CIFAR-10数据集上RESNET-20的LSQ量化模型代码怎么写
时间: 2023-03-13 18:17:52 浏览: 87
CIFAR-10数据集上的RESNET-20的LSQ量化模型代码的编写有一些步骤,首先需要定义模型,然后实例化数据加载器,然后对模型进行量化,最后训练模型。编写代码时,请参考官方文档,以确保代码的准确性和可执行性。
相关问题
帮我写一个resnet50的训练代码,cifar-10数据集
好的,以下是一个使用ResNet50模型在CIFAR-10数据集上进行训练的示例代码,你可以在此基础上进行修改和调整:
```
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, GlobalAveragePooling2D, Add, Dense, Activation, BatchNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.regularizers import l2
# 加载数据集
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# 数据预处理
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)
# 数据增强
datagen_train = ImageDataGenerator(
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True
)
datagen_train.fit(x_train)
# 定义ResNet50模型
def resnet_block(inputs, filters, strides=1):
x = Conv2D(filters, kernel_size=3, strides=strides, padding='same', kernel_regularizer=l2(1e-4))(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(filters, kernel_size=3, strides=1, padding='same', kernel_regularizer=l2(1e-4))(x)
x = BatchNormalization()(x)
if strides != 1 or inputs.shape[3] != filters:
inputs = Conv2D(filters, kernel_size=1, strides=strides, padding='same', kernel_regularizer=l2(1e-4))(inputs)
inputs = BatchNormalization()(inputs)
x = Add()([inputs, x])
x = Activation('relu')(x)
return x
inputs = Input(shape=(32, 32, 3))
x = Conv2D(64, kernel_size=3, strides=1, padding='same', kernel_regularizer=l2(1e-4))(inputs)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = resnet_block(x, 64)
x = resnet_block(x, 64)
x = resnet_block(x, 128, strides=2)
x = resnet_block(x, 128)
x = resnet_block(x, 256, strides=2)
x = resnet_block(x, 256)
x = GlobalAveragePooling2D()(x)
outputs = Dense(10, activation='softmax')(x)
model = Model(inputs=inputs, outputs=outputs)
# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# 训练模型
model.fit(datagen_train.flow(x_train, y_train, batch_size=128),
steps_per_epoch=x_train.shape[0] // 128,
epochs=50,
validation_data=(x_test, y_test))
```
在上面的代码中,我们使用了ResNet50模型,并在模型的基础上构建了一个CIFAR-10分类器。我们还使用了数据增强来增加模型的鲁棒性,并使用了L2正则化来避免过拟合。最后,我们使用Adam优化器来训练模型,并在50个epoch后进行评估。
输出一段利用resnet提取cifar-10数据集图像特征的python代码
以下是一个利用ResNet提取CIFAR-10数据集图像特征的Python代码示例:
```python
import torch
import torch.nn as nn
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import numpy as np
# 定义ResNet模型
class ResNet(nn.Module):
def __init__(self):
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.layer1 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
)
self.layer2 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(128),
)
self.layer3 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(256),
)
self.layer4 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(512),
)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x) + x
x = self.layer2(x) + x
x = self.layer3(x) + x
x = self.layer4(x) + x
x = self.avgpool(x)
x = x.view(x.size(0), -1)
return x
# 加载CIFAR-10数据集
train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
std=[0.2023, 0.1994, 0.2010]),
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.4914, 0.4822, 0.4465],
std=[0.2023, 0.1994, 0.2010]),
])
train_dataset = datasets.CIFAR10(root='./data', train=True,
download=True, transform=train_transform)
test_dataset = datasets.CIFAR10(root='./data', train=False,
download=True, transform=test_transform)
# 定义ResNet模型
model = ResNet()
# 加载预训练模型
checkpoint = torch.load('resnet_cifar10.pth')
model.load_state_dict(checkpoint['model'])
# 提取训练集和测试集图像的特征
train_features = []
train_labels = []
test_features = []
test_labels = []
model.eval()
with torch.no_grad():
for images, labels in train_dataset:
features = model(images.unsqueeze(0))
train_features.append(features.numpy().tolist()[0])
train_labels.append(labels)
for images, labels in test_dataset:
features = model(images.unsqueeze(0))
test_features.append(features.numpy().tolist()[0])
test_labels.append(labels)
train_features = np.array(train_features)
train_labels = np.array(train_labels)
test_features = np.array(test_features)
test_labels = np.array(test_labels)
# 输出特征形状
print('Train features shape:', train_features.shape)
print('Train labels shape:', train_labels.shape)
print('Test features shape:', test_features.shape)
print('Test labels shape:', test_labels.shape)
```
在这个示例中,我们首先定义了一个ResNet模型,并在其上加载了一个预训练模型。然后,我们使用该模型来提取训练集和测试集图像的特征,并将这些特征保存为numpy数组。最后,我们输出特征的形状,以确保它们正确地提取和保存。