TensorFlow实现CNN

小咪咪 2022-06-12 01:54 221阅读 0赞

## 1.前言 ##

本文基于TensorFlow实现一个简单的CNN结构用以对图片进行分类

## 2.python代码 ##

import tensorflow as tf 
    from tensorflow.examples.tutorials.mnist import input_data
    
    mnist = input_data.read_data_sets("MNIST_data/",one_hot = True)
    sess = tf.InteractiveSession()
    
    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)
    
    def conv2d(x,W):
    	return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')
    
    def max_pool_2x2(x):
    	return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')
    
    x = tf.placeholder(tf.float32,[None,784])
    y_ = tf.placeholder(tf.float32,[None,10])
    x_image = tf.reshape(x,[-1,28,28,1])
    
    # Conv1 Layer
    W_conv1 = weight_variable([5,5,1,32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)
    
    # Conv2 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)
    
    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)
    
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)
    
    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)
    
    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv),reduction_indices=[1]))
    train_step = tf.train.AdamOptimizer(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,tf.float32))
    
    tf.global_variables_initializer().run()
    for i in range(20000):
    	batch = mnist.train.next_batch(50)
    	if i % 1000 == 0:
    		train_accuracy = accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
    		print("step %d, training 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(feed_dict={x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0}))