softmax

#　coding=utf-8
# !/usr/bin/python


#  数据集合来自ｍｎｉｓｔ


from  tensorflow.examples.tutorials.mnist  import input_data

mnist = input_data.read_data_sets("MNIST_data",one_hot=True)

# 打印
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)



import tensorflow as tf

sess = tf.InteractiveSession()

# 定义预测函数

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)


#  定义损失函数

y_=tf.placeholder(tf.float32,[None,10])
cross_entroy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y),reduction_indices=[1]))

#  定义优化算法

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

# 全局参数的初始化

tf.global_variables_initializer().run()

# 迭代计算

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    train_step.run({x: batch_xs, y_: batch_ys})


# 验证模型的准确率
# (y,1)是求各个预测中概率最大的
# (y_1,1)是找样本中的真实类别

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}))