TFboy养成记 CNN
1/先解释下CNN的过程:
首先对一张图片进行卷积,可以有多个卷积核,卷积过后,对每一卷积核对应一个chanel,也就是一张新的图片,图片尺寸可能会变小也可能会不变,然后对这个chanel进行一些pooling操作。
最后pooling输出完成,这个算作一个卷积层。
最后对最后一个pooling结果进行一个简单的MLP的判别其就好了
2.代码分步:
2.1 W and bias:注意不要将一些W设为0,一定要注意,这个会在后面一些地方讲到
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1 #注意不要将一些W设为0,一定要注意,这个会在后面一些地方讲到 2 def getWeights(shape): 3 return tf.Variable(tf.truncated_normal(shape,stddev= 0.1)) 4 def getBias(shape): 5 return tf.Variable(tf.constant(0.1))
2.2 卷积层操作:
首先说下tf.nn.conv2d这个函数:
其中官方解释:
这里主要需要了解的是strides的含义:其shape表示的是[batch, in_height, in_width, in_channels]。需要注意的是,看我们在Weights初始化时的shape,我们自己定义的shape格式是[h,w,inchanel,outchanel] --->chanel也就是我们理解的厚度。
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1 def conv2d(x,W): 2 return tf.nn.conv2d(x,W,strides = [1,1,1,1],padding="SAME") 3 #ksize 4 def maxpooling(x): 5 return tf.nn.max_pool(x,ksize=[1,2,2,1],strides = [1,2,2,1],padding= "SAME")
关于data_format
padding也有两种方式:
其他地方其实也没有什么新操作所有代码在下面:
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1 # -*- coding: utf-8 -*- 2 """ 3 Spyder Editor 4 5 This is a temporary script file. 6 """ 7 from tensorflow.examples.tutorials.mnist import input_data 8 import tensorflow as tf 9 import numpy as np 10 #注意不要将一些W设为0,一定要注意,这个会在后面一些地方讲到 11 def getWeights(shape): 12 return tf.Variable(tf.truncated_normal(shape,stddev= 0.1)) 13 def getBias(shape): 14 return tf.Variable(tf.constant(0.1)) 15 #构造卷积层 strides前一个跟最后后一个为1,其他表示方向,padding一般是有两种方式 ,一个是SAME还有一个是VALID 16 #前者卷积后不改变大小后一个卷积后一般会变小 17 #strides--->data_format:data_format: An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width]. 18 # 19 def conv2d(x,W): 20 return tf.nn.conv2d(x,W,strides = [1,1,1,1],padding="SAME") 21 #ksize 22 def maxpooling(x): 23 return tf.nn.max_pool(x,ksize=[1,2,2,1],strides = [1,2,2,1],padding= "SAME") 24 def compute_acc(v_xs,v_ys): 25 global predict 26 y_pre = sess.run(predict,feed_dict = {xs:v_xs,keep_prob:1}) 27 tmp = tf.equal(tf.arg_max(y_pre,1),tf.arg_max(v_ys,1)) 28 accuracy = tf.reduce_mean(tf.cast(tmp,tf.float32)) 29 return sess.run(accuracy,feed_dict = {xs:v_xs,ys:v_ys,keep_prob:1}) 30 31 32 mnist = input_data.read_data_sets("MNIST_data",one_hot=True) 33 xs = tf.placeholder(tf.float32,[None,28*28]) 34 ys = tf.placeholder(tf.float32,[None,10]) 35 keep_prob = tf.placeholder(tf.float32) 36 37 x_images = tf.reshape(xs,[-1,28,28,1]) 38 39 W_c1 = getWeights([5,5,1,32]) 40 b_c1 = getBias([32]) 41 h_c1 = tf.nn.relu(conv2d(x_images,W_c1)+b_c1) 42 h_p1 = maxpooling(h_c1) 43 44 W_c2 = getWeights([5,5,32,64]) 45 b_c2 = getBias([64]) 46 h_c2 = tf.nn.relu(conv2d(h_p1,W_c2)+b_c2) 47 h_p2 = maxpooling(h_c2) 48 49 W_fc1 = getWeights([7*7*64,1024]) 50 b_fc1 = getBias([1024]) 51 h_flat = tf.reshape(h_p2,[-1,7*7*64]) 52 h_fc1 = tf.nn.relu(tf.matmul(h_flat,W_fc1)+b_fc1) 53 h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob) 54 55 W_fc2 = getWeights([1024,10]) 56 b_fc2 = getBias([10]) 57 predict = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+b_fc2) 58 59 loss = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(predict), 60 reduction_indices=[1])) 61 train_step = tf.train.AdamOptimizer(0.001).minimize(loss) 62 63 sess = tf.Session() 64 sess.run(tf.initialize_all_variables()) 65 for i in range(1000): 66 batch_xs, batch_ys = mnist.train.next_batch(100) 67 sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys, keep_prob: 0.5}) 68 if i % 50 == 0: 69 print (compute_acc(mnist.test.images,mnist.test.labels))
需要注意的是nn.dropout()