微调

热狗识别

获取数据集

import tensorflow as tf
import pathlib

traindir='hotdog/train'
testdir='hotdog/test'
image_gen=tf.keras.preprocessing.image.ImageDataGenerator(rescale=1/255)
train_data_gen=image_gen.flow_from_directory(traindir,batch_size=32,target_size=(224,224),shuffle=True)
test_data_gen=image_gen.flow_from_directory(testdir,batch_size=32,target_size=(224,224),shuffle=True)
image,label=next(train_data_gen)
plt.figure(figsize=(10,10))
for n in range(15):
    ax=plt.subplot(5,5,n+1)
    plt.imshow(image[n])
    plt.axis('off')

模型构建与训练

ResNet50=tf.keras.applications.ResNet50(weights='imagenet',input_shape=(224,224,3))
for layer in ResNet50.layers:
    layer.trainable=False
net=tf.keras.models.Sequential()
net.add(ResNet50)
net.add(tf.keras.layers.Flatten())
net.add(tf.keras.layers.Dense(2,activation='sigmoid'))

net.compile(optimizer='adam',loss=tf.keras.losses.binary_crossentropy,metrics=['accuarcy'])

net.fit(train_data_gen,steps_per_epoch=10,epochs=3,batch_size=30,validation_data=test_data_gen,validation_steps=10)