keras 构建模型很简单,上手很方便,同时又是 tensorflow 的高级 API,所以学学也挺好。
模型复现在我们的实验中也挺重要的,跑出了一个模型,虽然我们可以将模型的 checkpoint 保存,但再跑一遍,怎么都得不到相同的结果,对我而言这是不能接受的。
用 keras 实现模型,想要能够复现,需要将设置各个可能的随机过程的 seed;而且,代码不要在 GPU 上跑,而是在 CPU 上跑。(也就是说,GPU 上得到的 keras 模型没办法再复现。)
我的 tensorflow+keras 版本:
print(tf.VERSION) # '1.10.0'
print(tf.keras.__version__) # '2.1.6-tf'
keras 模型可复现的配置:
import numpy as np
import tensorflow as tf
import random as rn
import os
# run on CPU only
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
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/
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)
# Rest of code follows ...
References
How can I obtain reproducible results using Keras during development? -- Keras Documentation
具有Tensorflow后端的Keras可以随意使用CPU或GPU吗?
原文地址:https://www.cnblogs.com/wuliytTaotao/p/10883749.html
时间: 2024-10-07 14:25:15