python – 为什么即使我设置了随机种子,我也无法在Keras中获得可

我正在Mac OSX上使用Keras在虚拟数据上培训MobileNet架构.我设置了nump.random和tensorflow.set_random_seed,但由于某些原因我无法获得可重现的结果：每次重新运行代码时,我都会得到不同的结果.为什么？这不是因为GPU,因为我在拥有Radeon显卡的MacBook Pro 2017上运行,因此Tensorflow没有利用它.代码运行

python Keras_test.py

所以这不是状态问题(我没有使用Jupyter或IPython：每次运行代码时都应该重置环境).

编辑：我在导入Keras之前通过移动所有种子的设置来更改我的代码.结果仍然不确定,但结果的方差比以前小得多.这非常离奇.

目前的模型非常小(就深度神经网络而言)并不是微不足道的,它不需要GPU运行,它可以在几分钟内在现代笔记本电脑上进行训练,因此重复我的实验是在任何人的能力范围内.我邀请你这样做：我对了解从系统到另一个系统的变化程度非常感兴趣.

import numpy as np
# random seeds must be set before importing keras & tensorflow
my_seed = 512
np.random.seed(my_seed)
import random 
random.seed(my_seed)
import tensorflow as tf
tf.set_random_seed(my_seed)

# now we can import keras
import keras.utils
from keras.applications import MobileNet
from keras.callbacks import ModelCheckpoint
from keras.optimizers import Adam
import os

height = 224
width = 224
channels = 3
epochs = 10
num_classes = 10



# Generate dummy data
batch_size = 32  
n_train = 256
n_test = 64
x_train = np.random.random((n_train,height,width,channels))
y_train = keras.utils.to_categorical(np.random.randint(num_classes,size=(n_train,1)),num_classes=num_classes)
x_test = np.random.random((n_test,channels))
y_test = keras.utils.to_categorical(np.random.randint(num_classes,size=(n_test,num_classes=num_classes)
# Get input shape
input_shape = x_train.shape[1:]

# Instantiate model 
model = MobileNet(weights=None,input_shape=input_shape,classes=num_classes)

model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
# Viewing Model Configuration
model.summary()

# Model file name
filepath = 'model_epoch_{epoch:02d}_loss_{loss:0.2f}_val_{val_loss:.2f}.hdf5'

# Define save_best_only checkpointer
checkpointer = ModelCheckpoint(filepath=filepath,monitor='val_acc',verbose=1,save_best_only=True)

# Let's fit!
model.fit(x_train,y_train,batch_size=batch_size,epochs=epochs,validation_data=(x_test,y_test),callbacks=[checkpointer])

和往常一样,这是我的Python,Keras& Tensorflow版本：

python -c 'import keras; import tensorflow; import sys; print(sys.version,'keras.__version__','tensorflow.__version__')'
/anaconda2/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future,it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
('2.7.15 |Anaconda,Inc.| (default,May  1 2018,18:37:05) n[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]','2.1.6','1.8.0')

以下是多次运行此代码所获得的一些结果：如您所见,代码使用描述性文件名保存10个时期中的最佳模型(最佳验证准确性),因此比较不同运行中的文件名可以判断结果的可变性.

model_epoch_01_loss_2.39_val_3.28.hdf5
model_epoch_01_loss_2.39_val_3.54.hdf5
model_epoch_01_loss_2.40_val_3.47.hdf5
model_epoch_01_loss_2.41_val_3.08.hdf5

# Seed value
# Apparently you may use different seed values at each stage
seed_value= 0

# 1. Set `PYTHONHASHSEED` environment variable at a fixed value
import os
os.environ['PYTHONHASHSEED']=str(seed_value)

# 2. Set `python` built-in pseudo-random generator at a fixed value
import random
random.seed(seed_value)

# 3. Set `numpy` pseudo-random generator at a fixed value
import numpy as np
np.random.seed(seed_value)

# 4. Set `tensorflow` pseudo-random generator at a fixed value
import tensorflow as tf
tf.set_random_seed(seed_value)

# 5. Configure a new global `tensorflow` session
from keras import backend as K
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1,inter_op_parallelism_threads=1)
sess = tf.Session(graph=tf.get_default_graph(),config=session_conf)
K.set_session(sess)