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.
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