问对具有比预训练分类器更少的目标标签的数据的迁移学习

EN

#Load pretrained vgg16 network
from torchvision.models import vgg16

num_classes = 3
pretrained_model = vgg16(pretrained=True)
pretrained_model.eval()
pretrained_model.to(device)

#Extracting the first part of the model
feature_extractor = pretrained_model.features

#Define feature classifier
feature_classifier = nn.Sequential(
nn.Linear(4*4*512,256),
nn.ReLU(),
nn.Linear(256, num_classes))

#
model = nn.Sequential(
feature_extractor,
nn.Flatten(),
feature_classifier)

import tensorflow as tf
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.models import Sequential
from tensorflow.python.keras.layers import Dense

num_classes= 3

# Include the path of the weights for the pretrained model
resnet_weights_path='imagenet'

# Create your model
model= Sequential()

# Include the pre-trained model. In this case, ResNet50
model.add(ResNet50(include_top=False,pooling='avg',weights=resnet_weights_path ))

# Add as many extra layers as you need, according to you problem
# You can also try it directly

# Add the final layer that makes predictions. Suit yourself with the activation function 
model.add(Dense(num_classes,activation='softmax'))

# Don't train the pre-trained model
model.layers[0].trainable=False

# Compile your model according to your needs
model.compile(optimizer='sgd',loss='categorical_crossentropy',metrics=['accuracy'])