mnist_dropout.zip_DNN_MNIST_belowwtu_drop out_mnist neural netwo

import tensorflow as tf import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) batch_size=100 #计算一共有多少个批次 n_batch=mnist.train.num_examples//batch_size #定义placeholder x=tf.placeholder(tf.float32,[None,784]) y=tf.placeholder(tf.float32,[None,10])#标签 keep_prob=tf.placeholder(tf.float32) #创建一个的神经网络，2000个神经元的隐藏层，后面又是2000个神经元的隐藏层，只有是1000个神经元的隐藏层，最后是10个输出层 W1=tf.Variable(tf.truncated_normal([784,500],stddev=0.1)) b1=tf.Variable(tf.zeros([500])+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([500,100],stddev=0.1)) b2=tf.Variable(tf.zeros([100])+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) W3=tf.Variable(tf.truncated_normal([100,10],stddev=0.1)) b3=tf.Variable(tf.zeros([10])+0.1) prediction=tf.nn.softmax(tf.matmul(L2_drop,W3)+b3) #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(labels=y,logits=prediction)) #使用梯度下降法 #train_step=tf.train.GradientDescentOptimizer(0.2).minimize(loss) train_step=tf.train.AdamOptimizer(0.01).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))#比较两个参数大小，相同为true。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))#将布尔型转化为32位浮点型，再求一个平均值。true变为1.0，false变为0。 with tf.Session() as sess: sess.run(init) for epoch in range(21): 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:0.5})#keep_prob为1.0表示所有的神经元都是工作的 test_acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})#测试数据算出来的准确率 train_acc=sess.run(accuracy,feed_dict={x:mnist.train.images,y:mnist.train.labels,keep_prob:1.0})#训练集算出的准确率 print("Iter "+str(epoch)+",Testing Accuracy "+str(test_acc)+",Training Accuracy "+str(train_acc))