TensorFlow实践——Multilayer Perceptron

女爷i 2022-06-10 14:08 154阅读 0赞

本文是在Softmax Regression的基础上增加了一个隐含层，实现了Multilayer Perceptron的一个模型，Multilayer Perceptron是深度学习模型的基础，对于Softmax Regression的TensorFlow实现，可以参见博文“[TensorFlow实践——Softmax Regression][TensorFlow_Softmax Regression]”。对于Multilayer Perceptron的基本原理，可以参见以下的文章：

*  [深度学习算法原理——神经网络的基本原理][Link 1]
 *  [利用Theano理解深度学习——Multilayer Perceptron][Theano_Multilayer Perceptron]

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

'''
    @author:zhaozhiyong
    @date:20170821
    Multilayer Perceptron
    '''
    
    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 = 0.05
    training_epochs = 1000
    batch_size = 100
    display_step = 50
    
    n_input = 784
    n_hidden = 256
    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_hidden]))
    b1 = tf.Variable(tf.random_normal([n_hidden]))
    
    hidden = tf.add(tf.matmul(x, w1), b1)
    hidden = tf.nn.relu(hidden)
    #hidden = tf.nn.sigmoid(hidden)
    
    w2 = tf.Variable(tf.random_normal([n_hidden, n_classes]))
    b2 = tf.Variable(tf.random_normal([n_classes]))
    
    pred = tf.add(tf.matmul(hidden, w2), b2)
    
    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})

上述实现的运行结果如下所示：

![这里写图片描述][SouthEast]

# 参考文献 #

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

[TensorFlow_Softmax Regression]: https://blog.csdn.net/google19890102/article/details/77450071
[Link 1]: https://blog.csdn.net/google19890102/article/details/49736619
[Theano_Multilayer Perceptron]: https://blog.csdn.net/google19890102/article/details/49071305
[SouthEast]: /images/20220610/5fed550f17104093ac8f7b67770331f4.png
[03_tensorflow_softmax_softmax regression]: https://blog.csdn.net/sa14023053/article/details/51884894