当我使用张量流时出现错误分段错误（核心转储）

Question

当使用tensorflow时出现分段错误（核心转储）错误，我的代码是：

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 表示您尝试访问您无权访问的内存。如果您选择正确的 Tensorflow 组合，CUDA 和 cuDNN 将解决此问题。您可以参考tested build configuration。

我能够毫无问题地执行上述代码。请参考下图

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 ")

输出：

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