[英]Customising transfer learning model tensorflow-Keras
我正在尝试将添加卷积层添加到下面提到的迁移学习代码中。 但不确定如何进行。 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
。 让我知道这是否可能,如果可以,如何?
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()
您可以通过多种方式进行操作,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(...)
我想这就是你所追求的
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()
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