代码:
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data #载入数据集 #当前路径 mnist = input_data.read_data_sets("MNISt_data", one_hot=True)
运行结果:
Extracting MNISt_data/train-images-idx3-ubyte.gz Extracting MNISt_data/train-labels-idx1-ubyte.gz Extracting MNISt_data/t10k-images-idx3-ubyte.gz Extracting MNISt_data/t10k-labels-idx1-ubyte.gz
代码:
#每个批次的大小 #以矩阵的形式放进去 batch_size = 100 #计算一共有多少个批次 n_batch = mnist.train.num_examples // batch_size #命名空间 with tf.name_scope('input'): #定义两个placeholder #28 x 28 = 784 x = tf.placeholder(tf.float32, [None, 784], name='x_input') y = tf.placeholder(tf.float32, [None, 10], name='y_input') with tf.name_scope('layer'): #创建一个简单的神经网络 #输入层784,没有隐藏层,输出层10个神经元 with tf.name_scope('weights'): W = tf.Variable(tf.zeros([784, 10]), name='W') with tf.name_scope('biases'): b = tf.Variable(tf.zeros([1, 10]), name='b') with tf.name_scope('wx_plus_b'): wx_plus_b = tf.matmul(x, W) + b with tf.name_scope('softmax'): prediction = tf.nn.softmax(wx_plus_b) with tf.name_scope('loss'): #二次代价函数 loss = tf.reduce_mean(tf.square(y - prediction)) #交叉熵 #loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction)) with tf.name_scope('train'): #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() with tf.name_scope('accuracy'): with tf.name_scope('correct_prediction'): #结果存放在一个布尔型列表中 #tf.argmax(y, 1)与tf.argmax(prediction, 1)相同返回True,不同则返回False #argmax返回一维张量中最大的值所在的位置 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) with tf.name_scope('accuracy'): #求准确率 #tf.cast(correct_prediction, tf.float32) 将布尔型转换为浮点型 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) with tf.Session() as sess: sess.run(init) #当前路径logs文件夹 writer = tf.summary.FileWriter('logs/', sess.graph) #总共1个周期 for epoch in range(1): #总共n_batch个批次 for batch in range(n_batch): #获得一个批次 batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step, feed_dict={x:batch_xs, y:batch_ys}) #训练完一个周期后准确率 acc = sess.run(accuracy, feed_dict={x:mnist.test.images, y:mnist.test.labels}) print("Iter" + str(epoch) + ", Testing Accuracy" + str(acc))
在命令行:(注意切换到当前路径下)
tensorboard --logdir=logs
效果展示: