def __init__(self,block,block_num,num_classes=1000): super(ResNet,self).__init__() self.in_channel = 64 # conv1的输出维度 #第0层的卷积模块 self.conv1=nn.Conv2d(in_channels=3,out_channels=self.in_channel,kernel_size=7,stride=2, padding=3, bias=False) self.bn1=nn.BatchNorm2d...
def __init__(self, block, layers, num_classes=1000): "使用加深的ResNet,在最开始的地方增加了几个卷积层,提取更多浅层特征" self.inplanes = 128 super(ResNet, self).__init__() self.conv1 = conv3x3(3, 64, stride=2) self.bn1 = BatchNorm2d(64) self.relu1 = nn.ReLU(inplace=True...
In [1] !unzip -q -o data/data195256/RAF-DB.zip #解压数据集 In [2] #各参数定义,包含优化器、学习率、标签、模型保存路径等 text = '''__all__ = ['CONFIG', 'get'] CONFIG = { 'model_save_dir': "./output/ResNet50", 'num_classes': 7, 'total_images': 15339, 'epochs': 50...
class ResNet(nn.Module): #初始化网络结构和参数 def __init__(self,block,layers,num_classes=1000): #self.inplane为当前的fm的通道数 self.inplane=64 super(ResNet,self).__init__() #参数 self.block=block self.layers=layers #stem的网络层 self.conv1=nn.Conv2d(3,self.inplane,kernel_size...
python深色版本 import torch import torch.nn as nn import torchvision.models as models class PlantClassifier(nn.Module): def __init__(self, num_classes=1081): super(PlantClassifier, self).__init__() self.base_model = models.resnet50(pretrained=True) self.base_model.fc = nn.Linear(self....
utils.to_categorical(1, num_classes=2) test_data.append(img) test_labels.append(label) test_data = np.array(test_data) test_labels = np.array(test_labels) test_loss_resnet50, test_score_resnet50 = resnet50model_new.evaluate(test_data, test_labels, batch_size=32) print("Loss on ...
1)ResNet系列网络 2)ResNet50结构 3)残差区块 4)ResNet其他版本 In [12] network = paddle.vision.models.resnet50(num_classes=get('num_classes'), pretrained=True) #pretrained=True使用别人已经训练好的预训练模型进行训练网络 2021-05-12 18:51:46,337 - INFO - unique_endpoints {''} 2021-05...
out_channels=num_classes)defconstruct(self, x):x = self.conv1(x) x = self.norm(x)...
class ResNet50(nn.Module):def __init__(self, num_classes=1000):# ... 前几层代码 ...# 4个残差块的block1self.layer1 = self._make_layer(ResidualBlock, 64, 3, stride=1)# 4个残差块的block2self.layer2 = self._make_layer(ResidualBlock, 128, 4, stride=2)# 4个残差块的block3self...
predictions = Dense(train_generator.num_classes, activation=’softmax’)(x) 构建模型 model = Model(inputs=base_model.input, outputs=predictions) 冻结预训练模型的权重 for layer in base_model.layers: layer.trainable = False 编译模型 model.compile(optimizer=Adam(lr=0.0001), loss=’categorical_...