结果不能用Keras和TensorFlow在Python中重现

Question

我遇到问题，我无法重现Keras和ThensorFlow的结果。

好像最近也一直在Keras documentation site发布针对此问题的解决方法，但不知何故，没有为我工作。

我做错了什么？

我使用一个MBP视网膜Jupyter笔记本（不Nvidia的GPU）。

# ** Workaround from Keras Documentation ** 

import numpy as np 
import tensorflow as tf 
import random as rn 

# The below is necessary in Python 3.2.3 onwards to 
# have reproducible behavior for certain hash-based operations. 
# See these references for further details: 
# https://docs.python.org/3.4/using/cmdline.html#envvar-PYTHONHASHSEED 
# https://github.com/fchollet/keras/issues/2280#issuecomment-306959926 

import os 
os.environ['PYTHONHASHSEED'] = '0' 

# The below is necessary for starting Numpy generated random numbers 
# in a well-defined initial state. 

np.random.seed(42) 

# The below is necessary for starting core Python generated random numbers 
# in a well-defined state. 

rn.seed(12345) 

# Force TensorFlow to use single thread. 
# Multiple threads are a potential source of 
# non-reproducible results. 
# For further details, see: https://stackoverflow.com/questions/42022950/which-seeds-have-to-be-set-where-to-realize-100-reproducibility-of-training-res 

session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1) 

from keras import backend as K 

# The below tf.set_random_seed() will make random number generation 
# in the TensorFlow backend have a well-defined initial state. 
# For further details, see: https://www.tensorflow.org/api_docs/python/tf/set_random_seed 

tf.set_random_seed(1234) 

sess = tf.Session(graph=tf.get_default_graph(), config=session_conf) 
K.set_session(sess) 


# ** Workaround end ** 

# ** Start of my code ** 


# LSTM and CNN for sequence classification in the IMDB dataset 
from keras.models import Sequential 
from keras.layers import Dense 
from keras.layers import LSTM 
from keras.layers.embeddings import Embedding 
from keras.preprocessing import sequence 
from sklearn import metrics 
# fix random seed for reproducibility 
#np.random.seed(7) 

# ... importing data and so on ... 

# create the model 
embedding_vecor_length = 32 
neurons = 91 
epochs = 1 
model = Sequential() 
model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length)) 
model.add(LSTM(neurons)) 
model.add(Dense(1, activation='sigmoid')) 
model.compile(loss='mean_squared_logarithmic_error', optimizer='adam', metrics=['accuracy']) 
print(model.summary()) 
model.fit(X_train, y_train, epochs=epochs, batch_size=64) 
# Final evaluation of the model 
scores = model.evaluate(X_test, y_test, verbose=0) 
print("Accuracy: %.2f%%" % (scores[1]*100)) 

Python 3.6.3 | Anaconda custom（x86_64）| （默认，2017年10月6日，12:04:38） [GCC 4.2.1 Compatible Clang 4.0.1（tags/RELEASE_401/final）]

解决方法已包含在代码中（无效）。

随着每次我做培训部分我得到不同的结果。

当复位Jupyter笔记本电脑的内核，第1次与相对应的第一次和第二次与第2次。

所以复位我会永远在第一次运行时获得，例如0.7782，0.7732在第二次运行等

但没有经过内核复位结果是我每次运行它总是不同的。

我会有所帮助的任何建议！

Answer 1

我有完全相同的问题，并设法通过关闭和我每次运行模型时重新启动tensorflow会议来解决它。在你的情况应该是这样的：

#START A NEW TF SESSION 
np.random.seed(0) 
tf.set_random_seed(0) 
sess = tf.Session(graph=tf.get_default_graph()) 
K.set_session(sess) 

embedding_vecor_length = 32 
neurons = 91 
epochs = 1 
model = Sequential() 
model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length)) 
model.add(LSTM(neurons)) 
model.add(Dense(1, activation='sigmoid')) 
model.compile(loss='mean_squared_logarithmic_error', optimizer='adam', metrics=['accuracy']) 
print(model.summary()) 
model.fit(X_train, y_train, epochs=epochs, batch_size=64) 
# Final evaluation of the model 
scores = model.evaluate(X_test, y_test, verbose=0) 
print("Accuracy: %.2f%%" % (scores[1]*100)) 

#CLOSE TF SESSION 
K.clear_session() 

我跑了下面的代码，不得不使用GPU和tensorflow后端可重复的结果：

print datetime.now() 
for i in range(10): 
    np.random.seed(0) 
    tf.set_random_seed(0) 
    sess = tf.Session(graph=tf.get_default_graph()) 
    K.set_session(sess) 

    n_classes = 3 
    n_epochs = 20 
    batch_size = 128 

    task = Input(shape = x.shape[1:]) 
    h = Dense(100, activation='relu', name='shared')(task) 
    h1= Dense(100, activation='relu', name='single1')(h) 
    output1 = Dense(n_classes, activation='softmax')(h1) 

    model = Model(task, output1) 
    model.compile(loss='categorical_crossentropy', optimizer='Adam') 
    model.fit(x_train, y_train_onehot, batch_size = batch_size, epochs=n_epochs, verbose=0) 
print(model.evaluate(x=x_test, y=y_test_onehot, batch_size=batch_size, verbose=0)) 
K.clear_session() 

并获得了这样的输出：

2017-10-23 11:27:14.494482 
0.489712882132 
0.489712893813 
0.489712892765 
0.489712854426 
0.489712882132 
0.489712864011 
0.486303713004 
0.489712903398 
0.489712892765 
0.489712903398 

我的理解是，如果你不关闭你的tf会话（你是通过运行一个新的内核来完成的），你可以继续采样同样的“种子”发布。