import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data #载入数据集 mnist = input_data.read_data_sets("F:\\TensorflowProject\\MNIST_data",one_hot=True) #每个批次的大小，训练时一次100张放入神经网络中训练 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]) #0-9十个数字 y = tf.placeholder(tf.float32,[None,10]) #创建一个神经网络 W = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10])) prediction = tf.nn.softmax(tf.matmul(x,W)+b) #二次代价函数 #loss = tf.reduce_mean(tf.square(y-prediction)) #交叉熵代价函数 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) #1e-2 #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)) #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(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}) #测试准确率 acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}) print("Iter: "+str(epoch)+" ,Testing Accuracy "+str(acc))
###########运行结果
Extracting F:\TensorflowProject\MNIST_data\train-images-idx3-ubyte.gz Extracting F:\TensorflowProject\MNIST_data\train-labels-idx1-ubyte.gz Extracting F:\TensorflowProject\MNIST_data\t10k-images-idx3-ubyte.gz Extracting F:\TensorflowProject\MNIST_data\t10k-labels-idx1-ubyte.gz Iter: 0 ,Testing Accuracy 0.9221 Iter: 1 ,Testing Accuracy 0.9133 Iter: 2 ,Testing Accuracy 0.9271 Iter: 3 ,Testing Accuracy 0.9262 Iter: 4 ,Testing Accuracy 0.9299 Iter: 5 ,Testing Accuracy 0.9293 Iter: 6 ,Testing Accuracy 0.9301 Iter: 7 ,Testing Accuracy 0.9299 Iter: 8 ,Testing Accuracy 0.9287 Iter: 9 ,Testing Accuracy 0.9319 Iter: 10 ,Testing Accuracy 0.9317 Iter: 11 ,Testing Accuracy 0.9315 Iter: 12 ,Testing Accuracy 0.9307 Iter: 13 ,Testing Accuracy 0.932 Iter: 14 ,Testing Accuracy 0.9314 Iter: 15 ,Testing Accuracy 0.9316 Iter: 16 ,Testing Accuracy 0.9311 Iter: 17 ,Testing Accuracy 0.9333 Iter: 18 ,Testing Accuracy 0.9318 Iter: 19 ,Testing Accuracy 0.9318 Iter: 20 ,Testing Accuracy 0.9289