当我使用 tensorflow 时出现错误 Segmentation fault (core dumped)

Question

arise error Segmentation fault (core dumped) when I use tensorflow, my code is :

from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Embedding
from keras.layers import LSTM
from keras.utils import to_categorical

model = Sequential()
max_features = 100
model.add(Embedding(max_features, output_dim=256))
model.add(LSTM(128))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

print("model is ok ")

Answer 1

Segmentation fault means that you tried to access memory that you do not have access. If you choose right combination of Tensorflow , CUDA and cuDNN will solve this issue. You can refer tested build configuration .

I was able to execute above code without any issues. Please refer the same as shown below

import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Embedding, LSTM

model = Sequential()
max_features = 100
model.add(Embedding(max_features, output_dim=256))
model.add(LSTM(128))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.summary()

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

print("model is ok ")

Output:

2.5.0
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding (Embedding)        (None, None, 256)         25600     
_________________________________________________________________
lstm (LSTM)                  (None, 128)               197120    
_________________________________________________________________
dropout (Dropout)            (None, 128)               0         
_________________________________________________________________
dense (Dense)                (None, 1)                 129       
=================================================================
Total params: 222,849
Trainable params: 222,849
Non-trainable params: 0
_________________________________________________________________
model is ok