今天通過觀看老師分享的TensorFlow教學視訊，完成了邏輯迴歸模型的建立，內容比較複雜程式碼也比較陌生，理解起來非常費力。

具體程式碼如下：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('data/',one_hot=True)
#print
trainimg = mnist.train.images
trainlabel = mnist.train.labels
testimg = mnist.test.images
testlabel = mnist.test.labels
print("trainlabel:",type(trainlabel),"shape:",trainlabel.shape)
print("trainimg:",type(trainimg),"shape:",trainimg.shape)
print("testlabel:",type(testlabel),"shape:",testlabel.shape)
print("testimg:",type(testimg),"shape:",testimg.shape)
print(trainlabel[0])

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]))
#計算模型
actv = tf.nn.softmax(tf.matmul(x,W) + b)
#損失
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv),reduction_indices=1))
#學習引數
learning_rate = 0.01
#梯度下降優化器
optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
#對比預測值的索引和真實值的索引
pred = tf.equal(tf.argmax(actv,1),tf.argmax(y,1))
#把true或false轉換成1或0求均值作為精度
accr = tf.reduce_mean(tf.cast(pred,"float"))

init = tf.global_variables_initializer()
training_epochs = 50
batch_size = 100
display_step = 5
sess = tf.Session()
sess.run(init)
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:
        feed_train = {x:batch_xs,y:batch_ys}
        feed_test = {x:mnist.test.images,y:mnist.test.labels}
        train_acc = sess.run(accr,feed_dict=feed_train)
        test_acc = sess.run(accr,feed_dict=feed_test)
        print("Epoch: %03d/%03d cost: %.9f train_acc: %.3f test_acc: %.3f"
               % (epoch,training_epochs,avg_cost,train_acc,test_acc))
print("Done")
執行截圖：