Epoch 40/40

30/30 [==============================] - 0s 16ms/step - loss: 0.0964 - accuracy: 0.9750 - val_loss: 0.3096 - val_accuracy: 0.8812

782/782 - 1s - loss: 0.3282 - accuracy: 0.8718

[0.32820913195610046, 0.8718400001525879]

 원래 잘 되던게 다시 해보면, 한 번에 되는게 없네 ㅋ 믓튼, 자료 준비 잼남. tutorials 소스 요청은 mynameis@hajunho.com 으로 (은근 일임)

import tensorflow as tf

from tensorflow import keras

import numpy as np

print(tf.__version__)

imdb = keras.datasets.imdb

(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)

print("훈련 샘플: {}, 레이블: {}".format(len(train_data), len(train_labels)))

print(train_data[0])

len(train_data[0]), len(train_data[1])

# 단어와 정수 인덱스를 매핑한 딕셔너리

word_index = imdb.get_word_index()

# 처음 몇 개 인덱스는 사전에 정의되어 있습니다

word_index = {k:(v+3) for k,v in word_index.items()}

word_index["<PAD>"] = 0

word_index["<START>"] = 1

word_index["<UNK>"] = 2  # unknown

word_index["<UNUSED>"] = 3

reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])

def decode_review(text):

    return ' '.join([reverse_word_index.get(i, '?') for i in text])

decode_review(train_data[0])

train_data = keras.preprocessing.sequence.pad_sequences(train_data,

                                                        value=word_index["<PAD>"],

                                                        padding='post',

                                                        maxlen=256)

test_data = keras.preprocessing.sequence.pad_sequences(test_data,

                                                       value=word_index["<PAD>"],

                                                       padding='post',

                                                       maxlen=256)

len(train_data[0]), len(train_data[1])

print(train_data[0])

# 입력 크기는 영화 리뷰 데이터셋에 적용된 어휘 사전의 크기입니다(10,000개의 단어)

vocab_size = 10000

model = keras.Sequential()

model.add(keras.layers.Embedding(vocab_size, 16, input_shape=(None,)))

model.add(keras.layers.GlobalAveragePooling1D())

model.add(keras.layers.Dense(16, activation='relu'))

model.add(keras.layers.Dense(1, activation='sigmoid'))

model.summary()

model.compile(optimizer='adam',

              loss='binary_crossentropy',

              metrics=['accuracy'])

x_val = train_data[:10000]

partial_x_train = train_data[10000:]

y_val = train_labels[:10000]

partial_y_train = train_labels[10000:]

history = model.fit(partial_x_train,

                    partial_y_train,

                    epochs=40,

                    batch_size=512,

                    validation_data=(x_val, y_val),

                    verbose=1)

results = model.evaluate(test_data,  test_labels, verbose=2)

print(results)

history_dict = history.history

history_dict.keys()

import matplotlib.pyplot as plt

acc = history_dict['accuracy']

val_acc = history_dict['val_accuracy']

loss = history_dict['loss']

val_loss = history_dict['val_loss']

epochs = range(1, len(acc) + 1)

# "bo"는 "파란색 점"입니다

plt.plot(epochs, loss, 'bo', label='Training loss')

# b는 "파란 실선"입니다

plt.plot(epochs, val_loss, 'b', label='Validation loss')

plt.title('Training and validation loss')

plt.xlabel('Epochs')

plt.ylabel('Loss')

plt.legend()

plt.show()

plt.clf()   # 그림을 초기화합니다

plt.plot(epochs, acc, 'bo', label='Training acc')

plt.plot(epochs, val_acc, 'b', label='Validation acc')

plt.title('Training and validation accuracy')

plt.xlabel('Epochs')

plt.ylabel('Accuracy')

plt.legend()

plt.show()

#

# 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.

import numpy as np

import tensorflow as tf

#!pip install -q tensorflow-hub

#!pip install -q tfds-nightly

import tensorflow_hub as hub

import tensorflow_datasets as tfds

print("버전: ", tf.__version__)

print("즉시 실행 모드: ", tf.executing_eagerly())

print("허브 버전: ", hub.__version__)

print("GPU", "사용 가능" if tf.config.experimental.list_physical_devices("GPU") else "NOT AVAILABLE")

# 훈련 세트를 6대 4로 나눕니다.

# 결국 훈련에 15,000개 샘플, 검증에 10,000개 샘플, 테스트에 25,000개 샘플을 사용하게 됩니다.

train_data, validation_data, test_data = tfds.load(

    name="imdb_reviews",

    split=('train[:60%]', 'train[60%:]', 'test'),

    as_supervised=True)

train_examples_batch, train_labels_batch = next(iter(train_data.batch(10)))

train_examples_batch

train_labels_batch

embedding = "https://tfhub.dev/google/tf2-preview/gnews-swivel-20dim/1"

hub_layer = hub.KerasLayer(embedding, input_shape=[],

                           dtype=tf.string, trainable=True)

hub_layer(train_examples_batch[:3])

model = tf.keras.Sequential()

model.add(hub_layer)

model.add(tf.keras.layers.Dense(16, activation='relu'))

model.add(tf.keras.layers.Dense(1))

model.summary()

model.compile(optimizer='adam',

              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),

              metrics=['accuracy'])

history = model.fit(train_data.shuffle(10000).batch(512),

                    epochs=20,

                    validation_data=validation_data.batch(512),

                    verbose=1)

results = model.evaluate(test_data.batch(512), verbose=2)

for name, value in zip(model.metrics_names, results):

  print("%s: %.3f" % (name, value))

  #

  # 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.

O:\PycharmProjects\catdogtf2.2\venv\Scripts\python.exe O:\PyCharm\plugins\python\helpers\pydev\pydevconsole.py --mode=client --port=52110

import sys; print('Python %s on %s' % (sys.version, sys.platform))

sys.path.extend(['O:\\PycharmProjects\\catdogtf2.2', 'O:/PycharmProjects/catdogtf2.2'])

PyDev console: starting.

Python 3.7.7 (default, May  6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)] on win32

>>> runfile('O:/PycharmProjects/catdogtf2.2/003.py', wdir='O:/PycharmProjects/catdogtf2.2')

2020-08-11 05:11:11.697892: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

버전:  2.2.0

즉시 실행 모드:  True

허브 버전:  0.8.0

2020-08-11 05:11:14.939954: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll

2020-08-11 05:11:14.981186: 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 05:11:14.981690: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll

2020-08-11 05:11:14.987793: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_10.dll

2020-08-11 05:11:14.992555: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cufft64_10.dll

2020-08-11 05:11:14.995107: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library curand64_10.dll

2020-08-11 05:11:15.000251: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusolver64_10.dll

2020-08-11 05:11:15.003882: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cusparse64_10.dll

2020-08-11 05:11:15.011752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll

2020-08-11 05:11:15.012144: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0

GPU 사용 가능

Downloading and preparing dataset imdb_reviews/plain_text/1.0.0 (download: Unknown size, generated: Unknown size, total: Unknown size) to C:\Users\joe\tensorflow_datasets\imdb_reviews\plain_text\1.0.0...

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