tensorflow学习笔记1-—mnist全连接模型

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot = True)
//读入数据
import tensorflow as tf

sess = tf.InteractiveSession()

x = tf.placeholder(tf.float32, [None, 784])

w = tf.Variable(tf.zeros([784, 10]))

b = tf.Variable(tf.zeros([10]))

y = tf.nn.softmax(tf.matmul(x,w)+b)

y_=tf.placeholder(tf.float32,[None,10])

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

tf.global_variables_initializer().run()

for i in range(1000):
    
    batch_xs,batch_ys = mnist.train.next_batch(100)
    
    train_step.run({x:batch_xs, y_: batch_ys})
    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
    
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    
    print(accuracy.eval({x:mnist.test.images, y_:mnist.test.labels}))

模型建立步骤：

1 载入数据

2 创建权重  偏差变量

3 实现softmax算法 y = tf.nn.softmax(tf.matmul(x,w)+b)

4 为训练模型，定义loss function cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
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5 选择优化算法 对loss function 最小化 train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)