invalid syntax
jupyter notebook写的
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)
#设置每个批次的大小
batch_size=100
#计算一共有多少批次
n_batch = mnist.train.num_examples//batch_size
#定义3个placeholder
x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])
keep_prob=tf.placeholder(tf.float32) #存放百分率
#创建一个多层神经网络模型
#第一个隐藏层
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) #keep_prob设置工作状态神经元的百分率
#第二个隐藏层
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_drop)
#第三个隐藏层
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_drop)
#输出层
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)
#定义交叉熵代价函数
loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
#定义反向传播算法,使用梯度下降算法
train_step=tf.train.GradientDescentOptimizer(0.2).minimize(loss)
#结果存放在一个布尔型列表中(argmax函数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))
#求准确率(tf.cast将布尔值转化为float型)
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
#创建会话
with tf.Session() as sess:
sess.run(tf.global_variable_initializer()) #初始化变量
#训练次数
for i 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:1.0})
#测试数据计算出的准确率
test_acc=sess.run(accuracy,feed_dict+{x:mnist.test.images, y:mnist.test.labels, keep_prob:1.0})
print("Iter"+str(i)+",Testing Accuracy"+str(test_acc))