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Created September 3, 2017 16:27
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tf.log
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tf.log
Using TensorFlow backend.
X_train shape: (50000, 32, 32, 3)
50000 train samples
10000 test samples
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 32, 32, 3) 0
____________________________________________________________________________________________________
convolution2d_1 (Convolution2D) (None, 16, 16, 64) 9472 input_1[0][0]
____________________________________________________________________________________________________
batchnormalization_1 (BatchNorma (None, 16, 16, 64) 256 convolution2d_1[0][0]
____________________________________________________________________________________________________
activation_1 (Activation) (None, 16, 16, 64) 0 batchnormalization_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D) (None, 7, 7, 64) 0 activation_1[0][0]
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D) (None, 7, 7, 64) 4160 maxpooling2d_1[0][0]
____________________________________________________________________________________________________
batchnormalization_2 (BatchNorma (None, 7, 7, 64) 256 convolution2d_2[0][0]
____________________________________________________________________________________________________
activation_2 (Activation) (None, 7, 7, 64) 0 batchnormalization_2[0][0]
____________________________________________________________________________________________________
convolution2d_3 (Convolution2D) (None, 7, 7, 64) 36928 activation_2[0][0]
____________________________________________________________________________________________________
batchnormalization_3 (BatchNorma (None, 7, 7, 64) 256 convolution2d_3[0][0]
____________________________________________________________________________________________________
activation_3 (Activation) (None, 7, 7, 64) 0 batchnormalization_3[0][0]
____________________________________________________________________________________________________
convolution2d_5 (Convolution2D) (None, 7, 7, 256) 16640 maxpooling2d_1[0][0]
____________________________________________________________________________________________________
convolution2d_4 (Convolution2D) (None, 7, 7, 256) 16640 activation_3[0][0]
____________________________________________________________________________________________________
merge_1 (Merge) (None, 7, 7, 256) 0 convolution2d_5[0][0]
convolution2d_4[0][0]
____________________________________________________________________________________________________
batchnormalization_4 (BatchNorma (None, 7, 7, 256) 1024 merge_1[0][0]
____________________________________________________________________________________________________
activation_4 (Activation) (None, 7, 7, 256) 0 batchnormalization_4[0][0]
____________________________________________________________________________________________________
convolution2d_6 (Convolution2D) (None, 7, 7, 64) 16448 activation_4[0][0]
____________________________________________________________________________________________________
batchnormalization_5 (BatchNorma (None, 7, 7, 64) 256 convolution2d_6[0][0]
____________________________________________________________________________________________________
activation_5 (Activation) (None, 7, 7, 64) 0 batchnormalization_5[0][0]
____________________________________________________________________________________________________
convolution2d_7 (Convolution2D) (None, 7, 7, 64) 36928 activation_5[0][0]
____________________________________________________________________________________________________
batchnormalization_6 (BatchNorma (None, 7, 7, 64) 256 convolution2d_7[0][0]
____________________________________________________________________________________________________
activation_6 (Activation) (None, 7, 7, 64) 0 batchnormalization_6[0][0]
____________________________________________________________________________________________________
convolution2d_8 (Convolution2D) (None, 7, 7, 256) 16640 activation_6[0][0]
____________________________________________________________________________________________________
merge_2 (Merge) (None, 7, 7, 256) 0 merge_1[0][0]
convolution2d_8[0][0]
____________________________________________________________________________________________________
batchnormalization_7 (BatchNorma (None, 7, 7, 256) 1024 merge_2[0][0]
____________________________________________________________________________________________________
activation_7 (Activation) (None, 7, 7, 256) 0 batchnormalization_7[0][0]
____________________________________________________________________________________________________
convolution2d_9 (Convolution2D) (None, 7, 7, 64) 16448 activation_7[0][0]
____________________________________________________________________________________________________
batchnormalization_8 (BatchNorma (None, 7, 7, 64) 256 convolution2d_9[0][0]
____________________________________________________________________________________________________
activation_8 (Activation) (None, 7, 7, 64) 0 batchnormalization_8[0][0]
____________________________________________________________________________________________________
convolution2d_10 (Convolution2D) (None, 7, 7, 64) 36928 activation_8[0][0]
____________________________________________________________________________________________________
batchnormalization_9 (BatchNorma (None, 7, 7, 64) 256 convolution2d_10[0][0]
____________________________________________________________________________________________________
activation_9 (Activation) (None, 7, 7, 64) 0 batchnormalization_9[0][0]
____________________________________________________________________________________________________
convolution2d_11 (Convolution2D) (None, 7, 7, 256) 16640 activation_9[0][0]
____________________________________________________________________________________________________
merge_3 (Merge) (None, 7, 7, 256) 0 merge_2[0][0]
convolution2d_11[0][0]
____________________________________________________________________________________________________
batchnormalization_10 (BatchNorm (None, 7, 7, 256) 1024 merge_3[0][0]
____________________________________________________________________________________________________
activation_10 (Activation) (None, 7, 7, 256) 0 batchnormalization_10[0][0]
____________________________________________________________________________________________________
convolution2d_12 (Convolution2D) (None, 4, 4, 128) 32896 activation_10[0][0]
____________________________________________________________________________________________________
batchnormalization_11 (BatchNorm (None, 4, 4, 128) 512 convolution2d_12[0][0]
____________________________________________________________________________________________________
activation_11 (Activation) (None, 4, 4, 128) 0 batchnormalization_11[0][0]
____________________________________________________________________________________________________
convolution2d_13 (Convolution2D) (None, 4, 4, 128) 147584 activation_11[0][0]
____________________________________________________________________________________________________
batchnormalization_12 (BatchNorm (None, 4, 4, 128) 512 convolution2d_13[0][0]
____________________________________________________________________________________________________
activation_12 (Activation) (None, 4, 4, 128) 0 batchnormalization_12[0][0]
____________________________________________________________________________________________________
convolution2d_15 (Convolution2D) (None, 4, 4, 512) 131584 merge_3[0][0]
____________________________________________________________________________________________________
convolution2d_14 (Convolution2D) (None, 4, 4, 512) 66048 activation_12[0][0]
____________________________________________________________________________________________________
merge_4 (Merge) (None, 4, 4, 512) 0 convolution2d_15[0][0]
convolution2d_14[0][0]
____________________________________________________________________________________________________
batchnormalization_13 (BatchNorm (None, 4, 4, 512) 2048 merge_4[0][0]
____________________________________________________________________________________________________
activation_13 (Activation) (None, 4, 4, 512) 0 batchnormalization_13[0][0]
____________________________________________________________________________________________________
convolution2d_16 (Convolution2D) (None, 4, 4, 128) 65664 activation_13[0][0]
____________________________________________________________________________________________________
batchnormalization_14 (BatchNorm (None, 4, 4, 128) 512 convolution2d_16[0][0]
____________________________________________________________________________________________________
activation_14 (Activation) (None, 4, 4, 128) 0 batchnormalization_14[0][0]
____________________________________________________________________________________________________
convolution2d_17 (Convolution2D) (None, 4, 4, 128) 147584 activation_14[0][0]
____________________________________________________________________________________________________
batchnormalization_15 (BatchNorm (None, 4, 4, 128) 512 convolution2d_17[0][0]
____________________________________________________________________________________________________
activation_15 (Activation) (None, 4, 4, 128) 0 batchnormalization_15[0][0]
____________________________________________________________________________________________________
convolution2d_18 (Convolution2D) (None, 4, 4, 512) 66048 activation_15[0][0]
____________________________________________________________________________________________________
merge_5 (Merge) (None, 4, 4, 512) 0 merge_4[0][0]
convolution2d_18[0][0]
____________________________________________________________________________________________________
batchnormalization_16 (BatchNorm (None, 4, 4, 512) 2048 merge_5[0][0]
____________________________________________________________________________________________________
activation_16 (Activation) (None, 4, 4, 512) 0 batchnormalization_16[0][0]
____________________________________________________________________________________________________
convolution2d_19 (Convolution2D) (None, 4, 4, 128) 65664 activation_16[0][0]
____________________________________________________________________________________________________
batchnormalization_17 (BatchNorm (None, 4, 4, 128) 512 convolution2d_19[0][0]
____________________________________________________________________________________________________
activation_17 (Activation) (None, 4, 4, 128) 0 batchnormalization_17[0][0]
____________________________________________________________________________________________________
convolution2d_20 (Convolution2D) (None, 4, 4, 128) 147584 activation_17[0][0]
____________________________________________________________________________________________________
batchnormalization_18 (BatchNorm (None, 4, 4, 128) 512 convolution2d_20[0][0]
____________________________________________________________________________________________________
activation_18 (Activation) (None, 4, 4, 128) 0 batchnormalization_18[0][0]
____________________________________________________________________________________________________
convolution2d_21 (Convolution2D) (None, 4, 4, 512) 66048 activation_18[0][0]
____________________________________________________________________________________________________
merge_6 (Merge) (None, 4, 4, 512) 0 merge_5[0][0]
convolution2d_21[0][0]
____________________________________________________________________________________________________
batchnormalization_19 (BatchNorm (None, 4, 4, 512) 2048 merge_6[0][0]
____________________________________________________________________________________________________
activation_19 (Activation) (None, 4, 4, 512) 0 batchnormalization_19[0][0]
____________________________________________________________________________________________________
convolution2d_22 (Convolution2D) (None, 4, 4, 128) 65664 activation_19[0][0]
____________________________________________________________________________________________________
batchnormalization_20 (BatchNorm (None, 4, 4, 128) 512 convolution2d_22[0][0]
____________________________________________________________________________________________________
activation_20 (Activation) (None, 4, 4, 128) 0 batchnormalization_20[0][0]
____________________________________________________________________________________________________
convolution2d_23 (Convolution2D) (None, 4, 4, 128) 147584 activation_20[0][0]
____________________________________________________________________________________________________
batchnormalization_21 (BatchNorm (None, 4, 4, 128) 512 convolution2d_23[0][0]
____________________________________________________________________________________________________
activation_21 (Activation) (None, 4, 4, 128) 0 batchnormalization_21[0][0]
____________________________________________________________________________________________________
convolution2d_24 (Convolution2D) (None, 4, 4, 512) 66048 activation_21[0][0]
____________________________________________________________________________________________________
merge_7 (Merge) (None, 4, 4, 512) 0 merge_6[0][0]
convolution2d_24[0][0]
____________________________________________________________________________________________________
batchnormalization_22 (BatchNorm (None, 4, 4, 512) 2048 merge_7[0][0]
____________________________________________________________________________________________________
activation_22 (Activation) (None, 4, 4, 512) 0 batchnormalization_22[0][0]
____________________________________________________________________________________________________
convolution2d_25 (Convolution2D) (None, 2, 2, 256) 131328 activation_22[0][0]
____________________________________________________________________________________________________
batchnormalization_23 (BatchNorm (None, 2, 2, 256) 1024 convolution2d_25[0][0]
____________________________________________________________________________________________________
activation_23 (Activation) (None, 2, 2, 256) 0 batchnormalization_23[0][0]
____________________________________________________________________________________________________
convolution2d_26 (Convolution2D) (None, 2, 2, 256) 590080 activation_23[0][0]
____________________________________________________________________________________________________
batchnormalization_24 (BatchNorm (None, 2, 2, 256) 1024 convolution2d_26[0][0]
____________________________________________________________________________________________________
activation_24 (Activation) (None, 2, 2, 256) 0 batchnormalization_24[0][0]
____________________________________________________________________________________________________
convolution2d_28 (Convolution2D) (None, 2, 2, 1024) 525312 merge_7[0][0]
____________________________________________________________________________________________________
convolution2d_27 (Convolution2D) (None, 2, 2, 1024) 263168 activation_24[0][0]
____________________________________________________________________________________________________
merge_8 (Merge) (None, 2, 2, 1024) 0 convolution2d_28[0][0]
convolution2d_27[0][0]
____________________________________________________________________________________________________
batchnormalization_25 (BatchNorm (None, 2, 2, 1024) 4096 merge_8[0][0]
____________________________________________________________________________________________________
activation_25 (Activation) (None, 2, 2, 1024) 0 batchnormalization_25[0][0]
____________________________________________________________________________________________________
convolution2d_29 (Convolution2D) (None, 2, 2, 256) 262400 activation_25[0][0]
____________________________________________________________________________________________________
batchnormalization_26 (BatchNorm (None, 2, 2, 256) 1024 convolution2d_29[0][0]
____________________________________________________________________________________________________
activation_26 (Activation) (None, 2, 2, 256) 0 batchnormalization_26[0][0]
____________________________________________________________________________________________________
convolution2d_30 (Convolution2D) (None, 2, 2, 256) 590080 activation_26[0][0]
____________________________________________________________________________________________________
batchnormalization_27 (BatchNorm (None, 2, 2, 256) 1024 convolution2d_30[0][0]
____________________________________________________________________________________________________
activation_27 (Activation) (None, 2, 2, 256) 0 batchnormalization_27[0][0]
____________________________________________________________________________________________________
convolution2d_31 (Convolution2D) (None, 2, 2, 1024) 263168 activation_27[0][0]
____________________________________________________________________________________________________
merge_9 (Merge) (None, 2, 2, 1024) 0 merge_8[0][0]
convolution2d_31[0][0]
____________________________________________________________________________________________________
batchnormalization_28 (BatchNorm (None, 2, 2, 1024) 4096 merge_9[0][0]
____________________________________________________________________________________________________
activation_28 (Activation) (None, 2, 2, 1024) 0 batchnormalization_28[0][0]
____________________________________________________________________________________________________
convolution2d_32 (Convolution2D) (None, 2, 2, 256) 262400 activation_28[0][0]
____________________________________________________________________________________________________
batchnormalization_29 (BatchNorm (None, 2, 2, 256) 1024 convolution2d_32[0][0]
____________________________________________________________________________________________________
activation_29 (Activation) (None, 2, 2, 256) 0 batchnormalization_29[0][0]
____________________________________________________________________________________________________
convolution2d_33 (Convolution2D) (None, 2, 2, 256) 590080 activation_29[0][0]
____________________________________________________________________________________________________
batchnormalization_30 (BatchNorm (None, 2, 2, 256) 1024 convolution2d_33[0][0]
____________________________________________________________________________________________________
activation_30 (Activation) (None, 2, 2, 256) 0 batchnormalization_30[0][0]
____________________________________________________________________________________________________
convolution2d_34 (Convolution2D) (None, 2, 2, 1024) 263168 activation_30[0][0]
____________________________________________________________________________________________________
merge_10 (Merge) (None, 2, 2, 1024) 0 merge_9[0][0]
convolution2d_34[0][0]
____________________________________________________________________________________________________
batchnormalization_31 (BatchNorm (None, 2, 2, 1024) 4096 merge_10[0][0]
____________________________________________________________________________________________________
activation_31 (Activation) (None, 2, 2, 1024) 0 batchnormalization_31[0][0]
____________________________________________________________________________________________________
convolution2d_35 (Convolution2D) (None, 2, 2, 256) 262400 activation_31[0][0]
____________________________________________________________________________________________________
batchnormalization_32 (BatchNorm (None, 2, 2, 256) 1024 convolution2d_35[0][0]
____________________________________________________________________________________________________
activation_32 (Activation) (None, 2, 2, 256) 0 batchnormalization_32[0][0]
____________________________________________________________________________________________________
convolution2d_36 (Convolution2D) (None, 2, 2, 256) 590080 activation_32[0][0]
____________________________________________________________________________________________________
batchnormalization_33 (BatchNorm (None, 2, 2, 256) 1024 convolution2d_36[0][0]
____________________________________________________________________________________________________
activation_33 (Activation) (None, 2, 2, 256) 0 batchnormalization_33[0][0]
____________________________________________________________________________________________________
convolution2d_37 (Convolution2D) (None, 2, 2, 1024) 263168 activation_33[0][0]
____________________________________________________________________________________________________
merge_11 (Merge) (None, 2, 2, 1024) 0 merge_10[0][0]
convolution2d_37[0][0]
____________________________________________________________________________________________________
batchnormalization_34 (BatchNorm (None, 2, 2, 1024) 4096 merge_11[0][0]
____________________________________________________________________________________________________
activation_34 (Activation) (None, 2, 2, 1024) 0 batchnormalization_34[0][0]
____________________________________________________________________________________________________
convolution2d_38 (Convolution2D) (None, 2, 2, 256) 262400 activation_34[0][0]
____________________________________________________________________________________________________
batchnormalization_35 (BatchNorm (None, 2, 2, 256) 1024 convolution2d_38[0][0]
____________________________________________________________________________________________________
activation_35 (Activation) (None, 2, 2, 256) 0 batchnormalization_35[0][0]
____________________________________________________________________________________________________
convolution2d_39 (Convolution2D) (None, 2, 2, 256) 590080 activation_35[0][0]
____________________________________________________________________________________________________
batchnormalization_36 (BatchNorm (None, 2, 2, 256) 1024 convolution2d_39[0][0]
____________________________________________________________________________________________________
activation_36 (Activation) (None, 2, 2, 256) 0 batchnormalization_36[0][0]
____________________________________________________________________________________________________
convolution2d_40 (Convolution2D) (None, 2, 2, 1024) 263168 activation_36[0][0]
____________________________________________________________________________________________________
merge_12 (Merge) (None, 2, 2, 1024) 0 merge_11[0][0]
convolution2d_40[0][0]
____________________________________________________________________________________________________
batchnormalization_37 (BatchNorm (None, 2, 2, 1024) 4096 merge_12[0][0]
____________________________________________________________________________________________________
activation_37 (Activation) (None, 2, 2, 1024) 0 batchnormalization_37[0][0]
____________________________________________________________________________________________________
convolution2d_41 (Convolution2D) (None, 2, 2, 256) 262400 activation_37[0][0]
____________________________________________________________________________________________________
batchnormalization_38 (BatchNorm (None, 2, 2, 256) 1024 convolution2d_41[0][0]
____________________________________________________________________________________________________
activation_38 (Activation) (None, 2, 2, 256) 0 batchnormalization_38[0][0]
____________________________________________________________________________________________________
convolution2d_42 (Convolution2D) (None, 2, 2, 256) 590080 activation_38[0][0]
____________________________________________________________________________________________________
batchnormalization_39 (BatchNorm (None, 2, 2, 256) 1024 convolution2d_42[0][0]
____________________________________________________________________________________________________
activation_39 (Activation) (None, 2, 2, 256) 0 batchnormalization_39[0][0]
____________________________________________________________________________________________________
convolution2d_43 (Convolution2D) (None, 2, 2, 1024) 263168 activation_39[0][0]
____________________________________________________________________________________________________
merge_13 (Merge) (None, 2, 2, 1024) 0 merge_12[0][0]
convolution2d_43[0][0]
____________________________________________________________________________________________________
batchnormalization_40 (BatchNorm (None, 2, 2, 1024) 4096 merge_13[0][0]
____________________________________________________________________________________________________
activation_40 (Activation) (None, 2, 2, 1024) 0 batchnormalization_40[0][0]
____________________________________________________________________________________________________
convolution2d_44 (Convolution2D) (None, 1, 1, 512) 524800 activation_40[0][0]
____________________________________________________________________________________________________
batchnormalization_41 (BatchNorm (None, 1, 1, 512) 2048 convolution2d_44[0][0]
____________________________________________________________________________________________________
activation_41 (Activation) (None, 1, 1, 512) 0 batchnormalization_41[0][0]
____________________________________________________________________________________________________
convolution2d_45 (Convolution2D) (None, 1, 1, 512) 2359808 activation_41[0][0]
____________________________________________________________________________________________________
batchnormalization_42 (BatchNorm (None, 1, 1, 512) 2048 convolution2d_45[0][0]
____________________________________________________________________________________________________
activation_42 (Activation) (None, 1, 1, 512) 0 batchnormalization_42[0][0]
____________________________________________________________________________________________________
convolution2d_47 (Convolution2D) (None, 1, 1, 2048) 2099200 merge_13[0][0]
____________________________________________________________________________________________________
convolution2d_46 (Convolution2D) (None, 1, 1, 2048) 1050624 activation_42[0][0]
____________________________________________________________________________________________________
merge_14 (Merge) (None, 1, 1, 2048) 0 convolution2d_47[0][0]
convolution2d_46[0][0]
____________________________________________________________________________________________________
batchnormalization_43 (BatchNorm (None, 1, 1, 2048) 8192 merge_14[0][0]
____________________________________________________________________________________________________
activation_43 (Activation) (None, 1, 1, 2048) 0 batchnormalization_43[0][0]
____________________________________________________________________________________________________
convolution2d_48 (Convolution2D) (None, 1, 1, 512) 1049088 activation_43[0][0]
____________________________________________________________________________________________________
batchnormalization_44 (BatchNorm (None, 1, 1, 512) 2048 convolution2d_48[0][0]
____________________________________________________________________________________________________
activation_44 (Activation) (None, 1, 1, 512) 0 batchnormalization_44[0][0]
____________________________________________________________________________________________________
convolution2d_49 (Convolution2D) (None, 1, 1, 512) 2359808 activation_44[0][0]
____________________________________________________________________________________________________
batchnormalization_45 (BatchNorm (None, 1, 1, 512) 2048 convolution2d_49[0][0]
____________________________________________________________________________________________________
activation_45 (Activation) (None, 1, 1, 512) 0 batchnormalization_45[0][0]
____________________________________________________________________________________________________
convolution2d_50 (Convolution2D) (None, 1, 1, 2048) 1050624 activation_45[0][0]
____________________________________________________________________________________________________
merge_15 (Merge) (None, 1, 1, 2048) 0 merge_14[0][0]
convolution2d_50[0][0]
____________________________________________________________________________________________________
batchnormalization_46 (BatchNorm (None, 1, 1, 2048) 8192 merge_15[0][0]
____________________________________________________________________________________________________
activation_46 (Activation) (None, 1, 1, 2048) 0 batchnormalization_46[0][0]
____________________________________________________________________________________________________
convolution2d_51 (Convolution2D) (None, 1, 1, 512) 1049088 activation_46[0][0]
____________________________________________________________________________________________________
batchnormalization_47 (BatchNorm (None, 1, 1, 512) 2048 convolution2d_51[0][0]
____________________________________________________________________________________________________
activation_47 (Activation) (None, 1, 1, 512) 0 batchnormalization_47[0][0]
____________________________________________________________________________________________________
convolution2d_52 (Convolution2D) (None, 1, 1, 512) 2359808 activation_47[0][0]
____________________________________________________________________________________________________
batchnormalization_48 (BatchNorm (None, 1, 1, 512) 2048 convolution2d_52[0][0]
____________________________________________________________________________________________________
activation_48 (Activation) (None, 1, 1, 512) 0 batchnormalization_48[0][0]
____________________________________________________________________________________________________
convolution2d_53 (Convolution2D) (None, 1, 1, 2048) 1050624 activation_48[0][0]
____________________________________________________________________________________________________
merge_16 (Merge) (None, 1, 1, 2048) 0 merge_15[0][0]
convolution2d_53[0][0]
____________________________________________________________________________________________________
batchnormalization_49 (BatchNorm (None, 1, 1, 2048) 8192 merge_16[0][0]
____________________________________________________________________________________________________
activation_49 (Activation) (None, 1, 1, 2048) 0 batchnormalization_49[0][0]
____________________________________________________________________________________________________
averagepooling2d_1 (AveragePooli (None, 1, 1, 2048) 0 activation_49[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 2048) 0 averagepooling2d_1[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 10) 20490 flatten_1[0][0]
====================================================================================================
Total params: 23,592,842
Trainable params: 23,547,402
Non-trainable params: 45,440
_____________________________________________2017-09-03 14:43:00.457499: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-03 14:43:00.457554: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-03 14:43:00.457562: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-09-03 14:43:00.457567: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-03 14:43:00.457572: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-09-03 14:43:01.490559: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-09-03 14:43:01.491533: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID 0000:00:17.0
Total memory: 11.17GiB
Free memory: 11.11GiB
2017-09-03 14:43:01.622547: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0xb17c660 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-09-03 14:43:01.623114: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-09-03 14:43:01.624081: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 1 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID 0000:00:18.0
Total memory: 11.17GiB
Free memory: 11.11GiB
2017-09-03 14:43:01.758220: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0xb1804b0 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-09-03 14:43:01.758794: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-09-03 14:43:01.759726: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 2 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID 0000:00:19.0
Total memory: 11.17GiB
Free memory: 11.11GiB
2017-09-03 14:43:01.898248: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0xb1a4370 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-09-03 14:43:01.898831: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-09-03 14:43:01.899750: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 3 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID 0000:00:1a.0
Total memory: 11.17GiB
Free memory: 11.11GiB
2017-09-03 14:43:02.051807: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0xb1a81f0 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-09-03 14:43:02.052420: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-09-03 14:43:02.053331: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 4 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID 0000:00:1b.0
Total memory: 11.17GiB
Free memory: 11.11GiB
2017-09-03 14:43:02.197523: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0xb1ac070 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-09-03 14:43:02.198127: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-09-03 14:43:02.199023: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 5 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID 0000:00:1c.0
Total memory: 11.17GiB
Free memory: 11.11GiB
2017-09-03 14:43:02.346372: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0xb1afef0 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-09-03 14:43:02.346988: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-09-03 14:43:02.347892: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 6 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID 0000:00:1d.0
Total memory: 11.17GiB
Free memory: 11.11GiB
2017-09-03 14:43:02.498687: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0xb1b3d70 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-09-03 14:43:02.499312: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:893] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2017-09-03 14:43:02.500204: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 7 with properties:
name: Tesla K80
major: 3 minor: 7 memoryClockRate (GHz) 0.8235
pciBusID 0000:00:1e.0
Total memory: 11.17GiB
Free memory: 11.11GiB
2017-09-03 14:43:02.531654: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0 1 2 3 4 5 6 7
2017-09-03 14:43:02.531681: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: Y Y Y Y Y Y Y Y
2017-09-03 14:43:02.531689: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 1: Y Y Y Y Y Y Y Y
2017-09-03 14:43:02.531694: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 2: Y Y Y Y Y Y Y Y
2017-09-03 14:43:02.531704: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 3: Y Y Y Y Y Y Y Y
2017-09-03 14:43:02.531709: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 4: Y Y Y Y Y Y Y Y
2017-09-03 14:43:02.531713: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 5: Y Y Y Y Y Y Y Y
2017-09-03 14:43:02.531718: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 6: Y Y Y Y Y Y Y Y
2017-09-03 14:43:02.531724: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 7: Y Y Y Y Y Y Y Y
2017-09-03 14:43:02.531743: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K80, pci bus id: 0000:00:17.0)
2017-09-03 14:43:02.531757: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:1) -> (device: 1, name: Tesla K80, pci bus id: 0000:00:18.0)
2017-09-03 14:43:02.531764: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:2) -> (device: 2, name: Tesla K80, pci bus id: 0000:00:19.0)
2017-09-03 14:43:02.531769: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:3) -> (device: 3, name: Tesla K80, pci bus id: 0000:00:1a.0)
2017-09-03 14:43:02.531779: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:4) -> (device: 4, name: Tesla K80, pci bus id: 0000:00:1b.0)
2017-09-03 14:43:02.531799: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:5) -> (device: 5, name: Tesla K80, pci bus id: 0000:00:1c.0)
2017-09-03 14:43:02.531810: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:6) -> (device: 6, name: Tesla K80, pci bus id: 0000:00:1d.0)
2017-09-03 14:43:02.531816: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:7) -> (device: 7, name: Tesla K80, pci bus id: 0000:00:1e.0)
_______________________________________________________
Not using data augmentation.
Train on 50000 samples, validate on 10000 samples
Epoch 1/100
256/50000 [..............................] - ETA: 1765s - loss: 8.1725 - acc: 0.08592017-09-03 14:43:09.676270: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:247] PoolAllocator: After 2738 get requests, put_count=2396 evicted_count=1000 eviction_rate=0.417362 and unsatisfied allocation rate=0.526662
2017-09-03 14:43:09.676316: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:259] Raising pool_size_limit_ from 100 to 110
4608/50000 [=>............................] - ETA: 139s - loss: 7.6232 - acc: 0.17062017-09-03 14:43:14.731992: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:247] PoolAllocator: After 2759 get requests, put_count=2755 evicted_count=1000 eviction_rate=0.362976 and unsatisfied allocation rate=0.372236
2017-09-03 14:43:14.732049: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:259] Raising pool_size_limit_ from 256 to 281
14080/50000 [=======>......................] - ETA: 64s - loss: 7.4203 - acc: 0.24422017-09-03 14:43:25.755486: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:247] PoolAllocator: After 12431 get requests, put_count=12392 evicted_count=1000 eviction_rate=0.0806972 and unsatisfied allocation rate=0.0883276
2017-09-03 14:43:25.755523: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:259] Raising pool_size_limit_ from 655 to 720
50000/50000 [==============================] - 70s - loss: 7.1857 - acc: 0.3267 - val_loss: 7.2997 - val_acc: 0.2653
Epoch 2/100
50000/50000 [==============================] - 61s - loss: 6.8159 - acc: 0.4598 - val_loss: 6.8701 - val_acc: 0.4306
Epoch 3/100
50000/50000 [==============================] - 61s - loss: 6.6397 - acc: 0.5265 - val_loss: 6.7603 - val_acc: 0.4792
Epoch 4/100
50000/50000 [==============================] - 61s - loss: 6.5083 - acc: 0.5768 - val_loss: 6.7063 - val_acc: 0.4932
Epoch 5/100
50000/50000 [==============================] - 61s - loss: 6.3949 - acc: 0.6188 - val_loss: 6.6920 - val_acc: 0.4987
Epoch 6/100
50000/50000 [==============================] - 61s - loss: 6.2882 - acc: 0.6601 - val_loss: 6.6401 - val_acc: 0.5146
Epoch 7/100
50000/50000 [==============================] - 61s - loss: 6.1910 - acc: 0.6974 - val_loss: 6.6603 - val_acc: 0.5184
Epoch 8/100
50000/50000 [==============================] - 61s - loss: 6.0969 - acc: 0.7364 - val_loss: 6.6073 - val_acc: 0.5321
Epoch 9/100
50000/50000 [==============================] - 61s - loss: 6.0040 - acc: 0.7719 - val_loss: 6.6547 - val_acc: 0.5229
Epoch 10/100
50000/50000 [==============================] - 61s - loss: 5.9160 - acc: 0.8080 - val_loss: 6.6353 - val_acc: 0.5326
Epoch 11/100
50000/50000 [==============================] - 61s - loss: 5.8295 - acc: 0.8394 - val_loss: 6.6564 - val_acc: 0.5251
Epoch 12/100
50000/50000 [==============================] - 61s - loss: 5.7461 - acc: 0.8729 - val_loss: 6.7699 - val_acc: 0.5019
Epoch 13/100
50000/50000 [==============================] - 61s - loss: 5.6696 - acc: 0.9013 - val_loss: 6.6961 - val_acc: 0.5268
Epoch 14/100
50000/50000 [==============================] - 61s - loss: 5.5946 - acc: 0.9272 - val_loss: 6.6918 - val_acc: 0.5350
Epoch 15/100
50000/50000 [==============================] - 61s - loss: 5.5312 - acc: 0.9480 - val_loss: 6.7218 - val_acc: 0.5392
Epoch 16/100
50000/50000 [==============================] - 61s - loss: 5.4717 - acc: 0.9668 - val_loss: 6.7499 - val_acc: 0.5355
Epoch 17/100
50000/50000 [==============================] - 61s - loss: 5.4200 - acc: 0.9792 - val_loss: 6.7796 - val_acc: 0.5311
Epoch 18/100
50000/50000 [==============================] - 61s - loss: 5.3799 - acc: 0.9887 - val_loss: 6.8065 - val_acc: 0.5354
Epoch 19/100
50000/50000 [==============================] - 61s - loss: 5.3466 - acc: 0.9929 - val_loss: 6.8559 - val_acc: 0.5354
Epoch 20/100
50000/50000 [==============================] - 61s - loss: 5.3170 - acc: 0.9971 - val_loss: 6.8801 - val_acc: 0.5360
Epoch 21/100
50000/50000 [==============================] - 61s - loss: 5.2986 - acc: 0.9978 - val_loss: 6.8924 - val_acc: 0.5367
Epoch 22/100
42240/50000 [========================>.....] - ETA: 8s - loss: 5.2799 - acc: 0.99882017-09-03 15:05:21.906162: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:247] PoolAllocator: After 5883622 get requests, put_count=5883661 evicted_count=9000 eviction_rate=0.00152966 and unsatisfied allocation rate=0.00153528
50000/50000 [==============================] - 61s - loss: 5.2804 - acc: 0.9987 - val_loss: 6.9453 - val_acc: 0.5296
Epoch 23/100
50000/50000 [==============================] - 61s - loss: 5.2676 - acc: 0.9990 - val_loss: 6.9665 - val_acc: 0.5343
Epoch 24/100
50000/50000 [==============================] - 61s - loss: 5.2549 - acc: 0.9996 - val_loss: 6.9879 - val_acc: 0.5413
Epoch 25/100
50000/50000 [==============================] - 61s - loss: 5.2426 - acc: 0.9997 - val_loss: 7.0565 - val_acc: 0.5280
Epoch 26/100
50000/50000 [==============================] - 61s - loss: 5.2346 - acc: 0.9999 - val_loss: 7.0199 - val_acc: 0.5364
Epoch 27/100
50000/50000 [==============================] - 61s - loss: 5.2266 - acc: 0.9998 - val_loss: 7.0240 - val_acc: 0.5390
Epoch 28/100
50000/50000 [==============================] - 61s - loss: 5.2201 - acc: 0.9999 - val_loss: 7.0888 - val_acc: 0.5307
Epoch 29/100
50000/50000 [==============================] - 61s - loss: 5.2135 - acc: 0.9999 - val_loss: 7.0488 - val_acc: 0.5435
Epoch 30/100
50000/50000 [==============================] - 61s - loss: 5.2071 - acc: 0.9998 - val_loss: 7.0626 - val_acc: 0.5388
Epoch 31/100
50000/50000 [==============================] - 61s - loss: 5.2011 - acc: 0.9998 - val_loss: 7.0897 - val_acc: 0.5372
Epoch 32/100
50000/50000 [==============================] - 61s - loss: 5.1939 - acc: 0.9999 - val_loss: 7.0768 - val_acc: 0.5447
Epoch 33/100
50000/50000 [==============================] - 61s - loss: 5.1893 - acc: 1.0000 - val_loss: 7.1017 - val_acc: 0.5393
Epoch 34/100
50000/50000 [==============================] - 61s - loss: 5.1833 - acc: 1.0000 - val_loss: 7.1093 - val_acc: 0.5426
Epoch 35/100
50000/50000 [==============================] - 61s - loss: 5.1783 - acc: 1.0000 - val_loss: 7.1411 - val_acc: 0.5383
Epoch 36/100
50000/50000 [==============================] - 61s - loss: 5.1732 - acc: 0.9999 - val_loss: 7.1277 - val_acc: 0.5476
Epoch 37/100
50000/50000 [==============================] - 61s - loss: 5.1687 - acc: 1.0000 - val_loss: 7.1562 - val_acc: 0.5444
Epoch 38/100
50000/50000 [==============================] - 61s - loss: 5.1634 - acc: 1.0000 - val_loss: 7.1542 - val_acc: 0.5447
Epoch 39/100
50000/50000 [==============================] - 61s - loss: 5.1588 - acc: 0.9999 - val_loss: 7.1486 - val_acc: 0.5423
Epoch 40/100
50000/50000 [==============================] - 61s - loss: 5.1544 - acc: 1.0000 - val_loss: 7.1837 - val_acc: 0.5406
Epoch 41/100
50000/50000 [==============================] - 61s - loss: 5.1500 - acc: 1.0000 - val_loss: 7.1638 - val_acc: 0.5464
Epoch 42/100
50000/50000 [==============================] - 61s - loss: 5.1452 - acc: 1.0000 - val_loss: 7.2003 - val_acc: 0.5443
Epoch 43/100
50000/50000 [==============================] - 61s - loss: 5.1406 - acc: 1.0000 - val_loss: 7.1929 - val_acc: 0.5399
Epoch 44/100
50000/50000 [==============================] - 61s - loss: 5.1365 - acc: 1.0000 - val_loss: 7.1899 - val_acc: 0.5456
Epoch 45/100
48640/50000 [============================>.] - ETA: 1s - loss: 5.1319 - acc: 1.00002017-09-03 15:28:54.388651: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:247] PoolAllocator: After 12225786 get requests, put_count=12225831 evicted_count=19000 eviction_rate=0.00155409 and unsatisfied allocation rate=0.0015563
50000/50000 [==============================] - 61s - loss: 5.1323 - acc: 0.9999 - val_loss: 7.4057 - val_acc: 0.5100
Epoch 46/100
50000/50000 [==============================] - 61s - loss: 5.1286 - acc: 1.0000 - val_loss: 7.2061 - val_acc: 0.5418
Epoch 47/100
50000/50000 [==============================] - 61s - loss: 5.1236 - acc: 0.9999 - val_loss: 7.2082 - val_acc: 0.5431
Epoch 48/100
50000/50000 [==============================] - 61s - loss: 5.1188 - acc: 1.0000 - val_loss: 7.2193 - val_acc: 0.5398
Epoch 49/100
50000/50000 [==============================] - 61s - loss: 5.1143 - acc: 1.0000 - val_loss: 7.2193 - val_acc: 0.5449
Epoch 50/100
50000/50000 [==============================] - 61s - loss: 5.1100 - acc: 1.0000 - val_loss: 7.2264 - val_acc: 0.5414
Epoch 51/100
50000/50000 [==============================] - 61s - loss: 5.1058 - acc: 0.9999 - val_loss: 7.8771 - val_acc: 0.4546
Epoch 52/100
50000/50000 [==============================] - 61s - loss: 5.1033 - acc: 0.9998 - val_loss: 7.2290 - val_acc: 0.5452
Epoch 53/100
50000/50000 [==============================] - 61s - loss: 5.0980 - acc: 0.9999 - val_loss: 7.2627 - val_acc: 0.5371
Epoch 54/100
50000/50000 [==============================] - 61s - loss: 5.0937 - acc: 1.0000 - val_loss: 7.2594 - val_acc: 0.5406
Epoch 55/100
50000/50000 [==============================] - 61s - loss: 5.0896 - acc: 1.0000 - val_loss: 7.3483 - val_acc: 0.5327
Epoch 56/100
50000/50000 [==============================] - 61s - loss: 5.0853 - acc: 1.0000 - val_loss: 7.2470 - val_acc: 0.5423
Epoch 57/100
50000/50000 [==============================] - 61s - loss: 5.0808 - acc: 1.0000 - val_loss: 7.2485 - val_acc: 0.5454
Epoch 58/100
50000/50000 [==============================] - 61s - loss: 5.0766 - acc: 1.0000 - val_loss: 7.2642 - val_acc: 0.5455
Epoch 59/100
50000/50000 [==============================] - 61s - loss: 5.0727 - acc: 1.0000 - val_loss: 7.2806 - val_acc: 0.5413
Epoch 60/100
50000/50000 [==============================] - 61s - loss: 5.0682 - acc: 1.0000 - val_loss: 7.2613 - val_acc: 0.5422
Epoch 61/100
50000/50000 [==============================] - 61s - loss: 5.0643 - acc: 1.0000 - val_loss: 7.2641 - val_acc: 0.5439
Epoch 62/100
50000/50000 [==============================] - 61s - loss: 5.0605 - acc: 1.0000 - val_loss: 7.2669 - val_acc: 0.5428
Epoch 63/100
50000/50000 [==============================] - 61s - loss: 5.0564 - acc: 1.0000 - val_loss: 7.3457 - val_acc: 0.5339
Epoch 64/100
50000/50000 [==============================] - 61s - loss: 5.0524 - acc: 1.0000 - val_loss: 7.2703 - val_acc: 0.5454
Epoch 65/100
50000/50000 [==============================] - 61s - loss: 5.0480 - acc: 1.0000 - val_loss: 7.2840 - val_acc: 0.5491
Epoch 66/100
50000/50000 [==============================] - 61s - loss: 5.0439 - acc: 0.9999 - val_loss: 7.2937 - val_acc: 0.5407
Epoch 67/100
50000/50000 [==============================] - 61s - loss: 5.0405 - acc: 1.0000 - val_loss: 7.2798 - val_acc: 0.5428
Epoch 68/100
50000/50000 [==============================] - 61s - loss: 5.0358 - acc: 1.0000 - val_loss: 7.2852 - val_acc: 0.5427
Epoch 69/100
23552/50000 [=============>................] - ETA: 30s - loss: 5.0328 - acc: 1.00002017-09-03 15:52:51.483115: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:247] PoolAllocator: After 18670556 get requests, put_count=18670596 evicted_count=29000 eviction_rate=0.00155324 and unsatisfied allocation rate=0.00155496
50000/50000 [==============================] - 61s - loss: 5.0316 - acc: 1.0000 - val_loss: 7.2852 - val_acc: 0.5432
Epoch 70/100
50000/50000 [==============================] - 61s - loss: 5.0279 - acc: 1.0000 - val_loss: 7.2800 - val_acc: 0.5424
Epoch 71/100
50000/50000 [==============================] - 61s - loss: 5.0238 - acc: 1.0000 - val_loss: 7.2971 - val_acc: 0.5403
Epoch 72/100
50000/50000 [==============================] - 61s - loss: 5.0198 - acc: 1.0000 - val_loss: 7.2910 - val_acc: 0.5444
Epoch 73/100
50000/50000 [==============================] - 61s - loss: 5.0158 - acc: 1.0000 - val_loss: 7.2863 - val_acc: 0.5435
Epoch 74/100
50000/50000 [==============================] - 61s - loss: 5.0115 - acc: 1.0000 - val_loss: 7.2953 - val_acc: 0.5459
Epoch 75/100
50000/50000 [==============================] - 61s - loss: 5.0075 - acc: 1.0000 - val_loss: 7.2881 - val_acc: 0.5445
Epoch 76/100
50000/50000 [==============================] - 61s - loss: 5.0037 - acc: 1.0000 - val_loss: 7.3009 - val_acc: 0.5450
Epoch 77/100
50000/50000 [==============================] - 61s - loss: 4.9996 - acc: 1.0000 - val_loss: 7.2892 - val_acc: 0.5435
Epoch 78/100
50000/50000 [==============================] - 61s - loss: 4.9957 - acc: 1.0000 - val_loss: 7.3151 - val_acc: 0.5409
Epoch 79/100
50000/50000 [==============================] - 61s - loss: 4.9918 - acc: 1.0000 - val_loss: 7.2926 - val_acc: 0.5448
Epoch 80/100
50000/50000 [==============================] - 61s - loss: 4.9878 - acc: 1.0000 - val_loss: 7.2941 - val_acc: 0.5444
Epoch 81/100
50000/50000 [==============================] - 61s - loss: 4.9840 - acc: 0.9999 - val_loss: 7.5761 - val_acc: 0.4974
Epoch 82/100
50000/50000 [==============================] - 61s - loss: 4.9809 - acc: 0.9999 - val_loss: 7.3123 - val_acc: 0.5417
Epoch 83/100
50000/50000 [==============================] - 61s - loss: 4.9762 - acc: 1.0000 - val_loss: 7.3238 - val_acc: 0.5401
Epoch 84/100
50000/50000 [==============================] - 61s - loss: 4.9722 - acc: 1.0000 - val_loss: 7.3015 - val_acc: 0.5422
Epoch 85/100
50000/50000 [==============================] - 61s - loss: 4.9681 - acc: 1.0000 - val_loss: 7.3356 - val_acc: 0.5429
Epoch 86/100
50000/50000 [==============================] - 61s - loss: 4.9640 - acc: 1.0000 - val_loss: 7.3005 - val_acc: 0.5435
Epoch 87/100
50000/50000 [==============================] - 61s - loss: 4.9601 - acc: 1.0000 - val_loss: 7.3229 - val_acc: 0.5398
Epoch 88/100
50000/50000 [==============================] - 61s - loss: 4.9563 - acc: 1.0000 - val_loss: 7.3043 - val_acc: 0.5433
Epoch 89/100
50000/50000 [==============================] - 61s - loss: 4.9522 - acc: 1.0000 - val_loss: 7.2981 - val_acc: 0.5434
Epoch 90/100
50000/50000 [==============================] - 61s - loss: 4.9482 - acc: 1.0000 - val_loss: 7.3041 - val_acc: 0.5416
Epoch 91/100
50000/50000 [==============================] - 61s - loss: 4.9445 - acc: 1.0000 - val_loss: 7.3078 - val_acc: 0.5423
Epoch 92/100
28672/50000 [================>.............] - ETA: 24s - loss: 4.9414 - acc: 1.00002017-09-03 16:16:21.867984: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:247] PoolAllocator: After 25004707 get requests, put_count=25004751 evicted_count=39000 eviction_rate=0.0015597 and unsatisfied allocation rate=0.00156083
50000/50000 [==============================] - 61s - loss: 4.9407 - acc: 0.9999 - val_loss: 7.4865 - val_acc: 0.5221
Epoch 93/100
50000/50000 [==============================] - 61s - loss: 4.9375 - acc: 1.0000 - val_loss: 7.2946 - val_acc: 0.5445
Epoch 94/100
50000/50000 [==============================] - 61s - loss: 4.9329 - acc: 1.0000 - val_loss: 7.2940 - val_acc: 0.5452
Epoch 95/100
50000/50000 [==============================] - 61s - loss: 4.9290 - acc: 1.0000 - val_loss: 7.3612 - val_acc: 0.5342
Epoch 96/100
50000/50000 [==============================] - 61s - loss: 4.9256 - acc: 1.0000 - val_loss: 7.3948 - val_acc: 0.5277
Epoch 97/100
50000/50000 [==============================] - 61s - loss: 4.9215 - acc: 1.0000 - val_loss: 7.3196 - val_acc: 0.5451
Epoch 98/100
50000/50000 [==============================] - 61s - loss: 4.9174 - acc: 1.0000 - val_loss: 7.3004 - val_acc: 0.5423
Epoch 99/100
50000/50000 [==============================] - 61s - loss: 4.9133 - acc: 1.0000 - val_loss: 7.3111 - val_acc: 0.5458
Epoch 100/100
50000/50000 [==============================] - 61s - loss: 4.9095 - acc: 1.0000 - val_loss: 7.3092 - val_acc: 0.5438
Model training complete.
TRAINING ACCURACY - 1.0
TEST ACCURACY - 0.5438
6093.16user 1331.26system 1:42:22elapsed 120%CPU (0avgtext+0avgdata 5763920maxresident)k
6976inputs+8056outputs (35major+373703minor)pagefaults 0swaps
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