CODEWITHSUNDEEP
pythontensorflowkeras
hirschme
hirschme

Reputation: 894

How to fine-tune a functional model in Keras?

Taking a pre-trained model in Keras and replacing the top classification layer to retrain the network to a new task has several examples using a Sequential model in Keras. A sequential model has methods model.pop() and model.add() which make this fairly easy.

However, how is this achieved when using a functional model? This framework does not have method model.add().

How can I load a pretrained functional model in Keras, crop the last layer and replace it with a new one?

Current approach so far:

model.load_weights('/my_model_weights.h5')

def pop_layer(model):
    if not model.outputs:
    raise Exception('Sequential model cannot be popped: model is empty.')

    model.layers.pop()
    if not model.layers:
        model.outputs = []
        model.inbound_nodes = []
        model.outbound_nodes = []
    else:
        model.layers[-1].outbound_nodes = []
        model.outputs = [model.layers[-1].output]
    model.built = False

# Remove last layer with custom function (from another post)
pop_layer(model)

# Now add a new layer to the model ???

model.add(Dense(2, activation='softmax', name='fc2'))
model.compile(loss='sparse_categorical_crossentropy', optimizer='sgd',
              metrics=['accuracy'])

AttributeError: 'Model' object has no attribute 'add'

Upvotes: 2

Views: 1229

Answers (1)

giser_yugang
giser_yugang

Reputation: 6176

You can use a pretrained functional model with the last layer removed as a layer. You may think of a model as a "bigger layer". Then redefine a new model that wraps "bigger layer" and a new layer.

An example:

import tensorflow as tf
from keras.layers import Dense,Input
from keras.models import Sequential,Model

input_tensor = Input(shape=(64,))
x = Dense(32, activation='relu')(input_tensor)
x = Dense(32, activation='relu')(x)
output_tensor = Dense(10, activation=tf.nn.softmax)(x)
model = Model(input_tensor, output_tensor)
model.compile(loss='sparse_categorical_crossentropy', optimizer='sgd',
              metrics=['accuracy'])
print(model.summary())
model.save_weights('my_model_weights.h5')
# 
model.load_weights('my_model_weights.h5')

def pop_layer(model):
    if not model.outputs:
        raise Exception('Sequential model cannot be popped: model is empty.')
    model.layers.pop()
    if not model.layers:
        model.outputs = []
        model.inbound_nodes = []
        model.outbound_nodes = []
    else:
        model.layers[-1].outbound_nodes = []
        model.outputs = [model.layers[-1].output]
    return model

# Remove last layer with custom function (from another post)
model_old = pop_layer(model)
# Now add a new layer to the model
model_new = Sequential()
model_new.add(model_old)
model_new.add(Dense(2, activation=tf.nn.softmax, name='fc2'))
model_new.compile(loss='sparse_categorical_crossentropy', optimizer='sgd',
              metrics=['accuracy'])
print(model_new.summary())

As a result, you can see that the parameters of the last layer of pretrained functional model are missing.

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 64)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 32)                2080      
_________________________________________________________________
dense_2 (Dense)              (None, 32)                1056      
_________________________________________________________________
dense_3 (Dense)              (None, 10)                330       
=================================================================
Total params: 3,466
Trainable params: 3,466
Non-trainable params: 0
_________________________________________________________________
None

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
model_1 (Model)              multiple                  3136      
_________________________________________________________________
fc2 (Dense)                  (None, 2)                 66        
=================================================================
Total params: 3,202
Trainable params: 3,202
Non-trainable params: 0
_________________________________________________________________
None

Upvotes: 2

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