tensorflow实现逻辑回归模型

逻辑回归模型

逻辑回归是应用非常广泛的一个分类机器学习算法,它将数据拟合到一个logit函数(或者叫做logistic函数)中,从而能够完成对事件发生的概率进行预测。

import numpy as np

import tensorflow as tf

import matplotlib.pyplot as plt

from tensorflow.examples.tutorials.mnist import input_data

#下载好的mnist数据集存在F:/mnist/data/中

mnist = input_data.read_data_sets('F:/mnist/data/',one_hot = True)

print(mnist.train.num_examples)

print(mnist.test.num_examples)

trainimg = mnist.train.images

trainlabel = mnist.train.labels

testimg = mnist.test.images

testlabel = mnist.test.labels

print(type(trainimg))

print(trainimg.shape,)

print(trainlabel.shape,)

print(testimg.shape,)

print(testlabel.shape,)

nsample = 5

randidx = np.random.randint(trainimg.shape[0],size = nsample)

for i in randidx:

curr_img = np.reshape(trainimg[i,:],(28,28))

curr_label = np.argmax(trainlabel[i,:])

plt.matshow(curr_img,cmap=plt.get_cmap('gray'))

plt.title(""+str(i)+"th Training Data"+"label is"+str(curr_label))

print(""+str(i)+"th Training Data"+"label is"+str(curr_label))

plt.show()

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

y = tf.placeholder("float",[None,10])

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

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

#

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))

#学习率

learning_rate = 0.01

#随机梯度下降

optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

#求1位置索引值 对比预测值索引与label索引是否一样,一样返回True

pred = tf.equal(tf.argmax(actv,1),tf.argmax(y,1))

#tf.cast把True和false转换为float类型 0,1

#把所有预测结果加在一起求精度

accr = tf.reduce_mean(tf.cast(pred,"float"))

init = tf.global_variables_initializer()

"""

#测试代码

sess = tf.InteractiveSession()

arr = np.array([[31,23,4,24,27,34],[18,3,25,4,5,6],[4,3,2,1,5,67]])

#返回数组的维数 2

print(tf.rank(arr).eval())

#返回数组的行列数 [3 6]

print(tf.shape(arr).eval())

#返回数组中每一列中最大元素的索引[0 0 1 0 0 2]

print(tf.argmax(arr,0).eval())

#返回数组中每一行中最大元素的索引[5 2 5]

print(tf.argmax(arr,1).eval())

J"""

#把所有样本迭代50次

training_epochs = 50

#每次迭代选择多少样本

batch_size = 100

display_step = 5

sess = tf.Session()

sess.run(init)

#循环迭代

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(optm,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

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)

#每五个epoch打印一次信息

print("Epoch:%03d/%03d cost:%.9f train_acc:%.3f test_acc: %.3f" %(epoch,training_epochs,avg_cost,train_acc,test_acc))

print("Done")

程序训练结果如下:

Epoch:000/050 cost:1.177228655 train_acc:0.800 test_acc: 0.855

Epoch:005/050 cost:0.440933891 train_acc:0.890 test_acc: 0.894

Epoch:010/050 cost:0.383387268 train_acc:0.930 test_acc: 0.905

Epoch:015/050 cost:0.357281335 train_acc:0.930 test_acc: 0.909

Epoch:020/050 cost:0.341473956 train_acc:0.890 test_acc: 0.913

Epoch:025/050 cost:0.330586549 train_acc:0.920 test_acc: 0.915

Epoch:030/050 cost:0.322370980 train_acc:0.870 test_acc: 0.916

Epoch:035/050 cost:0.315942993 train_acc:0.940 test_acc: 0.916

Epoch:040/050 cost:0.310728854 train_acc:0.890 test_acc: 0.917

Epoch:045/050 cost:0.306357428 train_acc:0.870 test_acc: 0.918

Done

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