【Tensorflow】卷积神经网络实现车牌识别.zip

import tensorflow as tf import numpy as np def inference (images,keep_prob): ''' Build the model Args: image: image batch,4D tensor,tf.float32,[batch_size,height,width,channels] Returns: output tensor with the computed logits,float,[batch_size,65] ''' # conv1 with tf.variable_scope('conv1') as scope: weights = tf.get_variable('weights', shape = [3,3,3,32], dtype = tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32)) conv = tf.nn.conv2d(images,weights,strides=[1,1,1,1],padding='VALID') biases = tf.get_variable('biases', shape=[32], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) pre_activation = tf.nn.bias_add(conv,biases) conv1 = tf.nn.relu(pre_activation,name= scope.name) # conv2 with tf.variable_scope('conv2') as scope: weights = tf.get_variable('weights',shape=[3,3,32,32],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32)) conv = tf.nn.conv2d(conv1,weights,strides=[1,1,1,1],padding='VALID') biases = tf.get_variable('biases', shape=[32], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) pre_activation = tf.nn.bias_add(conv,biases) conv2 = tf.nn.relu(pre_activation,name= scope.name) with tf.variable_scope('max_pooling1') as scope: pool1 = tf.nn.max_pool(conv2,ksize = [1,2,2,1],strides= [1,2,2,1],padding='VALID',name='pooling1') #conv3 with tf.variable_scope('conv3') as scope: weights = tf.get_variable('weights',shape=[3,3,32,64],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32)) conv = tf.nn.conv2d(pool1,weights,strides=[1,1,1,1],padding='VALID') biases = tf.get_variable('biases',shape=[64],dtype = tf.float32,initializer= tf.constant_initializer(0.1)) pre_activation = tf.nn.bias_add(conv,biases) conv3 = tf.nn.relu(pre_activation,name=scope.name) #conv4 with tf.variable_scope('conv4') as scope: weights = tf.get_variable('weights',shape=[3,3,64,64],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32)) conv =tf.nn.conv2d(conv3,weights,strides=[1,1,1,1],padding='VALID') biases = tf.get_variable('biases',shape=[64],dtype=tf.float32,initializer=tf.constant_initializer(0.1)) pre_activation = tf.nn.bias_add(conv,biases) conv4 = tf.nn.relu(pre_activation,name=scope.name) with tf.variable_scope('max_pooling2') as scope: pool2 = tf.nn.max_pool(conv4,ksize=[1,2,2,1],strides=[1,2,2,1],padding='VALID',name='pooling2') #conv5 with tf.variable_scope('conv5') as scope: weights = tf.get_variable('weights',shape=[3,3,64,128],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32)) conv =tf.nn.conv2d(pool2,weights,strides=[1,1,1,1],padding='VALID') biases = tf.get_variable('biases',shape=[128],dtype=tf.float32,initializer=tf.constant_initializer(0.1)) pre_activation = tf.nn.bias_add(conv,biases) conv5 = tf.nn.relu(pre_activation,name=scope.name) #conv6 with tf.variable_scope('conv6') as scope: weights = tf.get_variable('weights',shape=[3,3,128,128],dtype=tf.float32,initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32)) conv =tf.nn.conv2d(conv5,weights,strides=[1,1,1,1],padding='VALID') biases = tf.get_variable('biases',shape=[128],dtype=tf.float32,initializer=tf.constant_initializer(0.1)) pre_activation = tf.nn.bias_add(conv,biases) conv6 = tf.nn.relu(pre_activation,name=scope.name) #pool3 with tf.variable_scope('max_pool3') as scope: pool3 = tf.nn.max_pool(conv6,ksize=[1,2,2,1],strides=[1,2,2,1],padding='VALID',name='pool3') #%% #fc1_flatten # 第一个全连接层，识别车牌上的省 with tf.variable_scope('fc1') as scope: shp = pool3.get_shape() flattened_shape =shp[1].value*shp[2].value*shp[3].value reshape = tf.reshape(pool3,[-1,flattened_shape]) fc1 = tf.nn.dropout(reshape,keep_prob,name='fc1_dropdot') # 第二个全连接层，识别车牌上的市 with tf.variable_scope('fc21') as scope: weights = tf.get_variable('weights', shape=[flattened_shape,65], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32)) biases = tf.get_variable('biases', shape=[65], dtype=tf.float32, initializer = tf.truncated_normal_initializer(0.1) ) fc21 = tf.matmul(fc1,weights)+biases #第三个全连接层识别，识别车牌第三位字母数字，以此类推4567 with tf.variable_scope('fc22') as scope: weights = tf.get_variable('weights', shape=[flattened_shape,65], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32)) biases = tf.get_variable('biases', shape=[65], dtype=tf.float32, initializer = tf.truncated_normal_initializer(0.1) ) fc22 = tf.matmul(fc1,weights)+biases with tf.variable_scope('fc23') as scope: weights = tf.get_variable('weights', shape=[flattened_shape,65], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32)) biases = tf.get_variable('biases', shape=[65], dtype=tf.float32, initializer = tf.truncated_normal_initializer(0.1) ) fc23= tf.matmul(fc1,weights)+biases with tf.variable_scope('fc24') as scope: weights = tf.get_variable('weights', shape=[flattened_shape,65], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32)) biases = tf.get_variable('biases', shape=[65], dtype=tf.float32, initializer = tf.truncated_normal_initializer(0.1) ) fc24 = tf.matmul(fc1,weights)+biases with tf.variable_scope('fc25') as scope: weights = tf.get_variable('weights', shape=[flattened_shape,65], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.005,dtype=tf.float32)) biases = tf.get_variable('biases', shape=[65], dtype=tf.float32, initializer = tf.truncated_normal_initializer(0.1) ) fc25 = tf.matmul(fc1,weights)+biases with tf.variable_scope('fc26') as scope: weights = tf.get_variable('weights', shape=[flattened_shape,65],