基于卷积神经网络的星座图识别.zip

# -*- coding: utf-8 -*- """ Created on Tue May 14 14:11:47 2019 @author: user """ #======================================================================== #输入数据：（batch_size，IMG_W，IMG_H，col_channel）= （20, 64, 64, 3） # #卷积层1： （conv_kernel，num_channel，num_out_neure）= （3, 3, 3, 64） # #池化层1： （ksize，strides，padding）= （[1,3,3,1]， [1,2,2,1]， 'SAME'） # #卷积层2： （conv_kernel，num_channel，num_out_neure）= （3, 3, 64, 16） # #池化层2： （ksize，strides，padding）= （[1,3,3,1]， [1,1,1,1]， 'SAME'） # #全连接1： （out_pool2_reshape，num_out_neure）= （dim， 128） # #全连接2： （fc1_out，num_out_neure）= （128，128） # #softmax层： （fc2_out，num_classes） = （128, 4） # #激活函数： tf.nn.relu # #损失函数： tf.nn.sparse_softmax_cross_entropy_with_logits #--------------------- #========================================================================= import tensorflow as tf #========================================================================= #网络结构定义 #输入参数：images，image batch、4D tensor、tf.float32、[batch_size, width, height, channels] #返回参数：logits, float、 [batch_size, n_classes] def inference(images, batch_size, n_classes): #一个简单的卷积神经网络，卷积+池化层x2，全连接层x2，最后一个softmax层做分类。 #卷积层1 #64个3x3的卷积核（3通道），padding=’SAME’，表示padding后卷积的图与原图尺寸一致，激活函数relu() with tf.variable_scope('conv1') as scope: weights = tf.Variable(tf.truncated_normal(shape=[3,3,3,64], stddev = 1.0, dtype = tf.float32), name = 'weights', dtype = tf.float32) biases = tf.Variable(tf.constant(value = 0.1, dtype = tf.float32, shape = [64]), name = 'biases', dtype = tf.float32) conv = tf.nn.conv2d(images, weights, strides=[1,1,1,1], padding='SAME') pre_activation = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(pre_activation, name= scope.name) #池化层1 #3x3最大池化，步长strides为2，池化后执行lrn()操作，局部响应归一化，对训练有利。 with tf.variable_scope('pooling1_lrn') as scope: pool1 = tf.nn.max_pool(conv1, ksize=[1,3,3,1],strides=[1,2,2,1],padding='SAME', name='pooling1') norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001/9.0, beta=0.75, name='norm1') #卷积层2 #16个3x3的卷积核（16通道），padding=’SAME’，表示padding后卷积的图与原图尺寸一致，激活函数relu() with tf.variable_scope('conv2') as scope: weights = tf.Variable(tf.truncated_normal(shape=[3,3,64,16], stddev = 0.1, dtype = tf.float32), name = 'weights', dtype = tf.float32) biases = tf.Variable(tf.constant(value = 0.1, dtype = tf.float32, shape = [16]), name = 'biases', dtype = tf.float32) conv = tf.nn.conv2d(norm1, weights, strides = [1,1,1,1],padding='SAME') pre_activation = tf.nn.bias_add(conv, biases) conv2 = tf.nn.relu(pre_activation, name='conv2') #池化层2 #3x3最大池化，步长strides为2，池化后执行lrn()操作， #pool2 and norm2 with tf.variable_scope('pooling2_lrn') as scope: norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001/9.0,beta=0.75,name='norm2') pool2 = tf.nn.max_pool(norm2, ksize=[1,3,3,1], strides=[1,1,1,1],padding='SAME',name='pooling2') #全连接层3 #128个神经元，将之前pool层的输出reshape成一行，激活函数relu() with tf.variable_scope('local3') as scope: reshape = tf.reshape(pool2, shape=[batch_size, -1]) dim = reshape.get_shape()[1].value weights = tf.Variable(tf.truncated_normal(shape=[dim,128], stddev = 0.005, dtype = tf.float32), name = 'weights', dtype = tf.float32) biases = tf.Variable(tf.constant(value = 0.1, dtype = tf.float32, shape = [128]), name = 'biases', dtype=tf.float32) local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name) #全连接层4 #128个神经元，激活函数relu() with tf.variable_scope('local4') as scope: weights = tf.Variable(tf.truncated_normal(shape=[128,128], stddev = 0.005, dtype = tf.float32), name = 'weights',dtype = tf.float32) biases = tf.Variable(tf.constant(value = 0.1, dtype = tf.float32, shape = [128]), name = 'biases', dtype = tf.float32) local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4') #dropout层 # with tf.variable_scope('dropout') as scope: # drop_out = tf.nn.dropout(local4, 0.8) #Softmax回归层 #将前面的FC层输出，做一个线性回归，计算出每一类的得分，在这里是2类，所以这个层输出的是两个得分。 with tf.variable_scope('softmax_linear') as scope: weights = tf.Variable(tf.truncated_normal(shape=[128, n_classes], stddev = 0.005, dtype = tf.float32), name = 'softmax_linear', dtype = tf.float32) biases = tf.Variable(tf.constant(value = 0.1, dtype = tf.float32, shape = [n_classes]), name = 'biases', dtype = tf.float32) softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear') return softmax_linear #----------------------------------------------------------------------------- #loss计算 #传入参数：logits，网络计算输出值。labels，真实值，在这里是0或者1 #返回参数：loss，损失值 def losses(logits, labels): with tf.variable_scope('loss') as scope: cross_entropy =tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels, name='xentropy_per_example') loss = tf.reduce_mean(cross_entropy, name='loss') tf.summary.scalar(scope.name+'/loss', loss) return loss #-------------------------------------------------------------------------- #loss损失值优化 #输入参数：loss。learning_rate，学习速率。 #返回参数：train_op，训练op，这个参数要输入sess.run中让模型去训练。 def trainning(loss, learning_rate): with tf.name_scope('optimizer'): optimizer = tf.train.AdamOptimizer(learning_rate= learning_rate) global_step = tf.Variable(0, name='global_step', trainable=False) train_op = optimizer.minimize(loss, global_step= global_step) return train_op #----------------------------------------------------------------------- #评价/准确率计算 #输入参数：logits，网络计算值。labels，标签，也就是真实值，在这里是0或者1。 #返回参数：accuracy，当前step的平均准确率，也就是在这些batch中多少张图片被正确分类了。 def evaluation(logits, labels): with tf.variable_scope('accuracy') as scope: correct = tf.nn.in_top_k(logits, labels, 1) correct = tf.cast(correct, tf.float16) accuracy = tf.reduce_mean(correct) tf.summary.scalar(scope.name+'/accuracy', accuracy) return accuracy #========================================================================