Question

我正在运行一些二进制图像分类问题的教程代码。它是一个非常简单的架构（3卷积/ relu /池化+完全连接），但每个时期的最后训练步骤需要约130秒，而前127个总共花费20秒。任何人都可以解释这一点，我能以某种方式加快速度吗？我正在使用2GB VRAM在GPU上运行。

rmsprop = optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(loss='binary_crossentropy',
          optimizer=rmsprop,
          metrics=['accuracy'])

nb_epoch = 30
nb_train_samples = 2048
nb_validation_samples = 832


model.summary()
model.fit_generator(
    train_generator,
    samples_per_epoch=nb_train_samples,
    nb_epoch=nb_epoch,
    validation_data=validation_generator,
    nb_val_samples=nb_validation_samples)

127/128 [============================>.] - ETA: 0s - loss: 0.7302 - acc: 0.5266

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 148, 148, 32)      896       
_________________________________________________________________
activation_1 (Activation)    (None, 148, 148, 32)      0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 74, 74, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 72, 72, 32)        9248      
_________________________________________________________________
activation_2 (Activation)    (None, 72, 72, 32)        0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 36, 36, 32)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 34, 34, 64)        18496     
_________________________________________________________________
activation_3 (Activation)    (None, 34, 34, 64)        0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 17, 17, 64)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 18496)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 64)                1183808   
_________________________________________________________________
activation_4 (Activation)    (None, 64)                0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 65        
_________________________________________________________________
activation_5 (Activation)    (None, 1)                 0         
=================================================================
Total params: 1,212,513.0
Trainable params: 1,212,513.0
Non-trainable params: 0.0
_________________________________________________________________

Answer 1

您的脚本可能会保存或验证，而不会在您的ETA计算中声明这一点

在训练一个小天使时，为什么每个时代的最后阶段需要这么长时间？

1 个答案: