[英]How to print the value at a particular index of a tensor
This tensorflow code is from this tutorial . 此tensorflow代码来自本教程 。 I am wondering if there is a way to print the values at particular indexes of a tensor? 我想知道是否有一种方法可以在张量的特定索引处打印值? For example in the session below can I print the value of row 1 column 1 of a the tensor y_
which should look something like [0,0,0,1,0,0,0,0,0]? 例如,在下面的会话中,我可以打印张量y_
的第1行第1列的值吗?它应该看起来像[0,0,0,1,0,0,0,0,0,0]?
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
import tensorflow as tf
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
for _ in range(10):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(y_))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
When running a placeholder
within a Session
, you must pass in the data to the placeholder by using the feed_dict
attribute of the method sess.run()
. 在Session
运行placeholder
,必须使用sess.run()
方法的feed_dict
属性将数据传递到占位符。
So by your question, to view the first row and column of the tensor y_
, adjust your code to: sess.run(y_[0:][0], feed_dict = {y_: batch_ys})
. 因此,根据您的问题,要查看张量y_
的第一行和第一列,请将代码调整为: sess.run(y_[0:][0], feed_dict = {y_: batch_ys})
。 The entire code block below should give you the results you expect: 下面的整个代码块应为您提供预期的结果:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
import tensorflow as tf
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), \
reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
for _ in range(10):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
print('Values of y\n{}'.format(sess.run(y_[0:][0], \
feed_dict = {y_: batch_ys})))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, \
y_: mnist.test.labels}))
sess.close()
Values of y
[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
Values of y
[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
Values of y
[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]
Values of y
[0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
Values of y
[0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
Values of y
[0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
Values of y
[0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
Values of y
[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
Values of y
[0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
Values of y
[0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.