Densenet-优快云博客

本文链接：https://blog.youkuaiyun.com/freedomUSTB/article/details/107161490

本文详细介绍了DenseNet神经网络在TensorFlow框架下的具体实现过程，包括密集连接块、过渡层等关键组件的设计与编码。通过深入探讨DenseNet的结构特点及其在图像分类任务中的应用，为读者提供了全面的技术指导。

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https://github.com/taki0112/Densenet-Tensorflow/

class DenseNet():
    def __init__(self, x, nb_blocks, filters, training):
        self.nb_blocks = nb_blocks
        self.filters = filters
        self.training = training
        self.model = self.Dense_net(x)


    def bottleneck_layer(self, x, scope):
        # print(x)
        with tf.name_scope(scope):
            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
            x = Relu(x)
            x = conv_layer(x, filter=4 * self.filters, kernel=[1,1], layer_name=scope+'_conv1')
            x = Drop_out(x, rate=dropout_rate, training=self.training)

            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch2')
            x = Relu(x)
            x = conv_layer(x, filter=self.filters, kernel=[3,3], layer_name=scope+'_conv2')
            x = Drop_out(x, rate=dropout_rate, training=self.training)

            # print(x)

            return x

    def transition_layer(self, x, scope):
        with tf.name_scope(scope):
            x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
            x = Relu(x)
            # x = conv_layer(x, filter=self.filters, kernel=[1,1], layer_name=scope+'_conv1')
            
            # https://github.com/taki0112/Densenet-Tensorflow/issues/10
            
            in_channel = x.shape[-1]
            x = conv_layer(x, filter=in_channel*0.5, kernel=[1,1], layer_name=scope+'_conv1')
            x = Drop_out(x, rate=dropout_rate, training=self.training)
            x = Average_pooling(x, pool_size=[2,2], stride=2)

            return x

    def dense_block(self, input_x, nb_layers, layer_name):
        with tf.name_scope(layer_name):
            layers_concat = list()
            layers_concat.append(input_x)

            x = self.bottleneck_layer(input_x, scope=layer_name + '_bottleN_' + str(0))

            layers_concat.append(x)

            for i in range(nb_layers - 1):
                x = Concatenation(layers_concat)
                x = self.bottleneck_layer(x, scope=layer_name + '_bottleN_' + str(i + 1))
                layers_concat.append(x)

            x = Concatenation(layers_concat)

            return x

    def Dense_net(self, input_x):
        x = conv_layer(input_x, filter=2 * self.filters, kernel=[7,7], stride=2, layer_name='conv0')
        # x = Max_Pooling(x, pool_size=[3,3], stride=2)

        """
        for i in range(self.nb_blocks) :
            # 6 -> 12 -> 48
            x = self.dense_block(input_x=x, nb_layers=4, layer_name='dense_'+str(i))
            x = self.transition_layer(x, scope='trans_'+str(i))
        """

        x = self.dense_block(input_x=x, nb_layers=6, layer_name='dense_1')
        x = self.transition_layer(x, scope='trans_1')

        x = self.dense_block(input_x=x, nb_layers=12, layer_name='dense_2')
        x = self.transition_layer(x, scope='trans_2')

        x = self.dense_block(input_x=x, nb_layers=48, layer_name='dense_3')
        x = self.transition_layer(x, scope='trans_3')

        x = self.dense_block(input_x=x, nb_layers=32, layer_name='dense_final')



        # 100 Layer
        x = Batch_Normalization(x, training=self.training, scope='linear_batch')
        x = Relu(x)
        x = Global_Average_Pooling(x)
        x = flatten(x)
        x = Linear(x)

        # x = tf.reshape(x, [-1, 10])
        return x