基于tensorflow搭建densenet

DenseNet是一种非常有效的卷积神经网络架构，它具有非常高的参数利用率和良好的训练性能。在这里，我们将使用TensorFlow来构建一个DenseNet。

首先，我们需要导入所需的库：

import tensorflow as tf
from tensorflow.keras.layers import Dense, Conv2D, BatchNormalization, Activation, Concatenate, AvgPool2D, Flatten

接下来，我们可以定义DenseNet的各个组件。我们将使用DenseBlock和TransitionBlock两种类型的块来构建DenseNet。DenseBlock将由若干个卷积层组成，每个卷积层都会接受前面所有卷积层的输出作为输入，并将它们连接在一起。TransitionBlock用于将特征图的尺寸减半，并减少通道数，以便在下一层中使用更少的内存和计算资源。

class DenseBlock(tf.keras.layers.Layer):
    def __init__(self, num_layers, growth_rate):
        super(DenseBlock, self).__init__()
        self.num_layers = num_layers
        self.growth_rate = growth_rate

    def build(self, input_shape):
        self.conv_layers = []
        for i in range(self.num_layers):
            self.conv_layers.append(Conv2D(filters=self.growth_rate, kernel_size=(3, 3), padding='same'))
        self.batch_norm = BatchNormalization()

    def call(self, inputs):
        x = inputs
        for i in range(self.num_layers):
            y = self.conv_layers[i](x)
            x = Concatenate()([x, y])
        x = self.batch_norm(x)
        return x


class TransitionBlock(tf.keras.layers.Layer):
    def __init__(self, num_output_channels):
        super(TransitionBlock, self).__init__()
        self.num_output_channels = num_output_channels

    def build(self, input_shape):
        self.conv_layer = Conv2D(filters=self.num_output_channels, kernel_size=(1, 1), padding='same')
        self.avg_pool = AvgPool2D(pool_size=(2, 2), strides=(2, 2))
        self.batch_norm = BatchNormalization()

    def call(self, inputs):
        x = self.conv_layer(inputs)
        x = self.avg_pool(x)
        x = self.batch_norm(x)
        return x

现在我们可以定义整个DenseNet模型了。我们将使用Sequential模型，并在其中添加DenseBlock和TransitionBlock。我们还将添加一些最终的卷积层和全连接层来生成输出。

def make_densenet(input_shape, num_classes, num_blocks=3, growth_rate=12, num_filters=16):
    model = tf.keras.Sequential()

    # initial convolution layer
    model.add(Conv2D(filters=num_filters, kernel_size=(7, 7), strides=(2, 2), padding='same', input_shape=input_shape))
    model.add(BatchNormalization())
    model.add(Activation('relu'))
    model.add(MaxPool2D(pool_size=(3, 3), strides=(2, 2), padding='same'))

    # dense blocks and transition blocks
    for i in range(num_blocks):
        model.add(DenseBlock(num_layers=4, growth_rate=growth_rate))
        num_filters += 4 * growth_rate
        if i != num_blocks - 1:
            model.add(TransitionBlock(num_filters // 2))
            num_filters = num_filters // 2

    # final layers
    model.add(Flatten())
    model.add(Dense(units=num_classes, activation='softmax'))

    return model

现在我们可以使用上面的代码来创建一个DenseNet模型。以下是一个示例：

input_shape = (224, 224, 3)
num_classes = 10
model = make_densenet(input_shape=input_shape, num_classes=num_classes)
model.summary()

这将创建一个具有10个类的DenseNet模型，并打印出模型的摘要。现在你可以根据需要调整模型的参数或添加其他层。