TensorFlow之卷積神經網路(CNN)實現MNIST資料集分類
阿新 • • 發佈:2018-12-03
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 #初始化權值 def weirht_variable(shape): initial=tf.truncated_normal(shape,stddev=0.1) return tf.Variable(initial) #初始化偏置 def bias_variable(shape): initial=tf.constant(0.1,shape=shape) return tf.Variable(initial) #卷積層 def conv2d(x,W): #x input tensor of shape '[batch, in_height, in_width, in_channels]' #W filter / kernel tensor of shape [fileter_height,fileter_width,in_channels,out_channels] #strides[0]=strides[3]=1 strides[1]代表x方向的步長,strides[2]代表y方向的步長 #padding:A 'string' from :'SAME','VALID' return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME') #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') #定義兩個placeholder x=tf.placeholder(tf.float32,[None,784]) #28*28 y=tf.placeholder(tf.float32,[None,10]) #改變x的格式轉換為4D的向量[batch,in_height,in_width,in_channels] x_image=tf.reshape(x,[-1,28,28,1]) #初始化第一個卷積層的權值和偏置 W_conv1=weirht_variable([5,5,1,32]) #5*5的取樣視窗,32個卷積核從1個平面抽取特徵 b_conv1=bias_variable([32]) #每一個卷積核對應一個偏置值 #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 h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1) h_pool1=max_pool_2x2(h_conv1) #進行max_pooling #初始化第二個卷積層的權值和偏置 W_conv2=weirht_variable([5,5,32,64]) #5*5取樣視窗,64個卷積核從32個平面抽取特徵 b_conv2=bias_variable([64]) #每一個卷積核對應一個偏置值 #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 h_conv2=tf.nn.relu(conv2d(h_pool1,W_conv2)+b_conv2) 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,第一次池化後變成14*14 #第二次卷積後為14*14,第二次池化後變成7*7 #進行上面的操作後得到64張7*7的平面 #初始化第一個全連線層的權值 W_fc1=weirht_variable([7*7*64,1024]) b_fc1=bias_variable([1024]) #把池化層2的輸出扁平化為1維 h_pool2_flat=tf.reshape(h_pool2,[-1,7*7*64]) #求第一個全連線的輸出 h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+b_fc1) #keep_prob用來表示神經元輸出概率 keep_prob=tf.placeholder(tf.float32) h_gc1_drop=tf.nn.dropout(h_fc1,keep_prob) #初始化第二個全連線層 W_fc2=weirht_variable([1024,10]) b_fc2=bias_variable([10]) #計算輸出 prediction=tf.nn.softmax(tf.matmul(h_fc1,W_fc2)+b_fc2) #交叉熵代價函式 cross_entropy=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)) #使用AdamOptimizer進行優化 train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) #結果儲存在一個布林列表中 correct_prediction=tf.equal(tf.argmax(prediction,1),tf.argmax(y,1)) #求準確率 accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for epoch in range(21): for batch in range(n_batch): batch_xs,batch_ys=mnist.train.next_batch(batch_size) sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:0.7}) acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0}) print("Iter "+str(epoch)+",Testing Accuracy="+str(acc))
Iter 0,Testing Accuracy=0.8593
Iter 1,Testing Accuracy=0.8752
Iter 2,Testing Accuracy=0.8771
Iter 3,Testing Accuracy=0.8826
Iter 4,Testing Accuracy=0.8821
Iter 5,Testing Accuracy=0.8835
Iter 6,Testing Accuracy=0.8825
Iter 7,Testing Accuracy=0.8853
Iter 8,Testing Accuracy=0.8881
Iter 9,Testing Accuracy=0.888
Iter 10,Testing Accuracy=0.8872
Iter 11,Testing Accuracy=0.8878
Iter 12,Testing Accuracy=0.985
Iter 13,Testing Accuracy=0.9897
Iter 14,Testing Accuracy=0.9887
Iter 15,Testing Accuracy=0.9876
Iter 16,Testing Accuracy=0.991
Iter 17,Testing Accuracy=0.9912
Iter 18,Testing Accuracy=0.991
Iter 19,Testing Accuracy=0.9888
Iter 20,Testing Accuracy=0.9908