基于CNN算法实现四种花的识别

import tensorflow as tf # 网络结构定义,四维参数， # 输入参数：images，image batch、四维张量、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，激活函数为relu() with tf.variable_scope('conv1') as scope: #权重，产生正态分布 weights = tf.Variable(tf.truncated_normal(shape=[3, 3, 3, 64], stddev=1.0, dtype=tf.float32), name='weights', dtype=tf.float32) #偏置，生成值为0.1，shape大小的偏置的值 biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[64]), name='biases', dtype=tf.float32) #卷积函数tf.nn.conv2d,训练数据为images，过滤器为weights conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME') #加上偏置值 pre_activation = tf.nn.bias_add(conv, biases) #激活函数，将特征值转换到另一个空间，更好的分类，不再是线性简单的结果 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。 # 局部归一化，侧抑制，局部神经元竞争，使响应比较大的值相对更大，提高识别率 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1 # 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2 # 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 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 # ----------------------------------------------------------------------------- # 损失值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