tensorflow更改變量的值實(shí)例
如下所示:
from __future__ import print_function,division import tensorflow as tf #create a Variable w=tf.Variable(initial_value=[[1,2],[3,4]],dtype=tf.float32) x=tf.Variable(initial_value=[[1,1],[1,1]],dtype=tf.float32,validate_shape=False) init_op=tf.global_variables_initializer() update=tf.assign(x,[[1,2],[1,2]]) with tf.Session() as session: session.run(init_op) session.run(update) x=session.run(x) print(x)
實(shí)驗(yàn)結(jié)果:
[[ 1. 2.] [ 1. 2.]]
tensorflow使用assign(variable,new_value)來更改變量的值,但是真正作用在garph中,必須要調(diào)用gpu或者cpu運(yùn)行這個(gè)更新過程。
session.run(update)
tensorflow不支持直接對(duì)變量進(jìn)行賦值更改
from __future__ import print_function,division import tensorflow as tf #create a Variable x=tf.Variable(initial_value=[[1,1],[1,1]],dtype=tf.float32,validate_shape=False) x=[[1,3],[2,4]] init_op=tf.global_variables_initializer() update=tf.assign(x,[[1,2],[1,2]]) with tf.Session() as session: session.run(init_op) session.run(update) print(session.run(x))
error:
"C:\Program Files\Anaconda3\python.exe" D:/pycharmprogram/tensorflow_learn/assign_learn/assign_learn.py Traceback (most recent call last): File "D:/pycharmprogram/tensorflow_learn/assign_learn/assign_learn.py", line 8, in <module> update=tf.assign(x,[[1,2],[1,2]]) File "C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\ops\state_ops.py", line 271, in assign if ref.dtype._is_ref_dtype: AttributeError: 'list' object has no attribute 'dtype' Process finished with exit code 1
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