TensorFlow实践——Softmax Regression

Softmax Regression是Logistic回归在多分类上的推广，对于Logistic回归以及Softmax Regression的详细介绍可以参见：

• 简单易学的机器学习算法——Logistic回归
• 利用Theano理解深度学习——Logistic Regression
• 深度学习算法原理——Softmax Regression

下面的代码是利用TensorFlow基本API实现的Softmax Regression：

'''
@author:zhaozhiyong
@date:20170822
Softmax Regression
'''

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("./MNIST_data", one_hot=True)

learning_rate = 0.01
training_epochs = 1000
batch_size = 100
display_step = 50

n_input = 784
n_classes = 10

x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [None, n_classes])

w1 = tf.Variable(tf.random_normal([n_input, n_classes]))
b1 = tf.Variable(tf.random_normal([n_classes]))

pred = tf.add(tf.matmul(x, w1), b1)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(cost)

init = tf.global_variables_initializer()

with tf.Session() as sess:
	sess.run(init)
	for epoch in range(training_epochs):
		avg_cost = 0
		total_batch = int(mnist.train.num_examples/batch_size)
		for i in range(total_batch):
			batch_x, batch_y = mnist.train.next_batch(batch_size)
			_, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
			avg_cost += c / total_batch
		if epoch % display_step == 0:
			print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost)
	print "Optimization Finished!"

	print "Get test data:"    	
	correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    	accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    	print "Accuracy:", accuracy.eval（{x: mnist.test.images, y: mnist.test.labels})

以下是运行的结果：

参考文献

1. [03]tensorflow实现softmax回归（softmax regression）