Question

嗨......我在我的P3 AWS机器上运行mnist代码，与我之前的P2机器相比，初始化过程似乎很长（尽管P3＆gt; P2）

Train on 60000 samples, validate on 10000 samples
Epoch 1/10
60000/60000 [==============================] - 265s 4ms/step - loss: 0.2674 - acc: 0.9175 - val_loss: 0.0602 - val_acc: 0.9811
Epoch 2/10
60000/60000 [==============================] - 3s 51us/step - loss: 0.0860 - acc: 0.9742 - val_loss: 0.0393 - val_acc: 0.9866
Epoch 3/10
60000/60000 [==============================] - 3s 50us/step - loss: 0.0647 - acc: 0.9808 - val_loss: 0.0338 - val_acc: 0.9884
Epoch 4/10
60000/60000 [==============================] - 3s 50us/step - loss: 0.0542 - acc: 0.9839 - val_loss: 0.0337 - val_acc: 0.9887
Epoch 5/10
60000/60000 [==============================] - 3s 50us/step - loss: 0.0453 - acc: 0.9863 - val_loss: 0.0311 - val_acc: 0.9900
Epoch 6/10
60000/60000 [==============================] - 3s 51us/step - loss: 0.0412 - acc: 0.9873 - val_loss: 0.0291 - val_acc: 0.9898
Epoch 7/10
60000/60000 [==============================] - 3s 50us/step - loss: 0.0368 - acc: 0.9891 - val_loss: 0.0300 - val_acc: 0.9901
Epoch 8/10
60000/60000 [==============================] - 3s 50us/step - loss: 0.0340 - acc: 0.9897 - val_loss: 0.0298 - val_acc: 0.9897
Epoch 9/10
60000/60000 [==============================] - 3s 50us/step - loss: 0.0320 - acc: 0.9908 - val_loss: 0.0267 - val_acc: 0.9916
Epoch 10/10
60000/60000 [==============================] - 3s 50us/step - loss: 0.0286 - acc: 0.9914 - val_loss: 0.0276 - val_acc: 0.9903
Test loss: 0.02757222411266339
Test accuracy: 0.9903

我正在使用Keras = 2.1.4 tensorflow-GPU = 1.5.0

我的keras.json文件配置如下：

{
    "floatx": "float32",
    "epsilon": 1e-07,
    "backend": "tensorflow",
    "image_data_format": "channels_last"
}

任何想法为什么会这样？

提前致谢

Answer 1

基于此issue：

第一个纪元需要相同的时间，但计数器也要计入通过构建计算图的一部分来考虑所花费的时间处理培训（几秒钟）。这曾经在 compile步骤，但是现在懒惰地完成了一项避免不必要的工作。

第一次训练时代非常缓慢

1 个答案: