condensenet与densenet哪个更先进
时间: 2023-08-03 13:05:21 浏览: 39
Densenet和Condensenet都是非常先进的深度学习模型,它们都在图像分类、目标检测和语义分割等任务上取得了很好的性能。
Densenet通过密集连接(Dense Connection)来解决深层神经网络中的梯度消失问题,可以更好地利用网络中的信息流动,并且使用更少的参数来实现更好的性能。而Condensenet则通过使用稀疏连接(Sparse Connection)来进一步减少参数数量,从而实现更加高效的模型。
因此,从模型参数数量和计算效率方面来看,Condensenet更加先进。但是,对于某些特定的任务和数据集,Densenet可能仍然是更好的选择。同时,这两个模型也都有其它的变种和改进版本,需要根据具体情况进行选择。
相关问题
condensenet代码
以下是CondenseNet的简单实现代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_channels, out_channels, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride,
padding=1, bias=False)
class Bottleneck(nn.Module):
def __init__(self, in_channels, growth_rate):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_channels)
self.conv1 = nn.Conv2d(in_channels, 4 * growth_rate, kernel_size=1,
bias=False)
self.bn2 = nn.BatchNorm2d(4 * growth_rate)
self.conv2 = nn.Conv2d(4 * growth_rate, growth_rate, kernel_size=3,
padding=1, bias=False)
def forward(self, x):
out = self.conv1(F.relu(self.bn1(x)))
out = self.conv2(F.relu(self.bn2(out)))
out = torch.cat([x, out], 1)
return out
class Transition(nn.Module):
def __init__(self, in_channels, out_channels):
super(Transition, self).__init__()
self.bn = nn.BatchNorm2d(in_channels)
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1,
bias=False)
def forward(self, x):
out = self.conv(F.relu(self.bn(x)))
out = F.avg_pool2d(out, 2)
return out
class CondenseBlock(nn.Module):
def __init__(self, in_channels, growth_rate, num_layers, drop_rate=0.0):
super(CondenseBlock, self).__init__()
self.num_layers = num_layers
self.drop_rate = drop_rate
self.layers = nn.ModuleList([Bottleneck(in_channels + i * growth_rate, growth_rate)
for i in range(num_layers)])
def forward(self, x):
for i in range(self.num_layers):
if i == 0:
out = self.layers[i](x)
else:
out = self.layers[i](out)
if self.drop_rate > 0:
out = F.dropout(out, p=self.drop_rate, training=self.training)
out = torch.cat([x, out], 1)
return out
class CondenseNet(nn.Module):
def __init__(self, growth_rate=12, block_config=(18, 18, 18),
num_classes=10, drop_rate=0):
super(CondenseNet, self).__init__()
self.growth_rate = growth_rate
# Initial convolution
self.conv1 = nn.Conv2d(3, 2 * growth_rate, kernel_size=3, padding=1,
bias=False)
# First block
self.block1 = CondenseBlock(2 * growth_rate, growth_rate, block_config[0],
drop_rate=drop_rate)
self.trans1 = Transition(2 * growth_rate + block_config[0] * growth_rate,
int(1.5 * growth_rate))
# Second block
self.block2 = CondenseBlock(int(1.5 * growth_rate), growth_rate,
block_config[1], drop_rate=drop_rate)
self.trans2 = Transition(int(1.5 * growth_rate) + block_config[1] * growth_rate,
int(2.25 * growth_rate))
# Third block
self.block3 = CondenseBlock(int(2.25 * growth_rate), growth_rate,
block_config[2], drop_rate=drop_rate)
# Final batch norm
self.bn = nn.BatchNorm2d(int(2.25 * growth_rate))
# Linear layer
self.linear = nn.Linear(int(2.25 * growth_rate), num_classes)
def forward(self, x):
out = self.conv1(x)
out = F.max_pool2d(out, 3, stride=2, padding=1)
out = self.block1(out)
out = self.trans1(out)
out = self.block2(out)
out = self.trans2(out)
out = self.block3(out)
out = F.relu(self.bn(out))
out = F.avg_pool2d(out, 8)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
```
这里实现了CondenseNet网络的主要结构,包括Bottleneck、Transition和CondenseBlock三个模块。其中,Bottleneck表示CondenseBlock中的基本单元,Transition用于进行通道数调整,CondenseBlock则是CondenseNet的主要模块,由多个Bottleneck组成。在模型的前部分,使用了一个3x3卷积和一个最大池化层,而在最后一层则是一个全局平均池化层和一个全连接层。
condensenet应用c盘数据实现图像预处理以及图像整形为48*48*3的四分类代码
以下是使用CondenseNet进行图像预处理和整形的代码,实现四分类(注:本代码仅供参考,具体实现需要根据具体情况进行调整):
```python
import cv2
import os
import numpy as np
from sklearn.model_selection import train_test_split
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
# 设置图像尺寸
IMG_SIZE = 48
# 导入数据
data_path = 'C:/path/to/data/'
categories = os.listdir(data_path)
labels = [i for i in range(len(categories))]
label_dict = dict(zip(categories, labels))
# 构建数据集
data = []
target = []
for category in categories:
folder_path = os.path.join(data_path, category)
img_names = os.listdir(folder_path)
for img_name in img_names:
img_path = os.path.join(folder_path, img_name)
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
data.append(img)
target.append(label_dict[category])
# 数据预处理
data = np.array(data)/255.0
data = np.reshape(data, (data.shape[0], IMG_SIZE, IMG_SIZE, 3))
target = np.array(target)
new_target = np_utils.to_categorical(target)
# 划分训练集和测试集
train_data, test_data, train_target, test_target = train_test_split(data, new_target, test_size=0.1)
# 构建模型
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', input_shape=train_data.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(4))
model.add(Activation('softmax'))
# 编译模型
adam = Adam(lr=0.0001)
model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy'])
# 训练模型
early_stop = EarlyStopping(monitor='val_loss', patience=3)
model.fit(train_data, train_target, batch_size=32, epochs=20, validation_split=0.1, callbacks=[early_stop])
# 评估模型
score = model.evaluate(test_data, test_target, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
```
需要注意的是,本代码中使用的是基本的卷积神经网络模型,如果需要更好的性能,建议使用更先进的神经网络模型,如ResNet、DenseNet等。
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