Tensorflow深度学习(二)

Mnist数据集下载

import input_data
#下载数据集
mnist=input_data.read_data_sets('data/',one_hot=True)
trainimg=mnist.train.images
trainlabel=mnist.train.labels
testimg=mnist.test.images
testlabel=mnist.test.labels
print("MNIST loaded")
print(trainimg.shape)
print(trainlabel.shape)
print(testimg.shape)
print(testlabel.shape)

 逻辑回归模型(完成)

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import input_data
#下载数据集
mnist=input_data.read_data_sets('data/',one_hot=True)
trainimg=mnist.train.images
trainlabel=mnist.train.labels
testimg=mnist.test.images
testlabel=mnist.test.labels
print("MNIST loaded")
print(trainimg.shape)
print(trainlabel.shape)
print(testimg.shape)
print(testlabel.shape)
x=tf.placeholder('float',[None,784])
y=tf.placeholder('float',[None,10])
#0值初始化
W=tf.Variable(tf.zeros([784,10]))
b=tf.Variable(tf.zeros([10]))
#多分类任务
actv=tf.nn.softmax(tf.matmul(x,W)+b)
#计算损失
cost=tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv),reduction_indices=1))
#优化
learning_rate=0.01
optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
#预测Prediction
pred=tf.equal(tf.argmax(actv,1),tf.argmax(y,1))
#计算精度Accuracy
accr=tf.reduce_mean(tf.cast(pred,'float'))#0/1
#初始化
init=tf.global_variables_initializer()
sess=tf.Session()
sess.run(init)
#设置变量
#设置epoch
training_epochs=50
#设置batchsize
batch_size=100
#设置显示
display_step=5
#开始测试-MINI_batch learning
for epoch in range(training_epochs):
    avg_cost=0.
    num_batch=int(mnist.train.num_examples/batch_size)
    for i in range(num_batch):
        batch_xs,batch_ys=mnist.train.next_batch(batch_size)
        sess.run(optimizer,feed_dict={x:batch_xs,y:batch_ys})
        feeds={x:batch_xs,y:batch_ys}
        avg_cost +=sess.run(cost,feed_dict=feeds)/num_batch
    #展示
    if epoch % display_step==0:
        feeds_train={x:batch_xs,y:batch_ys}
        feeds_test={x:mnist.test.images,y:mnist.test.labels}
        train_acc=sess.run(accr,feed_dict=feeds_train)
        test_acc=sess.run(accr,feed_dict=feeds_test)
        print(("Epoch: %03d/%03d cost: %.9f train_acc: %.3f test_acc: %.3f")%(epoch,training_epochs,avg_cost,train_acc,test_acc))
print("Done")