很早之前写的，鉴于之前Tensorflow学习笔记有1，不能这么轻易弃坑，所以又加了这一部分。
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import tensorflow as tf import read_data import time import numpy mnist = read_data.DataSets(dlimit = 300) sess = tf.InteractiveSession() x = tf.placeholder(tf.float32,shape=[None,784]) yy = tf.placeholder(tf.float32,shape=[None,10]) w = tf.Variable(tf.zeros([784,10])) b = tf.Variable(tf.zeros([10])) sess.run(tf.global_variables_initializer()) y = tf.nn.softmax(tf.matmul(x,w)+b) cross_entropy = -tf.reduce_sum(yy*tf.log(y)) train_step = tf.train.GradientDescentOptimizer(0.0001).minimize(cross_entropy) for i in range(0,600): print("Iteration %d"%i) batch_xs,batch_ys = mnist.train.next_batch(50) train_step.run(feed_dict={x:batch_xs,yy:batch_ys}) correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(yy,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print(accuracy.eval(feed_dict={x: mnist.test.images, yy:mnist.test.labels})) |
这是最简单的线性回归算法，理解了这种算法，其实就可以搞懂Tensorflow的运行机制了，大概就是把梯度下降的图提前建出来，然后在对于训练数据一次次在图上迭代。
接下来是我自己写的CNN代码，自以为是的用了多个不同的卷积核，具体有没有用？Who Knows？毕竟玄学
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import tensorflow as tf import read_data import numpy import random def col_calc(x): x_level1 = tf.reshape(x,[-1,28,28,1]) w_level1 = tf.Variable(tf.truncated_normal([3,16,1,64],stddev=.1)) b_level1 = tf.Variable(tf.constant(.1,shape=[64])) r_level1 = tf.nn.relu(tf.nn.conv2d(x_level1,w_level1,strides=[1,1,4,1],padding='VALID') + b_level1) x_level2 = tf.reshape(r_level1,[-1,26*4*64]) w_level2 = tf.Variable(tf.truncated_normal([26*4*64,2048],stddev=.1)) b_level2 = tf.Variable(tf.constant(.1,shape=[2048])) r_level2 = tf.nn.relu(tf.matmul(x_level2,w_level2)+b_level2) return r_level2 def row_calc(x): x_level1 = tf.reshape(x,[-1,28,28,1]) w_level1 = tf.Variable(tf.truncated_normal([16,3,1,32],stddev=.1)) b_level1 = tf.Variable(tf.constant(.1,shape=[32])) r_level1 = tf.nn.relu(tf.nn.conv2d(x_level1,w_level1,strides=[1,4,1,1],padding='VALID') + b_level1) x_level2 = tf.reshape(r_level1,[-1,26*4*32]) w_level2 = tf.Variable(tf.truncated_normal([26*4*32,1024],stddev=.1)) b_level2 = tf.Variable(tf.constant(.1,shape=[1024])) r_level2 = tf.nn.relu(tf.matmul(x_level2,w_level2)+b_level2) return r_level2 def block_calc(x): ''' 第一层：使用5*5卷积核做卷积，特征数取为16个 ''' x_level1 = tf.reshape(x,[-1,28,28,1]) w_level1 = tf.Variable(tf.truncated_normal([5,5,1,32],stddev=.1)) b_level1 = tf.Variable(tf.constant(.1,shape=[32])) #28*28*1 r_level1 = tf.nn.relu(tf.nn.conv2d(x_level1,w_level1,strides=[1,1,1,1],padding='VALID') + b_level1) #24*24*16 h_level1 = tf.nn.max_pool(r_level1,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') #12*12*16 ''' 第二层：使用5*5卷积核做卷积 ''' x_level2 = h_level1 w_level2 = tf.Variable(tf.truncated_normal([5,5,32,64],stddev=.1)); b_level2 = tf.Variable(tf.constant(.1,shape=[64])); r_level2 = tf.nn.relu(tf.nn.conv2d(x_level2,w_level2,strides=[1,1,1,1],padding='SAME') + b_level2) h_level2 = tf.nn.max_pool(r_level2,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') #12*12*256 x_level3 = tf.reshape(h_level2,[-1,6*6*64]) w_level3 = tf.Variable(tf.truncated_normal([6*6*64,1024],stddev=.1)) b_level3 = tf.Variable(tf.constant(.1,shape=[1024])) r_level3 = tf.nn.relu(tf.matmul(x_level3,w_level3)+b_level3) return r_level3 if __name__ == '__main__': #预处理数组 mnist = read_data.DataSets(dlimit=-1) #初始化session sess = tf.InteractiveSession() x = tf.placeholder(tf.float32,shape=[None,784]) yy = tf.placeholder(tf.float32,shape=[None,10]) kp = tf.placeholder(tf.float32) r_block = block_calc(x) r_row = row_calc(x) r_col = col_calc(x) r_all = tf.concat(axis=1,values=[r_block,r_row,r_col]) x_level4 = tf.nn.dropout(r_all,kp) w_level4 = tf.Variable(tf.truncated_normal([4096,10],stddev=.1)) b_level4 = tf.Variable(tf.constant(.1,shape=[10])) r_level4 = tf.matmul(x_level4,w_level4)+b_level4 y = tf.nn.softmax(r_level4) cross_entropy = tf.reduce_sum(-tf.log(y) * yy) correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(yy,1)) # 计算准确度 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) train_step = tf.train.AdamOptimizer(5*1e-5).minimize(cross_entropy) sess.run(tf.global_variables_initializer()) lastans = -1e10 for i in range(0,1000000): if (i%50 == 0): print("Iteration #%d..."%i) batch_xs,batch_ys = mnist.train.next_batch(60) train_step.run(feed_dict={x:batch_xs,yy:batch_ys,kp:0.5}) if i%200 == 20: train_accuracy = accuracy.eval(feed_dict={x:batch_xs, yy: batch_ys,kp: 1.0}) print("step %d, current training accuracy %g"%(i, train_accuracy)) if i%1000 == 20: train_accuracy = accuracy.eval(feed_dict={x:mnist.test.images, yy:mnist.test.labels,kp: 1.0}) print("step %d, training accuracy %g"%(i, train_accuracy)) if train_accuracy>=lastans: print("update result...") lastans = train_accuracy correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(yy,1)) final_prediction = tf.argmax(y,1); result = final_prediction.eval(feed_dict={x:mnist.uploads.images,kp:1}) print("saving result...") mnist.uploads.labels = result mnist.uploads.save('result.csv') mnist.uploads.save2('result2.csv') |