TensorFlow——優化器選擇

應用實例：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

#載入數據集
mnist = input_data.read_data_sets("E:/學習/目標檢測/TensorFlow學習/MNIST_DATA",one_hot=True)

#每個批次的大小
batch_size = 100
#計算一共有多少個批次
n_batch = mnist.train.num_examples // batch_size

#定義兩個placeholder
x = tf.placeholder(tf.float32,[None,784])
y = tf.placeholder(tf.float32,[None,10])

#創建一個簡單的神經網絡
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
prediction = tf.nn.softmax(tf.matmul(x,W)+b)

#二次代價函數
# loss = tf.reduce_mean(tf.square(y-prediction))
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))#使用交叉熵來定義代價函數
#使用梯度下降法
# train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
# train_step = tf.train.AdadeltaOptimizer().minimize(loss)
train_step = tf.train.AdagradOptimizer(0.01).minimize(loss)

#初始化變量
init = tf.global_variables_initializer()

#結果存放在一個布爾型列表中
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一維張量中最大的值所在的位置
#求准確率
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})
        
        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))

 

 

 

 

TensorFlow中有多個優化器可以選擇，最簡單的就是SGD（隨機梯度下降法）