不加Dropout，训练数据的准确率高，基本上可以接近100%，但是，对于测试集来说，效果并不好；
加上Dropout，训练数据的准确率可能变低，但是，对于测试集来说，效果更好了，所以说Dropout可以防止过拟合。
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data # 载入数据集 mnist = input_data.read_data_sets("MNIST_data", one_hot=True) # 每个批次的大小 batch_size = 100 # 计算一共有多少个批次 n_batch = mnist.train.num_examples // batch_size # 定义两个placeholder x = tf.placeholder(tf.float32, [None, 784]) y = tf.placeholder(tf.float32, [None, 10]) keep_prob = tf.placeholder(tf.float32) # 创建一个简单的神经网络 W1 = tf.Variable(tf.truncated_normal([784, 2000], stddev=0.1)) b1 = tf.Variable(tf.zeros([2000]) + 0.1) L1 = tf.nn.tanh(tf.matmul(x, W1) + b1) L1_drop = tf.nn.dropout(L1, keep_prob) W2 = tf.Variable(tf.truncated_normal([2000, 2000], stddev=0.1)) b2 = tf.Variable(tf.zeros([2000]) + 0.1) L2 = tf.nn.tanh(tf.matmul(L1_drop, W2) + b2) L2_drop = tf.nn.dropout(L2, keep_prob) W3 = tf.Variable(tf.truncated_normal([2000, 1000], stddev=0.1)) b3 = tf.Variable(tf.zeros([1000]) + 0.1) L3 = tf.nn.tanh(tf.matmul(L2_drop, W3) + b3) L3_drop = tf.nn.dropout(L3, keep_prob) W4 = tf.Variable(tf.truncated_normal([1000, 10], stddev=0.1)) b4 = tf.Variable(tf.zeros([10]) + 0.1) prediction = tf.nn.softmax(tf.matmul(L3_drop, W4) + b4) # 二次代价函数 # loss = tf.reduce_mean(tf.square(y-prediction)) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=prediction)) # 使用梯度下降法 train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss) # 初始化变量 init = tf.global_variables_initializer() # 结果存放在一个布尔型列表中 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) #argmax返回一维张量中最大的值所在的位置 # 求准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) with tf.Session() as sess: sess.run(init) for epoch in range(31): for batch in range(n_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 0.7}) test_acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0}) train_acc = sess.run(accuracy, feed_dict={x: mnist.train.images, y: mnist.train.labels, keep_prob: 1.0}) print("Iter " + str(epoch) + ",Testing Accuracy " + str(test_acc) + ",Training Accuracy " + str(train_acc))
