Tensorflow的應用(五)
阿新 • • 發佈:2019-01-03
本小節主要是構建卷積神經網路,本小節構建的卷積網路過程如下:
原圖片->第一層非線性卷積->第一層池化->第二層非線性卷積->第二層池化->第一層全連線->第二層全連線
程式碼如下所示,上面有註釋,就不詳細再解釋。
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('MNIST_data',one_hot=True) #每個批次的大小 batch_size = 100 #計算一共有多少個批次 n_batch = mnist.train.num_examples // batch_size #引數概要 def variable_summaries(var): with tf.name_scope('summaries'): mean = tf.reduce_mean(var) tf.summary.scalar('mean', mean)#平均值 with tf.name_scope('stddev'): stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))) tf.summary.scalar('stddev', stddev)#標準差 tf.summary.scalar('max', tf.reduce_max(var))#最大值 tf.summary.scalar('min', tf.reduce_min(var))#最小值 tf.summary.histogram('histogram', var)#直方圖 #初始化權值 def weight_variable(shape,name): initial = tf.truncated_normal(shape,stddev=0.1)#生成一個截斷的正態分佈 return tf.Variable(initial,name=name) #初始化偏置 def bias_variable(shape,name): initial = tf.constant(0.1,shape=shape) return tf.Variable(initial,name=name) #卷積層 def conv2d(x,W): return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME') #strides[0]代表x方向的步長,strides[1]、strides[2]代表padding視窗大小,strides[3]代表y方向的步長,這裡採用same padding, #same padding採取在圖片外圍補0的方式,使得進行卷積以後的圖片大小與原圖片大小相同,從strides[1]=strides[2]=1可知,補一圈0 #池化層 def max_pool_2x2(x): #ksize [1,x,y,1] return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') #ksize[0]、ksize[3]分別表示pooling視窗在x、y方向的步長,ksize[1]、ksize[2]表示pooling視窗的大小,strides含義跟上面相同。 #名稱空間 with tf.name_scope('input'): #定義兩個placeholder x = tf.placeholder(tf.float32,[None,784],name='x-input') y = tf.placeholder(tf.float32,[None,10],name='y-input') with tf.name_scope('x_image'): #改變x的格式轉為4D的向量[batch, in_height, in_width, in_channels]` x_image = tf.reshape(x,[-1,28,28,1],name='x_image')#將784畫素的圖片轉為28*28畫素 with tf.name_scope('Conv1'): #初始化第一個卷積層的權值和偏置 with tf.name_scope('W_conv1'): W_conv1 = weight_variable([5,5,1,32],name='W_conv1')#5*5的取樣視窗,32個卷積核從1個平面抽取特徵 with tf.name_scope('b_conv1'): b_conv1 = bias_variable([32],name='b_conv1')#每一個卷積核一個偏置值 #把x_image和權值向量進行卷積,再加上偏置值,然後應用於relu啟用函式 with tf.name_scope('conv2d_1'): conv2d_1 = conv2d(x_image,W_conv1) + b_conv1 with tf.name_scope('relu'): h_conv1 = tf.nn.relu(conv2d_1) with tf.name_scope('h_pool1'): h_pool1 = max_pool_2x2(h_conv1)#進行max-pooling with tf.name_scope('Conv2'): #初始化第二個卷積層的權值和偏置 with tf.name_scope('W_conv2'): W_conv2 = weight_variable([5,5,32,64],name='W_conv2')#5*5的取樣視窗,64個卷積核從32個平面抽取特徵 with tf.name_scope('b_conv2'): b_conv2 = bias_variable([64],name='b_conv2')#每一個卷積核一個偏置值 #把h_pool1和權值向量進行卷積,再加上偏置值,然後應用於relu啟用函式 with tf.name_scope('conv2d_2'): conv2d_2 = conv2d(h_pool1,W_conv2) + b_conv2 with tf.name_scope('relu'): h_conv2 = tf.nn.relu(conv2d_2) with tf.name_scope('h_pool2'): h_pool2 = max_pool_2x2(h_conv2)#進行max-pooling #28*28的圖片第一次卷積後還是28*28,第一次池化後變為14*14 #第二次卷積後為14*14,第二次池化後變為了7*7 #進過上面操作後得到64張7*7的平面 with tf.name_scope('fc1'): #初始化第一個全連線層的權值 with tf.name_scope('W_fc1'): W_fc1 = weight_variable([7*7*64,1024],name='W_fc1')#上一層有7*7*64個神經元,全連線層有1024個神經元 with tf.name_scope('b_fc1'): b_fc1 = bias_variable([1024],name='b_fc1')#1024個節點 #把池化層2的輸出扁平化為1維 with tf.name_scope('h_pool2_flat'): h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64],name='h_pool2_flat') #求第一個全連線層的輸出 with tf.name_scope('wx_plus_b1'): wx_plus_b1 = tf.matmul(h_pool2_flat,W_fc1) + b_fc1 with tf.name_scope('relu'): h_fc1 = tf.nn.relu(wx_plus_b1) #keep_prob用來表示神經元的輸出概率 with tf.name_scope('keep_prob'): keep_prob = tf.placeholder(tf.float32,name='keep_prob') with tf.name_scope('h_fc1_drop'): h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob,name='h_fc1_drop') with tf.name_scope('fc2'): #初始化第二個全連線層 with tf.name_scope('W_fc2'): W_fc2 = weight_variable([1024,10],name='W_fc2') with tf.name_scope('b_fc2'): b_fc2 = bias_variable([10],name='b_fc2') with tf.name_scope('wx_plus_b2'): wx_plus_b2 = tf.matmul(h_fc1_drop,W_fc2) + b_fc2 with tf.name_scope('softmax'): #計算輸出 prediction = tf.nn.softmax(wx_plus_b2) #交叉熵代價函式 with tf.name_scope('cross_entropy'): cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction),name='cross_entropy') tf.summary.scalar('cross_entropy',cross_entropy) #使用AdamOptimizer進行優化 with tf.name_scope('train'): train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #求準確率 with tf.name_scope('accuracy'): with tf.name_scope('correct_prediction'): #結果存放在一個布林列表中 correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))#argmax返回一維張量中最大的值所在的位置 with tf.name_scope('accuracy'): #求準確率 accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) tf.summary.scalar('accuracy',accuracy) #合併所有的summary merged = tf.summary.merge_all() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) train_writer = tf.summary.FileWriter('logs/train',sess.graph) test_writer = tf.summary.FileWriter('logs/test',sess.graph) for i in range(1001): #訓練模型 batch_xs,batch_ys = mnist.train.next_batch(batch_size) sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:0.5}) #記錄訓練集計算的引數 summary = sess.run(merged,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.0}) train_writer.add_summary(summary,i) #記錄測試集計算的引數 batch_xs,batch_ys = mnist.test.next_batch(batch_size) summary = sess.run(merged,feed_dict={x:batch_xs,y:batch_ys,keep_prob:1.0}) test_writer.add_summary(summary,i) if i%100==0: test_acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0}) train_acc = sess.run(accuracy,feed_dict={x:mnist.train.images[:10000],y:mnist.train.labels[:10000],keep_prob:1.0}) print ("Iter " + str(i) + ", Testing Accuracy= " + str(test_acc) + ", Training Accuracy= " + str(train_acc))