TensorFlow邏輯迴歸

邏輯迴歸可以看作只有一層網路的前向神經網路，並且引數連線的權重只是一個值，而非矩陣。公式為：y_predict=logistic(X*W+b)，其中X為輸入，W為輸入與隱含層之間的權重，b為隱含層神經元的偏置，而logistic為啟用函式，一般為sigmoid或者tanh，y_predict為最終預測結果。

邏輯迴歸是一種分類器模型，需要函式不斷的優化引數，這裡目標函式為y_predict與真實標籤Y之間的L2距離，使用隨機梯度下降演算法來更新權重和偏置。

原始碼：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import
 
 input_data
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"

mnist=input_data.read_data_sets("/home/yxcx/tf_data",one_hot=True)

#Parameters
learning_rate=0.01
training_epochs=25
batch_size=100
display_step=1

#tf Graph Input
x=tf.placeholder(tf.float32,[None,784])
y=tf.placeholder(tf.float32,[None,10])

#Set model weights
 

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

#Construct model
pred=tf.nn.softmax(tf.matmul(x,W)+b)

#Minimize error using cross entropy
cost=tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred),reduction_indices=1))

#Gradient Descent
optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

 
#Initialize the variables
init=tf.global_variables_initializer()

#Start training
with tf.Session() as sess:
    sess.run(init)

    #Training cycle
    for epoch in range(training_epochs):
        avg_cost=0
        total_batch=int(mnist.train.num_examples/batch_size)
        # loop over all batches
        for i in range(total_batch):
            batch_xs,batch_ys=mnist.train.next_batch(batch_size)
            #Fit training using batch data
            _,c=sess.run([optimizer,cost],feed_dict={x:batch_xs,y:batch_ys})

            #Conpute average loss
            avg_cost+= c/total_batch
        if (epoch+1) % display_step==0:
            print("Epoch:",'%04d' % (epoch+1),"Cost:" ,"{:.09f}".format(avg_cost))

    print("Optimization Finished!")

    #Test model
    correct_prediction=tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
    # Calculate accuracy for 3000 examples
    accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    print("Accuracy:",accuracy.eval({x:mnist.test.images[:3000],y:mnist.test.labels[:3000]}))

結果截圖：