Add new nodes to one of the output layers in a Keras model-CodePudding

I have a custom ResNet model that I define through the Keras Functional API. Also my model has multiple outputs. The last element of the output array is the fully connected dense layer with num_class nodes. I want to be able to increment the number of nodes of this layer. This is the relevant code for the creation of my network:

from tensorflow.keras import layers, models, Input, regularizers

res = []
inputs = Input(shape=(height, width, channels), name='data')
x = MyLayer()(inputs)
# ... other layers
x = MyLayer()(x)
res.append(x)
# ... other layers
x = layers.Dense(num_class, name='fc1', use_bias=True)(x)
res.append(x)
model = models.Model(inputs=inputs, outputs=[res[-2], res[-3], res[-4], res[-1]])

At the question Adding new nodes to output layer in Keras I found an answer similar to what I'm searching for that I'm adding here below:

def add_outputs(self, n_new_outputs):
    #Increment the number of outputs
    self.n_outputs  = n_new_outputs
    weights = self.model.get_layer('fc8').get_weights()
    #Adding new weights, weights will be 0 and the connections random
    shape = weights[0].shape[0]
    weights[1] = np.concatenate((weights[1], np.zeros(n_new_outputs)), axis=0)
    weights[0] = np.concatenate((weights[0], -0.0001 * np.random.random_sample((shape, n_new_outputs))   0.0001), axis=1)
    #Deleting the old output layer
    self.model.layers.pop()
    last_layer = self.model.get_layer('batchnormalization_1').output
    #New output layer
    out = Dense(self.n_outputs, activation='softmax', name='fc8')(last_layer)
    self.model = Model(input=self.model.input, output=out)
    #set weights to the layer
    self.model.get_layer('fc8').set_weights(weights)
    print(weights[0])

However in this question there was only one layer as output and I'm not sure how to replicate the same with my architecture.

CodePudding user response：

This is the solution I've come up with. I assigned the layers that I wanted to keep as output to variables:

from tensorflow.keras import layers, models, Input, regularizers

inputs = Input(shape=(height, width, channels), name='data')
x = MyLayer()(inputs)
# ... other layers
a = MyLayer(name="a")(x)
b = MyLayer(name="b")(a)
c = MyLayer(name="c")(b)
x = layers.Dense(num_class, name='fc1', use_bias=True)(c)
model = models.Model(inputs=inputs, outputs=[c, b, a, x])

And then in order to increase the number of nodes in the last layer just call the function increment_classes. The total number of nodes will be the sum of old_num_class and num_class.

def increment_classes(model, old_num_class, num_class):
    weights = model.get_layer("fc1").get_weights() 
    new_num_class = old_num_class   num_class

    # Adding new weights, weights will be 0 and the connections random
    shape = weights[0].shape[0]
    weights[1] = np.concatenate((weights[1], np.zeros(num_class)), axis=0)
    weights[0] = np.concatenate((weights[0], -0.0001 * np.random.random_sample((shape, num_class))   0.0001), axis=1)

    # Deleting the old dense output layer
    model.layers.pop()

    # get the output layers
    a = model.get_layer("a").output
    b = model.get_layer("b").output
    c = model.get_layer('c').output

    # Replace dense output layer (x)
    out = layers.Dense(new_num_class, name='fc1', use_bias=True)(c)

    model = models.Model(inputs=model.input, outputs=[c, b, a, out]) 

    # set weights to the layer
    model.get_layer('fc1').set_weights(weights)
    return model