定制转移学习模型tensorflow-Keras

Question

我试图在下面提到的传输学习代码中添加conv层。但不知道该怎么做。我想在下面提到的迁移学习代码中添加conv, max-pooling, 3x3 filter and stride 3 and activation mode ReLU或conv, max-pooling, 3x3 filter and stride 3 and activation mode LReLU。告诉我是否有可能，如果有，怎么做？

 CLASSES = 2
 
# setup model
base_model = MobileNet(weights='imagenet', include_top=False)
 
x = base_model.output
x = GlobalAveragePooling2D(name='avg_pool')(x)
x = Dropout(0.4)(x)
predictions = Dense(CLASSES, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
 
# transfer learning
for layer in base_model.layers:
 layer.trainable = False
 
model.compile(optimizer='rmsprop',
 loss='categorical_crossentropy',
 metrics=['accuracy'])
 
"""##Data augmentation"""
 
# data prep

"""
## Transfer learning
"""
 
from tensorflow.keras.callbacks import ModelCheckpoint
filepath="mobilenet/my_model.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
 
EPOCHS = 1
BATCH_SIZE = 32
STEPS_PER_EPOCH = 5
VALIDATION_STEPS = 32
 
MODEL_FILE = 'mobilenet/filename.model'
 
history = model.fit_generator(
 train_generator,
 epochs=EPOCHS,
 steps_per_epoch=STEPS_PER_EPOCH,
 validation_data=validation_generator,
 validation_steps=VALIDATION_STEPS,
 callbacks=callbacks_list)
 
model.save(MODEL_FILE)
backup_model = model
model.summary()

Answer 1

你可以有几种方法，其中之一是：

model = Sequential([
    base_model,
    GlobalAveragePooling2D(name='avg_pool'),
    Dropout(0.4),
    Conv(...), # the layers you would like to add for the base model
    MaxPool(...),
    ...
])

model.compile(...)

Answer 2

我想这就是你想要的

CLASSES=2
new_filters=256 # specify the number of filter you want in the added convolutional layer
img_shape=(224,224,3)
base_model=tf.keras.applications.mobilenet.MobileNet( include_top=False, input_shape=img_shape,  weights='imagenet',dropout=.4) 
x=base_model.output
x= Conv2D(new_filters, 3, padding='same', strides= (3,3), activation='relu', name='added')(x)
x= GlobalAveragePooling2D(name='avg_pool')(x)
x= Dropout(0.4)(x)
predictions= Dense(CLASSES, activation='softmax', name='output')(x)

model=Model(inputs=base_model.input, outputs=predictions)
model.summary()