python神經(jīng)網(wǎng)絡Densenet模型復現(xiàn)詳解

更新時間：2022年05月07日 12:15:19 作者：Bubbliiiing

這篇文章主要為大家介紹了python神經(jīng)網(wǎng)絡Densenet模型復現(xiàn)詳解，有需要的朋友可以借鑒參考下，希望能夠有所幫助，祝大家多多進步，早日升職加薪

什么是Densenet

據(jù)說Densenet比Resnet還要厲害，我決定好好學一下。

ResNet模型的出現(xiàn)使得深度學習神經(jīng)網(wǎng)絡可以變得更深，進而實現(xiàn)了更高的準確度。

ResNet模型的核心是通過建立前面層與后面層之間的短路連接（shortcuts），這有助于訓練過程中梯度的反向傳播，從而能訓練出更深的CNN網(wǎng)絡。

DenseNet模型，它的基本思路與ResNet一致，也是建立前面層與后面層的短路連接，不同的是，但是它建立的是前面所有層與后面層的密集連接。

DenseNet還有一個特點是實現(xiàn)了特征重用。

這些特點讓DenseNet在參數(shù)和計算成本更少的情形下實現(xiàn)比ResNet更優(yōu)的性能。

DenseNet示意圖如下：

代碼下載

Densenet

1、Densenet的整體結(jié)構(gòu)

如圖所示Densenet由DenseBlock和中間的間隔模塊Transition Layer組成。

1、DenseBlock：DenseBlock指的就是DenseNet特有的模塊，如下圖所示，前面所有層與后面層的具有密集連接，在同一個DenseBlock當中，特征層的高寬不會發(fā)生改變，但是通道數(shù)會發(fā)生改變。

2、Transition Layer：Transition Layer是將不同DenseBlock之間進行連接的模塊，主要功能是整合上一個DenseBlock獲得的特征，并且縮小上一個DenseBlock的寬高，在Transition Layer中，一般會使用一個步長為2的AveragePooling2D縮小特征層的寬高。

2、DenseBlock

DenseBlock的實現(xiàn)示意圖如圖所示：

以前獲得的特征會在保留后不斷的堆疊起來。

以一個簡單例子來表現(xiàn)一下具體的DenseBlock的流程：

假設(shè)輸入特征層為X0。

1、對x0進行一次1x1卷積調(diào)整通道數(shù)到4*32后，再利用3x3卷積獲得一個32通道的特征層，此時會獲得一個shape為(h,w,32)的特征層x1。

2、將獲得的x1和初始的x0堆疊，獲得一個新的特征層，這個特征層會同時保留初始x0的特征也會保留經(jīng)過卷積處理后的特征。

3、反復經(jīng)過步驟1、2的處理，原始的特征會一直得到保留，經(jīng)過卷積處理后的特征也會得到保留。當網(wǎng)絡程度不斷加深，就可以實現(xiàn)前面所有層與后面層的具有密集連接。

實現(xiàn)代碼為：

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將不同DenseBlock之間進行連接的模塊，主要功能是整合上一個DenseBlock獲得的特征，并且縮小上一個DenseBlock的寬高，在Transition Layer中，一般會使用一個步長為2的AveragePooling2D縮小特征層的寬高。

實現(xiàn)代碼為：

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

網(wǎng)絡實現(xiàn)代碼

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))

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