我有相当简单的架构LSTM nn。在经历了几次“时代1-2”之后,我的电脑完全冻结了,我甚至连鼠标都动不了:
Layer (type) Output Shape Param #
=================================================================
lstm_4 (LSTM) (None, 128) 116224
_________________________________________________________________
dropout_3 (Dropout) (None, 128) 0
_________________________________________________________________
dense_5 (Dense) (None, 98) 12642
=================================================================
Total params: 128,866
Trainable params: 128,866
Non-trainable params: 0
# Same problem with 2 layers LSTM with dropout and Adam optimizer
SEQUENCE_LENGTH =3, len(chars) = 98
model = Sequential()
model.add(LSTM(128, input_shape = (SEQUENCE_LENGTH, len(chars))))
#model.add(Dropout(0.15))
#model.add(LSTM(128))
model.add(Dropout(0.10))
model.add(Dense(len(chars), activation = 'softmax'))
model.compile(loss = 'categorical_crossentropy', optimizer = RMSprop(lr=0.01), metrics=['accuracy'])
我就是这样训练的:
history = model.fit(X, y, validation_split=0.20, batch_size=128, epochs=10, shuffle=True,verbose=2).history
NN需要5分钟才能完成1个时代。批量越大并不意味着问题发生得越快。但是更复杂的模型可以训练更多的时间来达到几乎相同的精度-大约0.46(完整代码
here
)
我有最新的Linux Mint,1070ti,8GB,32GB RAM
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 396.26 Driver Version: 396.26 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 107... Off | 00000000:08:00.0 On | N/A |
| 0% 35C P8 10W / 180W | 303MiB / 8116MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
库:
Keras==2.2.0
Keras-Applications==1.0.2
Keras-Preprocessing==1.0.1
keras-sequential-ascii==0.1.1
keras-tqdm==2.0.1
tensorboard==1.8.0
tensorflow==1.0.1
tensorflow-gpu==1.8.0
我试过限制GPU内存的使用,但这里不会有问题,因为在培训期间,它只消耗1 GB的GPU内存:
from keras.backend.tensorflow_backend
import set_session config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
config.gpu_options.allow_growth = True set_session(tf.Session(config=config))
这里怎么了?我怎样才能解决这个问题?
