tensorFlow 基础见前博客
逻辑回归广泛应用在各类分类，回归任务中。本实验介绍逻辑回归在 TensorFlow 上的实现
理论知识回顾
逻辑回归的主要公式罗列如下：
激活函数（activation function）：
损失函数（cost function）：
其中
损失函数求偏导（derivative cost function）：
训练模型
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数据准备首先我们需要先下载MNIST的数据集。使用以下的命令进行下载：
wget https://devlab-1251520893.cos.ap-guangzhou.myqcloud.com/t10k-images-idx3-ubyte.gz wget https://devlab-1251520893.cos.ap-guangzhou.myqcloud.com/t10k-labels-idx1-ubyte.gz wget https://devlab-1251520893.cos.ap-guangzhou.myqcloud.com/train-images-idx3-ubyte.gz wget https://devlab-1251520893.cos.ap-guangzhou.myqcloud.com/train-labels-idx1-ubyte.gz
创建代码
#-*- coding:utf-8 -*-
import time
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
MNIST = input_data.read_data_sets("./", one_hot=True)
learning_rate = 0.01
batch_size = 128
n_epochs = 25
X = tf.placeholder(tf.float32, [batch_size, 784])
Y = tf.placeholder(tf.float32, [batch_size, 10])
w = tf.Variable(tf.random_normal(shape=[784,10], stddev=0.01), name="weights")
b = tf.Variable(tf.zeros([1, 10]), name="bias")
logits = tf.matmul(X, w) + b
entropy = tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=logits)
loss = tf.reduce_mean(entropy)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
n_batches = int(MNIST.train.num_examples/batch_size)
for i in range(n_epochs):
for j in range(n_batches):
X_batch, Y_batch = MNIST.train.next_batch(batch_size)
_, loss_ = sess.run([optimizer, loss], feed_dict={ X: X_batch, Y: Y_batch})
print "Loss of epochs[{0}] batch[{1}]: {2}".format(i, j, loss_)
执行结果
Loss of epochs[0] batch[0]: 2.28968191147 Loss of epochs[0] batch[1]: 2.30224704742 Loss of epochs[0] batch[2]: 2.26435565948 Loss of epochs[0] batch[3]: 2.26956915855 Loss of epochs[0] batch[4]: 2.25983452797 Loss of epochs[0] batch[5]: 2.2572259903 ...... Loss of epochs[24] batch[420]: 0.393310219049 Loss of epochs[24] batch[421]: 0.309725940228 Loss of epochs[24] batch[422]: 0.378903746605 Loss of epochs[24] batch[423]: 0.472946226597 Loss of epochs[24] batch[424]: 0.259472459555 Loss of epochs[24] batch[425]: 0.290799200535 Loss of epochs[24] batch[426]: 0.256865829229 Loss of epochs[24] batch[427]: 0.250789999962 Loss of epochs[24] batch[428]: 0.328135550022
测试模型
#-*- coding:utf-8 -*-
import time
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
MNIST = input_data.read_data_sets("./", one_hot=True)
learning_rate = 0.01
batch_size = 128
n_epochs = 25
X = tf.placeholder(tf.float32, [batch_size, 784])
Y = tf.placeholder(tf.float32, [batch_size, 10])
w = tf.Variable(tf.random_normal(shape=[784,10], stddev=0.01), name="weights")
b = tf.Variable(tf.zeros([1, 10]), name="bias")
logits = tf.matmul(X, w) + b
entropy = tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=logits)
loss = tf.reduce_mean(entropy)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
n_batches = int(MNIST.train.num_examples/batch_size)
for i in range(n_epochs):
for j in range(n_batches):
X_batch, Y_batch = MNIST.train.next_batch(batch_size)
_, loss_ = sess.run([optimizer, loss], feed_dict={ X: X_batch, Y: Y_batch})
print "Loss of epochs[{0}] batch[{1}]: {2}".format(i, j, loss_)
n_batches = int(MNIST.test.num_examples/batch_size)
total_correct_preds = 0
for i in range(n_batches):
X_batch, Y_batch = MNIST.test.next_batch(batch_size)
preds = tf.nn.softmax(tf.matmul(X_batch, w) + b)
correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(Y_batch, 1))
accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32))
total_correct_preds += sess.run(accuracy)
print "Accuracy {0}".format(total_correct_preds/MNIST.test.num_examples)
执行结果
Accuracy 0.9108
来源：https://www.cnblogs.com/fclbky/p/9646603.html