python编程之with tf.Session() as sess:有什么用 查看原文 错误解决:Attempting to use uninitialized value Variable 声明一个tensot变量,并用sess语句执行时(如上图所示)会报如第二张图所示错误,此时需要初始化所有变量,加入语句sess.run(tf.gloabl_variables_initializer())就可以。 运行成功结果如图: 注意:...
在tf的教程中看到有关的代码: 会话在完成后必须关闭以释放资源。你也可以使用"with"句块开始一个会话,该会 话将在"with"句块结束时自动关闭 with tf.Session() as sess: result = sess.run([product]) print(result) 概览: python中的with语句用于访问资源。它确保执行指定的__exit__(“清理”)操作,而不...
<tf.Tensor'Const:0'shape=(3,) dtype=int32> >>> a=tf.gather(a, b , axis=1) >>> a <tf.Tensor'GatherV2:0'shape=(2,3,3) dtype=int64> >>> with tf.Session() as sess: ...printsess.run(a) ... ... 2020-06-1609:54:51.947755: I tensorflow/core/platform/cpu_feature_guard....
一个小错误 ,不过以后会记住的 withtf.Sessionassess: AttributeError:exit 错误原因 在Session后加Session() with tf.Session as sess: 修改成下面就好 with tf.Session() as sess: 原文链接:https://blog.csdn.net/bc521bc/article/details/83713426
with tf.Session as sess: AttributeError: __exit__ 一个小错误 ,不过以后会记住的 withtf.Sessionassess: AttributeError:exit 错误原因 在Session后加Session() with tf.Session as sess: 修改成下面就好 with tf.Session() as sess: 原文链接:https://blog.csdn.net/bc521bc/article/details/83713426...
w = tf.constant(3) x = w + 2 y = x + 5 z = x * 3 with tf.Session() as sess: print(y.eval()) # 10 print(z.eval()) # 15 上述代码定义了一个很简单的计算图,之后开始会话并计算 y 的值:TensorFlow自动的寻找到 y 依赖 x ,而 x 又依赖于 w ,所以先计算 w 的值,然后是 x ...
10. cross_entropy = -tf.reduce_sum(y_*tf.log(y)) 11. #do cross_entropy just one step 12. cross_entropy2=tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits, y_))#dont forget tf.reduce_sum()!! 13. 14. with tf.Session() as sess: ...
import numpy as np import matplotlib.pyplot as plt import scipy.io import random import tensorflow as tf # set the folder path dir_name = '/media/chi/New Volume/Dataset/GENKI4K/Feature_Data' print '--- no sub dir' # set the
with tf.Session() as sess: print (sess.run(logits_scaled)) print (sess.run(result)) 运行结果: [[0.95033026 0.04731416 0.00235563] [0.04622407 0.11369288 0.84008306]] [3.9509459 1.6642545] 需要注意的是: (1)如果labels的每一行是one-hot表示,也就是只有一个地方为1(或者说100%),其他地方为0(或者...
with tf.Session() as sess: softmax=sess.run(y) c_e = sess.run(cross_entropy) c_e2 = sess.run(cross_entropy2) print("step1:softmax result=") print(softmax) print("step2:cross_entropy result=") print(c_e) print("Function(softmax_cross_entropy_with_logits) result=") ...