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.
'진행 프로젝트 > [진행] Tensorflow2 &amp;amp;amp;quot;해볼까?&amp;amp;amp;quot;' 카테고리의 다른 글
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| tutorial 01 running on pyCharm 2020.2 & 3.7 (1) | 2020.08.11 |