tensorflow_api_4:tf.equal( )

本文介绍了TensorFlow中的tf.equal()函数,该函数用于逐元素比较两个tensor,并返回布尔值组成的tensor。文章详细解释了函数的参数及返回值,并通过一个具体的Python示例展示了如何使用此函数。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

tf.equal( )

  • 函数作用:
    逐元素比较两个tensor x, y,返回x == y的真值

  • 参数:
    tf.equal(x, y, name=None)
    x: 一个tensor. 必须是以下类型:half, float32, float64, uint8, int8, int16, int32, int64, complex64, quint8, qint8, qint32, string, bool, complex128
    y: 一个tensor. 必须与x 的数据类型一致。
    name: 自定义操作的名称 (可选)。

  • 返回:
    一个bool类型的tensor

  • 例子:

import tensorflow as tf  

A = tf.constant([1, 3, 4, 5, 6]) 
B = tf.constant([1, 3, 4, 3, 2]) 

with tf.Session() as sess:  
    print(sess.run(tf.equal(A, B)))  

# 输出[ True  True  True False False]
对下面代码进行改错 import tensorflow.compat.v1 as tf tf.compat.v1.disable_eager_execution() from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('MNIST_data', one_hot=True) num_classes = 10 input_size = 784 hidden_units_size = 30 batch_size = 100 training_iterations = 10000 X = tf.placeholder(tf.float32, [None, input_size]) Y = tf.placeholder(tf.float32, [None, num_classes]) W1 = tf.Variable(tf.random_normal([input_size, hidden_units_size],stddev = 0.1)) B1 = tf.Variable(tf.constant([hidden_units_size])) W2 = tf.Variable(tf.random_normal ([hidden_units_size,num_classes],stddev = 0.1)) B2 = tf.Variable(tf.constant(0.1), [num_classes]) hidden_opt = tf.matmul(X, W1) + B1 hidden_opt = tf.nn.relu(hidden_opt) final_opt = tf.matmul(hidden_opt, W2) + B2 final_opt = tf.nn.relu(final_opt) loss1 = tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=final_opt) loss = tf.reduce_mean(loss1) opt = tf.train.GradientDescentOptimizer(0.05).minimize(loss) init = tf.global_variables_initializer() correct_prediction = tf.equal(tf.argmax(Y,1), tf.argmax(final_opt,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float')) sess = tf.Session() sess.run(init) for i in range(training_iterations): batch = mnist.train.next_batch(batch_size) batch_input = batch[0] batch_labels = batch[1] train_loss = sess.run([opt, loss], feed_dict={X: batch_input, Y: batch_labels}) if i % 100 == 0: train_accuracy = accuracy.eval (session = sess, feed_dict={X: batch_input, Y: batch_labels}) print("step %d, training accuracy %g" % (i, train_accuracy))
04-01
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data/", one_hot=True)def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial)def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial)def conv2d(x, W): return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')def max_pool_2x2(x): return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')W_conv1 = weight_variable([5, 5, 1, 32])b_conv1 = bias_variable([32])x_image = tf.reshape(x, [-1,28,28,1])h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)h_pool1 = max_pool_2x2(h_conv1)W_conv2 = weight_variable([5, 5, 32, 64])b_conv2 = bias_variable([64])h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)h_pool2 = max_pool_2x2(h_conv2)W_fc1 = weight_variable([7 * 7 * 64, 1024])b_fc1 = bias_variable([1024])h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)keep_prob = tf.placeholder("float")h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)W_fc2 = weight_variable([1024, 10])b_fc2 = bias_variable([10])y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv)) #交叉熵损失#定义优化策略:采用Adam优化算法,以0.0001的学习率进行最小化损失操作train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #判断预测值与标签是否相等,返回一个布尔值correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) #将布尔值转化为数值,计算平均准确率sess.run(tf.initialize_all_variables()) #变量初始化for i in range(20000): #在循环中实现参数更新 batch = mnist.train.next_batch(50) #batch size为50,每次获取50张数据 if i%100 == 0: #每100次在屏幕打印一次信息 train_accuracy = accuracy.eval(feed_dict={ x:batch[0], y_: batch[1], keep_prob: 1.0}) #将数据填入占位符,喂给网络 print "step %d, training accuracy %g"%(i, train_accuracy) #输出迭代次数和测试训练的准确率 train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})print "test accuracy %g"%accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}) 改进他,让他能运行
最新发布
05-14
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值