ResNet50 Model 没有在 keras 中使用迁移学习进行学习

Question

I am trying to perform transfer learning on ResNet50 model pretrained on Imag.net weights for PASCAL VOC 2012 dataset. As it is a multi label dataset, I am using sigmoid activation function in the final layer and binary_crossentropy loss. The metrics are precision,recall and accuracy . Below is the code I used to build the model for 20 classes (PASCAL VOC has 20 classes).

img_height,img_width = 128,128
num_classes = 20
#If imagenet weights are being loaded,
#input must have a static square shape (one of (128, 128), (160, 160), (192, 192), or (224, 224))
base_model = applications.resnet50.ResNet50(weights= 'imagenet', include_top=False, input_shape= (img_height,img_width,3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
#x = Dropout(0.7)(x)
predictions = Dense(num_classes, activation= 'sigmoid')(x)
model = Model(inputs = base_model.input, outputs = predictions)
for layer in model.layers[-2:]:
        layer.trainable=True
for layer in model.layers[:-3]:
        layer.trainable=False

adam = Adam(lr=0.0001)
model.compile(optimizer= adam, loss='binary_crossentropy', metrics=['accuracy',precision_m,recall_m])
#print(model.summary())

X_train, X_test, Y_train, Y_test = train_test_split(x_train, y, random_state=42, test_size=0.2)
savingcheckpoint = ModelCheckpoint('ResnetTL.h5',monitor='val_loss',verbose=1,save_best_only=True,mode='min')
earlystopcheckpoint = EarlyStopping(monitor='val_loss',patience=10,verbose=1,mode='min',restore_best_weights=True)
model.fit(X_train, Y_train, epochs=epochs, validation_data=(X_test,Y_test), batch_size=batch_size,callbacks=[savingcheckpoint,earlystopcheckpoint],shuffle=True)
model.save_weights('ResnetTLweights.h5')

It ran for 35 epochs until earlystopping and the metrics are as follows (without Dropout layer):

loss: 0.1195 - accuracy: 0.9551 - precision_m: 0.8200 - recall_m: 0.5420 - val_loss: 0.3535 - val_accuracy: 0.8358 - val_precision_m: 0.0583 - val_recall_m: 0.0757

Even with Dropout layer, I don't see much difference.

loss: 0.1584 - accuracy: 0.9428 - precision_m: 0.7212 - recall_m: 0.4333 - val_loss: 0.3508 - val_accuracy: 0.8783 - val_precision_m: 0.0595 - val_recall_m: 0.0403

With dropout, for a few epochs, the model is reaching to a validation precision and accuracy of 0.2 but not above that.

I see that precision and recall of validation set is pretty low compared to training set with and without dropout layer. How should I interpret this? Does this mean the model is overfitting. If so, what should I do? As of now the model predictions are quite random (totally incorrect). The dataset size is 11000 images.

Answer 1

Please can you modify code as below and try to execute

From:

predictions = Dense(num_classes, activation= 'sigmoid')(x)

To:

predictions = Dense(num_classes, activation= 'softmax')(x)

From:

model.compile(optimizer= adam, loss='binary_crossentropy', metrics=['accuracy',precision_m,recall_m])

To:

model.compile(optimizer= adam, loss='categorical_crossentropy', metrics=['accuracy',precision_m,recall_m])

Answer 2

This question is pretty old, but I'll answer it in case it is helpful to someone else:

In this example, you froze all layers except by the last two (Global Average Pooling and the last Dense one). There is a cleaner way to achieve the same state:

rn50 = applications.resnet50.ResNet50(weights='imagenet', include_top=False, 
input_shape=(img_height, img_width, 3))

x = rn50.output
x = GlobalAveragePooling2D()(x)
predictions = Dense(num_classes, activation= 'sigmoid')(x)
model = Model(inputs = base_model.input, outputs = predictions)

rn50.trainable = False  # <- this

model.compile(...)

In this case, features are being extracted from the ResNet50.network and fed to a linear softmax classifier, but the ResNet50's weights are not being trained. This is called feature extraction, not fine-tuning.

The only weights being trained are from your classifier, which was instantiated with weights drawn from a random distribution, and thus should be entirely trained. You should be using Adam with its default learning rate:

model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.001))

So you can train it for a few epochs, and, once it's done, then you unfreeze the backbone and "fine-tune" it:

backbone.trainable = False
model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.001))
model.fit(epochs=50)

backbone.trainable = True
model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.00001))
model.fit(epochs=60, initial_epoch=50)

There is a nice article about this on Keras website: https://keras.io/guides/transfer_learning/