TensorFlow入门——MNIST深入

 #load MNIST data
 import tensorflow.examples.tutorials.mnist.input_data as input_data
 mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)

 #start tensorflow interactiveSession
 import tensorflow as tf
 sess = tf.InteractiveSession()

 #weight initilization
 def weight_variable(shape):
     initial = tf.truncated_normal(shape, stddev=0.1)
     return tf.Variable(initial)

 def bias_variable(shape):
     initial = tf.constant(0.1, shape= shape)
     return tf.Variable(initial)

 #convolution
 def conv2d(x, W):
     return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')

 #pooling
 def max_pool_2x2(x):
     return tf.nn.max_pool(x, ksize=[1,2,2,1],strides=[1,2,2,1], padding='SAME')

 #Create the model
 #placeholder
 x = tf.placeholder("float",[None, 784])
 y_ = tf.placeholder("float", [None, 10])

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

 y = tf.nn.softmax(tf.matmul(x,W) +b)

 #first convolutional layer
 w_conv1 = weight_variable([5,5,1,32])
 b_conv1 = bias_variable([32])

 x_image = tf.reshape(x,[-1,28,28,1])

 h_conv1 =tf.nn.relu(conv2d(x_image,w_conv1) + b_conv1)
 h_pool1 =max_pool_2x2(h_conv1)

 #second convolutional layer
 w_conv2 = weight_variable([5,5,32,64])
 b_conv2 = bias_variable([64])

 h_conv2 =tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)
 h_pool2 =max_pool_2x2(h_conv2)

 #densely connected layer
 w_fc1 = weight_variable([7*7*64, 1024])
 b_fc1 = bias_variable([1024])

 h_pool2_flat = tf.reshape(h_pool2, [-1,7*7*64])
 h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)

 #dropout
 keep_prob = tf.placeholder("float")
 h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

 #readout layer
 w_fc2 = weight_variable([1024,10])
 b_fc2 = bias_variable([10])

 y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop,w_fc2) + b_fc2)

 #train and evaluate the model
 cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
 #train_step = tf.train.AdagradOptimizer(1e-4).minimize(cross_entropy)
 train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(cross_entropy)
 correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
 sess.run(tf.initialize_all_variables())
 for i in range(5000):
     batch = mnist.train.next_batch(50)
     if i%100 ==0:
         train_accuracy = accuracy.eval(feed_dict={x:batch[0],y_:batch[1], keep_prob:1.0})
         print "step %d, train accuracy %g " %(i,train_accuracy)
     train_step.run(feed_dict={x:batch[0],y_:batch[1], keep_prob:0.5})

 print "test accuracy %g" % accuracy.eval(fedd_dict={x:mnist.test.images, y_:mnist.test.labels, keep_prob:1.0})

同样是极客学院的课程，其实也是翻译的国外的robot-ai博客上的内容，但是这个博客，现在打不开了，可能是墙的问题？没有太深究。

按照作者的说法，是采用自适应下降的方式，在train阶段能达到99%的正确率，但是，我的结果只有93%左右，修改梯度步长到1e-4也只有94% 。因此尝试换用原来的梯度下降方式，反而能获得97.61%的正确率，在训练中还达到过98%，这个问题比较无奈，修改步长的结果提升也并不明显。有人在评论中说在不同的平台上测试的值不同，比如在纯CPU环境，和我的结果比较相似。在K20环境中能达到99%，这个问题留待以后探索。代码参考至：文章链接： http://blog.csdn.net/yhl_leo/article/details/50624471