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3.5 卷积神经网络进阶-Inception-mobile_net 实战

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4.2.5 Inception-mobile_net实战

  • Inception-Net

    Inception Net的思想是分组卷积,上一层分成几组卷积,卷积完成之后在把分组的结果拼接起来

    可以进行扩展,每个组有很多层,这里只实现基本的分组卷积

    # 定义 Inception-Net的分组结构def inception_block(x,
                      output_channel_for_each_path,
                      name):
        """inception block implementation"""
        """
        Args:
        - x: 输入数据
        - output_channel_for_each_path: 每组的输出通道数目 eg: [10,20,30]
        - name: 每组的卷积命名
        """
        # variable_scope 在这个scope下命名不会有冲突 conv1 = 'conv1' => scope_name/conv1
        with tf.variable_scope(name):
            conv1_1 = tf.layers.conv2d(x,
                                       output_channel_for_each_path[0],
                                       (1, 1),
                                       strides = (1,1),
                                       padding = 'same',
                                       activation = tf.nn.relu,
                                       name = 'conv1_1')
            
            conv3_3 = tf.layers.conv2d(x,
                                       output_channel_for_each_path[1],
                                       (3, 3),
                                       strides = (1,1),
                                       padding = 'same',
                                       activation = tf.nn.relu,
                                       name = 'conv3_3')
            conv5_5 = tf.layers.conv2d(x,
                                       output_channel_for_each_path[0],
                                       (5, 5),
                                       strides = (1,1),
                                       padding = 'same',
                                       activation = tf.nn.relu,
                                       name = 'conv5_5')
            max_pooling = tf.layers.max_pooling2d(x,
                                                (2,2),
                                                (2,2),
                                                name = 'max_pooling')        
            # max_pooling 会使得图像变小,所以需要padding
            max_pooling_shape = max_pooling.get_shape().as_list()[1:]
            input_shape = x.get_shape().as_list()[1:]
            width_padding = (input_shape[0] - max_pooling_shape[0]) // 2
            height_padding = (input_shape[1] - max_pooling_shape[1]) // 2
            padded_pooling = tf.pad(max_pooling,
                                    [[0,0],
                                     [width_padding,width_padding],
                                     [height_padding,height_padding],
                                     [0,0]])        
            # 在第四个维度(通道数)上做拼接
            concat_layer = tf.concat(
                [conv1_1, conv3_3, conv5_5, padded_pooling],
                axis = 3)        return concat_layer
            
    x = tf.placeholder(tf.float32, [None, 3072])
    y = tf.placeholder(tf.int64, [None])# 将向量变成具有三通道的图片的格式x_image = tf.reshape(x, [-1,3,32,32])# 32*32x_image = tf.transpose(x_image, perm = [0, 2, 3, 1])# 先经过一个普通的卷积层和池化层# conv1:神经元图,feature map,输出图像conv1 = tf.layers.conv2d(x_image,                           32, # output channel number
                               (3,3), # kernal size
                               padding = 'same', # same 代表输出图像的大小没有变化,valid 代表不做padding
                               activation = tf.nn.relu,
                               name = 'conv1')# 16*16pooling1 = tf.layers.max_pooling2d(conv1,
                                       (2, 2), # kernal size
                                       (2, 2), # stride
                                       name = 'pool1' # name为了给这一层做一个命名,这样会让图打印出来的时候会是一个有意义的图
                                      )# 经过两个个分组卷积inception_2a = inception_block(pooling1, 
                                   [16, 16, 16],
                                   name = 'inception_2a')
    
    inception_2b = inception_block(inception_2a, 
                                   [16, 16, 16],
                                   name = 'inception_2b')# 接一个池化pooling2 = tf.layers.max_pooling2d(inception_2b,
                                       (2, 2), 
                                       (2, 2), 
                                       name = 'pool2' 
                                      )# 再经过两个分组卷积核一个池化inception_3a = inception_block(pooling2, 
                                   [16, 16, 16],
                                   name = 'inception_3a')
    
    inception_3b = inception_block(inception_3a, 
                                   [16, 16, 16],
                                   name = 'inception_3b')
    
    pooling3 = tf.layers.max_pooling2d(inception_3b,
                                       (2, 2), 
                                       (2, 2), 
                                       name = 'pool3' 
                                      )# [None, 4*4*42] 将三通道的图形转换成矩阵flatten = tf.layers.flatten(pooling3)
    y_ = tf.layers.dense(flatten, 10)# 交叉熵loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)# y_-> softmax# y -> one_hot# loss = ylogy_# boolpredict = tf.argmax(y_, 1)# [1,0,1,1,1,0,0,0]correct_prediction = tf.equal(predict, y)
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))with tf.name_scope('train_op'):
        train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)
  • Mobile-Net

    Mobile Net 的基本结构 深度可分类的卷积 -> BN ->RELU-> 1*1 的卷积 -> BN -> RELU

    这里BN先不加,这是下节课的内容

    webp

    image.png

def separable_conv_block(x,
                  output_channel_number,
                  name):
    """separable_conv block implementation"""
    """
    Args:
    - x: 输入数据
    - output_channel_number: 经过深度可分离卷积之后,再经过1*1 的卷积生成的通道数目
    - name: 每组的卷积命名
    """
    # variable_scope 在这个scope下命名不会有冲突 conv1 = 'conv1' => scope_name/conv1
    with tf.variable_scope(name):
        input_channel = x.get_shape().as_list()[-1]        # 将x 在 第四个维度(axis+1) 上 拆分成 input_channel 份
        # channel_wise_x: [channel1, channel2, ...]
        channel_wise_x = tf.split(x, input_channel, axis = 3)
        output_channels = []        for i in range(len(channel_wise_x)):
            output_channel = tf.layers.conv2d(channel_wise_x[i],                                              1,
                                              (3,3),
                                              strides = (1,1),
                                              padding = 'same',
                                              activation = tf.nn.relu,
                                              name = 'conv_%d' % i)
            output_channels.append(output_channel)
        concat_layers = tf.concat(output_channels, axis = 3)
        conv1_1 = tf.layers.conv2d(concat_layers,
                                   output_channel_number,
                                   (1,1),
                                   strides = (1,1),
                                   padding = 'same',
                                   activation = tf.nn.relu,
                                   name = 'conv1_1')        return conv1_1
        
        
x = tf.placeholder(tf.float32, [None, 3072])
y = tf.placeholder(tf.int64, [None])# 将向量变成具有三通道的图片的格式x_image = tf.reshape(x, [-1,3,32,32])# 32*32x_image = tf.transpose(x_image, perm = [0, 2, 3, 1])# conv1:神经元图,feature map,输出图像conv1 = tf.layers.conv2d(x_image,                           32, # output channel number
                           (3,3), # kernal size
                           padding = 'same', # same 代表输出图像的大小没有变化,valid 代表不做padding
                           activation = tf.nn.relu,
                           name = 'conv1')# 16*16pooling1 = tf.layers.max_pooling2d(conv1,
                                   (2, 2), # kernal size
                                   (2, 2), # stride
                                   name = 'pool1' # name为了给这一层做一个命名,这样会让图打印出来的时候会是一个有意义的图
                                  )

separable_2a = separable_conv_block(pooling1, 
                                    32,
                                    name = 'separable_2a')

separable_2b = separable_conv_block(separable_2a, 
                                    32,
                                    name = 'separable_2b')

pooling2 = tf.layers.max_pooling2d(separable_2b,
                                   (2, 2), 
                                   (2, 2), 
                                   name = 'pool2' 
                                  )

separable_3a = separable_conv_block(pooling2, 
                                    32,
                                    name = 'separable_3a')

separable_3b = separable_conv_block(separable_3a, 
                                    32,
                                    name = 'separable_3b')

pooling3 = tf.layers.max_pooling2d(separable_3b,
                                   (2, 2), 
                                   (2, 2), 
                                   name = 'pool3')# [None, 4*4*42] 将三通道的图形转换成矩阵flatten = tf.layers.flatten(pooling3)
y_ = tf.layers.dense(flatten, 10)# 交叉熵loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)# y_-> softmax# y -> one_hot# loss = ylogy_# boolpredict = tf.argmax(y_, 1)# [1,0,1,1,1,0,0,0]correct_prediction = tf.equal(predict, y)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))with tf.name_scope('train_op'):
    train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)

这里的准确率是10000次百分之60,这是因为mobile net 的 参数减小和计算率减小影响了准确率。

  • 这里的训练我们都使用的是一万次训练,真正的神经网络训练远不止于此,可能会达到100万次的规模



作者:Meet相识_bfa5
链接:https://www.jianshu.com/p/5a5bd59116fc


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