TensorFlow学习笔记（一）--Softmax Regression实现识别手写数字

#Softmax Regression识别手写数字
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
print(mnist.train.images.shape,mnist.train.labels.shape)
print(mnist.test.images.shape,mnist.test.labels.shape)
print(mnist.validation.images.shape,mnist.validation.labels.shape)

sess = tf.InteractiveSession()
#第一步，定义算法公式
x = tf.placeholder(tf.float32,[None,784]) #None表示任意维数
W = tf.Variable(tf.zeros([784,10])) #初始权重
b = tf.Variable(tf.zeros([10]))#初始偏置
y = tf.nn.softmax(tf.matmul(x,W)+b)
#第二步，定义损失函数
y_ = tf.placeholder(tf.float32,[None,10])
cross_entropy = -1*tf.reduce_sum(y_*tf.log(y))
#梯度下降最小化交叉熵
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
#第三步，迭代地对数据进行训练
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})
#第四步，在测试集或验证集上对准确率进行评判
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}))
print(accuracy.eval({x:mnist.validation.images,y_:mnist.validation.labels}))

运行结果：