Tensorflow 优化学习
# coding: utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
print("hello")
#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])
keep_prob = tf.placeholder(tf.float32)
lr = tf.Variable(0.001,dtype=tf.float32)
#创建一个神经网络
# W = tf.Variable(tf.zeros([784,10]))
# b = tf.Variable(tf.zeros([10]))
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)
#隐藏层1
W2 = tf.Variable(tf.truncated_normal([500,300],stddev=0.1))
b2 = tf.Variable(tf.zeros([300])+0.1)
L2 = tf.nn.tanh(tf.matmul(L1_drop,W2)+b2)
L2_drop = tf.nn.dropout(L2,keep_prob)
#隐藏层2
W3 = tf.Variable(tf.truncated_normal([300,10],stddev=0.1))
b3 = tf.Variable(tf.zeros([10])+0.1)
#L3 = tf.nn.tanh(tf.matmul(L2_drop,W3)+b3)
#L3_drop = tf.nn.dropout(L3,keep_prob)
prediction = tf.nn.softmax(tf.matmul(L2_drop,W3)+b3)
#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))
#交叉熵
#loss值最小的时候准确率最高
#loss = tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=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)
#训练
train_step = tf.train.AdamOptimizer(lr).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))
#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(30):
sess.run(tf.assign(lr,0.001*(0.95 ** epoch)))
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})
#测试准确率
#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})
learning_rate = sess.run(lr)
test_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})
print("Iter: "+str(epoch)+" ,Testing Accuracy "+str(test_acc)+" Train : "+str(learning_rate))
####运行效果
