如何打印十位数的特定索引的值

Question

这个tensorflow代码来自这个tutorial。我想知道是否有一种方法可以打印张量的特定索引处的值？例如，在下面的会话中，我是否可以打印张量y_的第1行第1列的值，它应该类似于[0,0,0,1,0,0,0,0,0]？你知道吗

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}))

Answer 1

在Session中运行placeholder时，必须使用方法sess.run()的feed_dict属性将数据传入占位符。你知道吗

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})。下面的整个代码块将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()

输出：

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.]