結果不能用Keras和TensorFlow在Python中重現

Question

我遇到問題，我無法重現Keras和ThensorFlow的結果。

好像最近也一直在Keras documentation site發佈針對此問題的解決方法，但不知何故，沒有爲我工作。

我做錯了什麼？

我使用一個MBP視網膜Jupyter筆記本（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）| （默認，2017年10月6日，12:04:38） [GCC 4.2.1 Compatible Clang 4.0.1（tags/RELEASE_401/final）]

解決方法已包含在代碼中（無效）。

隨着每次我做培訓部分我得到不同的結果。

當復位Jupyter筆記本電腦的內核，第1次與相對應的第一次和第二次與第2次。

所以復位我會永遠在第一次運行時獲得，例如0.7782，0.7732在第二次運行等

但沒有經過內核復位結果是我每次運行它總是不同的。

我會有所幫助的任何建議！

Answer 1

我有完全相同的問題，並設法通過關閉和我每次運行模型時重新啓動tensorflow會議來解決它。在你的情況應該是這樣的：

#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() 

我跑了下面的代碼，不得不使用GPU和tensorflow後端可重複的結果：

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() 

並獲得了這樣的輸出：

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，如果你不關閉你的tf會話（你是通過運行一個新的內核來完成的），你可以繼續採樣同樣的「種子」發佈。