TensorFlow教程之深入MNIST测试

测试代码 mnist_test_deep.py

# -*- coding: utf-8 -*-import tensorflow as tffrom mnist_ import read_data_sets#加载MNIST数据input_data = read_data_sets('MNIST_data', one_hot=True)#运行TensorFlow的InteractiveSessionsess = tf.InteractiveSession()#构建Softmax 回归模型#占位符x = tf.placeholder("float", shape=[None, 784])y_ = tf.placeholder("float", shape=[None, 10])#变量W = tf.Variable(tf.zeros([784,10]))b = tf.Variable(tf.zeros([10]))'''#初始化变量sess.run(tf.initialize_all_variables())#类别预测与损失函数y = tf.nn.softmax(tf.matmul(x,W) + b)#损失函数是目标类别和预测类别之间的交叉熵cross_entropy = -tf.reduce_sum(y_*tf.log(y))#用梯度下降算法以0.01的学习速率最小化交叉熵train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)#开始训练模型，这里我们让模型循环训练1000次for i in range(1000):  batch = input_data.train.next_batch(50)  train_step.run(feed_dict={x: batch[0], y_: batch[1]})#评估模型#检测我们的预测是否真实标签匹配(索引位置一样表示匹配)，输出为一组布尔值correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))#为了确定正确预测项的比例，把布尔值转换成浮点数，然后取平均值accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))#计算所学习到的模型在测试数据集上面的正确率print accuracy.eval(feed_dict={x: input_data.test.images, y_: input_data.test.labels})'''#构建一个多层卷积网络#权重初始化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)#卷积和池化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')#第一层卷积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)#第二层卷积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)#密集连接层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)#Dropoutkeep_prob = tf.placeholder("float")h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)#输出层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)#训练和评估模型cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))sess.run(tf.initialize_all_variables())for i in range(20000):  batch = input_data.train.next_batch(50)  if i%100 == 0:    train_accuracy = accuracy.eval(feed_dict={        x:batch[0], y_: batch[1], keep_prob: 1.0})    print "step %d, training accuracy %g"%(i, train_accuracy)  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})print "test accuracy %g"%accuracy.eval(feed_dict={    x: input_data.test.images, y_: input_data.test.labels, keep_prob: 1.0})

测试结果为 0.993