什么是Densenet
据说Densenet比Resnet还要厉害，我决定好好学一下。
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
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))
以上就是python神经网络Densenet模型复现详解的详细内容，更多关于Densenet模型复现的资料请关注服务器之家其它相关文章！
原文地址：https://blog.csdn.net/weixin_44791964/article/details/105472196
