标签： 50000 1s 0s ETA loss accuracy

C:\Users\Administrator\AppData\Local\Programs\Python\Python36\python.exe "C:\Program Files\JetBrains\PyCharm 2019.1.2\helpers\pydev\pydevconsole.py" --mode=client --port=53108
import sys; print('Python %s on %s' % (sys.version, sys.platform))
sys.path.extend(['E:\\', 'E:/'])
PyDev console: starting.
Python 3.6.8 (tags/v3.6.8:3c6b436a57, Dec 24 2018, 00:16:47) [MSC v.1916 64 bit (AMD64)] on win32
runfile('E:/02cifar10/cifar10_V2.py', wdir='E:/02cifar10')
2019-10-04 13:38:02.549163: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library nvcuda.dll
2019-10-04 13:38:04.090486: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: 
name: GeForce RTX 2070 major: 7 minor: 5 memoryClockRate(GHz): 1.44
pciBusID: 0000:01:00.0
2019-10-04 13:38:04.091544: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-10-04 13:38:04.092932: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-10-04 13:38:04.095555: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-10-04 13:38:04.100545: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: 
name: GeForce RTX 2070 major: 7 minor: 5 memoryClockRate(GHz): 1.44
pciBusID: 0000:01:00.0
2019-10-04 13:38:04.100932: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-10-04 13:38:04.101942: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-10-04 13:38:06.671335: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-10-04 13:38:06.671642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 
2019-10-04 13:38:06.671821: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N 
2019-10-04 13:38:06.674798: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6315 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2070, pci bus id: 0000:01:00.0, compute capability: 7.5)
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 30, 30, 32)        896       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 15, 15, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 13, 13, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64)          0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 4, 4, 64)          36928     
=================================================================
Total params: 56,320
Trainable params: 56,320
Non-trainable params: 0
_________________________________________________________________
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 30, 30, 32)        896       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 15, 15, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 13, 13, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64)          0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 4, 4, 64)          36928     
_________________________________________________________________
flatten (Flatten)            (None, 1024)              0         
_________________________________________________________________
dense (Dense)                (None, 64)                65600     
_________________________________________________________________
dense_1 (Dense)              (None, 10)                650       
=================================================================
Total params: 122,570
Trainable params: 122,570
Non-trainable params: 0
_________________________________________________________________
WARNING: Logging before flag parsing goes to stderr.
W1004 13:38:07.284916 12924 deprecation.py:323] From C:\Users\Administrator\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
Train on 50000 samples, validate on 10000 samples
Epoch 1/10
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50000/50000 [==============================] - 12s 249us/sample - loss: 1.4962 - accuracy: 0.4547 - val_loss: 1.2418 - val_accuracy: 0.5535
Epoch 2/10
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50000/50000 [==============================] - 8s 157us/sample - loss: 1.1250 - accuracy: 0.6016 - val_loss: 1.0745 - val_accuracy: 0.6253
Epoch 3/10
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50000/50000 [==============================] - 8s 157us/sample - loss: 0.9661 - accuracy: 0.6636 - val_loss: 1.0065 - val_accuracy: 0.6480
Epoch 4/10
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50000/50000 [==============================] - 8s 158us/sample - loss: 0.8648 - accuracy: 0.6972 - val_loss: 0.9078 - val_accuracy: 0.6846
Epoch 5/10
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50000/50000 [==============================] - 8s 157us/sample - loss: 0.7846 - accuracy: 0.7258 - val_loss: 0.9045 - val_accuracy: 0.6829
Epoch 6/10
   32/50000 [..............................] - ETA: 24s - loss: 0.5246 - accuracy: 0.8125
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50000/50000 [==============================] - 8s 157us/sample - loss: 0.7222 - accuracy: 0.7483 - val_loss: 0.8591 - val_accuracy: 0.7037
Epoch 7/10
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50000/50000 [==============================] - 8s 157us/sample - loss: 0.6637 - accuracy: 0.7688 - val_loss: 0.8608 - val_accuracy: 0.7059
Epoch 8/10
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50000/50000 [==============================] - 8s 158us/sample - loss: 0.6164 - accuracy: 0.7850 - val_loss: 0.8763 - val_accuracy: 0.7081
Epoch 9/10
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50000/50000 [==============================] - 8s 157us/sample - loss: 0.5673 - accuracy: 0.7993 - val_loss: 0.8531 - val_accuracy: 0.7155
Epoch 10/10
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50000/50000 [==============================] - 8s 157us/sample - loss: 0.5292 - accuracy: 0.8125 - val_loss: 0.9076 - val_accuracy: 0.7104
10000/10000 - 1s - loss: 0.9076 - accuracy: 0.7104
0.7104

标签：,50000,1s,0s,ETA,loss,accuracy
来源： https://www.cnblogs.com/gengyi/p/11622024.html

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