目录

什么是Densenet代码下载Densenet1、Densenet的整体结构2、DenseBlock3、Transition Layer 网络实现代码

什么是Densenet

ResNet模型的出现使得深度学习神经网络可以变得更深，进而实现了更高的准确度。ResNet模型的核心是通过建立前面层与后面层之间的短路连接（shortcuts），这有助于训练过程中梯度的反向传播，从而能训练出更深的CNN网络。

DenseNet模型，它的基本思路与ResNet一致，也是建立前面层与后面层的短路连接，不同的是，但是它建立的是前面所有层与后面层的密集连接。

DenseNet还有一个特点是实现了特征重用。

这些特点让DenseNet在参数和计算成本更少的情形下实现比ResNet更优的性能。

DenseNet示意图如下：

代码下载

https://github.com/bubbliiiing/classic-convolution-network

Densenet

1、Densenet的整体结构

如图所示Densenet由DenseBlock和中间的间隔模块Transition Layer组成。 1、DenseBlock：DenseBlock指的就是DenseNet特有的模块，如下图所示，前面所有层与后面层的具有密集连接，在同一个DenseBlock当中，特征层的高宽不会发生改变，但是通道数会发生改变。

2、Transition Layer：Transition Layer是将不同DenseBlock之间进行连接的模块，主要功能是整合上一个DenseBlock获得的特征，并且缩小上一个DenseBlock的宽高，在Transition Layer中，一般会使用一个步长为2的AveragePooling2D缩小特征层的宽高。

2、DenseBlock

DenseBlock的实现示意图如图所示：

以前获得的特征会在保留后不断的堆叠起来。

以一个简单例子来表现一下具体的DenseBlock的流程： 假设输入特征层为X0 1、对x0进行一次1x1卷积调整通道数到4*32后，再利用3x3卷积获得一个32通道的特征层，此时会获得一个shape为(h,w,32)的特征层x1。 2、将获得的x1和初始的x0堆叠，获得一个新的特征层，这个特征层会同时保留初始x0的特征也会保留经过卷积处理后的特征。 3、反复经过步骤1、2的处理，原始的特征会一直得到保留，经过卷积处理后的特征也会得到保留。当网络程度不断加深，就可以实现前面所有层与后面层的具有密集连接。

实现代码为：

def dense_block(x, blocks, name): for i in range(blocks): x = conv_block(x, 32, name=name + '_block' + str(i + 1)) return x def conv_block(x, growth_rate, name): bn_axis = 3 x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(x) x1 = layers.Activation('relu', name=name + '_0_relu')(x1) x1 = layers.Conv2D(4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(x1) x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x1) x1 = layers.Activation('relu', name=name + '_1_relu')(x1) x1 = layers.Conv2D(growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')(x1) x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1]) return x

3、Transition Layer

Transition Layer将不同DenseBlock之间进行连接的模块，主要功能是整合上一个DenseBlock获得的特征，并且缩小上一个DenseBlock的宽高，在Transition Layer中，一般会使用一个步长为2的AveragePooling2D缩小特征层的宽高。 实现代码为：

def transition_block(x, reduction, name): bn_axis = 3 x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(x) x = layers.Activation('relu', name=name + '_relu')(x) x = layers.Conv2D(int(backend.int_shape(x)[bn_axis] * reduction), 1, use_bias=False, name=name + '_conv')(x) x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x) return x

网络实现代码

from keras.preprocessing import image from keras.models import Model from keras import layers from keras.applications import imagenet_utils from keras.applications.imagenet_utils import decode_predictions from keras.utils.data_utils import get_file from keras import backend import numpy as np BASE_WEIGTHS_PATH = ( 'https://github.com/keras-team/keras-applications/' 'releases/download/densenet/') DENSENET121_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet121_weights_tf_dim_ordering_tf_kernels.h5') DENSENET169_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet169_weights_tf_dim_ordering_tf_kernels.h5') DENSENET201_WEIGHT_PATH = ( BASE_WEIGTHS_PATH + 'densenet201_weights_tf_dim_ordering_tf_kernels.h5') def dense_block(x, blocks, name): for i in range(blocks): x = conv_block(x, 32, name=name + '_block' + str(i + 1)) return x def conv_block(x, growth_rate, name): bn_axis = 3 x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_0_bn')(x) x1 = layers.Activation('relu', name=name + '_0_relu')(x1) x1 = layers.Conv2D(4 * growth_rate, 1, use_bias=False, name=name + '_1_conv')(x1) x1 = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_1_bn')(x1) x1 = layers.Activation('relu', name=name + '_1_relu')(x1) x1 = layers.Conv2D(growth_rate, 3, padding='same', use_bias=False, name=name + '_2_conv')(x1) x = layers.Concatenate(axis=bn_axis, name=name + '_concat')([x, x1]) return x def transition_block(x, reduction, name): bn_axis = 3 x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name=name + '_bn')(x) x = layers.Activation('relu', name=name + '_relu')(x) x = layers.Conv2D(int(backend.int_shape(x)[bn_axis] * reduction), 1, use_bias=False, name=name + '_conv')(x) x = layers.AveragePooling2D(2, strides=2, name=name + '_pool')(x) return x def DenseNet(blocks, input_shape=None, classes=1000, **kwargs): img_input = layers.Input(shape=input_shape) bn_axis = 3 # 224,224,3 -> 112,112,64 x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input) x = layers.Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x) x = layers.BatchNormalization( axis=bn_axis, epsilon=1.001e-5, name='conv1/bn')(x) x = layers.Activation('relu', name='conv1/relu')(x) # 112,112,64 -> 56,56,64 x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x) x = layers.MaxPooling2D(3, strides=2, name='pool1')(x) # 56,56,64 -> 56,56,64+32*block[0] # Densenet121 56,56,64 -> 56,56,64+32*6 == 56,56,256 x = dense_block(x, blocks[0], name='conv2') # 56,56,64+32*block[0] -> 28,28,32+16*block[0] # Densenet121 56,56,256 -> 28,28,32+16*6 == 28,28,128 x = transition_block(x, 0.5, name='pool2') # 28,28,32+16*block[0] -> 28,28,32+16*block[0]+32*block[1] # Densenet121 28,28,128 -> 28,28,128+32*12 == 28,28,512 x = dense_block(x, blocks[1], name='conv3') # Densenet121 28,28,512 -> 14,14,256 x = transition_block(x, 0.5, name='pool3') # Densenet121 14,14,256 -> 14,14,256+32*block[2] == 14,14,1024 x = dense_block(x, blocks[2], name='conv4') # Densenet121 14,14,1024 -> 7,7,512 x = transition_block(x, 0.5, name='pool4') # Densenet121 7,7,512 -> 7,7,256+32*block[3] == 7,7,1024 x = dense_block(x, blocks[3], name='conv5') x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x) x = layers.Activation('relu', name='relu')(x) x = layers.GlobalAveragePooling2D(name='avg_pool')(x) x = layers.Dense(classes, activation='softmax', name='fc1000')(x) inputs = img_input if blocks == [6, 12, 24, 16]: model = Model(inputs, x, name='densenet121') elif blocks == [6, 12, 32, 32]: model = Model(inputs, x, name='densenet169') elif blocks == [6, 12, 48, 32]: model = Model(inputs, x, name='densenet201') else: model = Model(inputs, x, name='densenet') return model def DenseNet121(input_shape=[224,224,3], classes=1000, **kwargs): return DenseNet([6, 12, 24, 16], input_shape, classes, **kwargs) def DenseNet169(input_shape=[224,224,3], classes=1000, **kwargs): return DenseNet([6, 12, 32, 32], input_shape, classes, **kwargs) def DenseNet201(input_shape=[224,224,3], classes=1000, **kwargs): return DenseNet([6, 12, 48, 32], input_shape, classes, **kwargs) def preprocess_input(x): x /= 255. mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] x[..., 0] -= mean[0] x[..., 1] -= mean[1] x[..., 2] -= mean[2] if std is not None: x[..., 0] /= std[0] x[..., 1] /= std[1] x[..., 2] /= std[2] return x if __name__ == '__main__': # model = DenseNet121() # weights_path = get_file( # 'densenet121_weights_tf_dim_ordering_tf_kernels.h5', # DENSENET121_WEIGHT_PATH, # cache_subdir='models', # file_hash='9d60b8095a5708f2dcce2bca79d332c7') model = DenseNet169() weights_path = get_file( 'densenet169_weights_tf_dim_ordering_tf_kernels.h5', DENSENET169_WEIGHT_PATH, cache_subdir='models', file_hash='d699b8f76981ab1b30698df4c175e90b') # model = DenseNet201() # weights_path = get_file( # 'densenet201_weights_tf_dim_ordering_tf_kernels.h5', # DENSENET201_WEIGHT_PATH, # cache_subdir='models', # file_hash='1ceb130c1ea1b78c3bf6114dbdfd8807') model.load_weights(weights_path) model.summary() img_path = 'elephant.jpg' img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) print('Input image shape:', x.shape) preds = model.predict(x) print(np.argmax(preds)) print('Predicted:', decode_predictions(preds))