what is subclass tf.keras.layers.Layer instead of using a Lambda in this code?

Question

An example of replacing a lambda with a subclass in code is as follows:

scale = tf.Variable(1.)
scale_layer = tf.keras.layers.Lambda(lambda x: x * scale)

Because scale_layer does not directly track the scale variable, it will not appear in scale_layer.trainable_weights and will therefore not be trained if scale_layer is used in a Model. A better pattern is to write a subclassed Layer:

class ScaleLayer(tf.keras.layers.Layer):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.scale = tf.Variable(1.)

    def call(self, inputs):
        return inputs * self.scale

According to the instructions above, how should the lambda section in the last line of the code below be changed?

def grouped_convolution_block(inputs, num_filters, kernel_size, strides, cardinality): 
# Adds a grouped convolution block
    group_list = [] 
    grouped_channels = int(num_filters / cardinality)
    
    for c in range(cardinality):
        x = tf.keras.layers.Lambda(lambda z: z[:, :, c * grouped_channels:(c + 1) * grouped_channels])(inputs)

What is the subclass layer that replaces the last line (lambda part) of code?

Seven · Accepted Answer · 2024-12-07 06:51:54Z

0

Define the custom layer:

class GroupSliceLayer(tf.keras.layers.Layer):
    def __init__(self, start, end, **kwargs):
        super().__init__(**kwargs)
        self.start = start
        self.end = end

    def call(self, inputs):
        return inputs[:, :, self.start:self.end]

Use the custom layer in your grouped_convolution_block function:

def grouped_convolution_block(inputs, num_filters, kernel_size, strides, cardinality): 
    # Adds a grouped convolution block
    group_list = [] 
    grouped_channels = int(num_filters / cardinality)
    
    for c in range(cardinality):
        start = c * grouped_channels
        end = (c + 1) * grouped_channels
        x = GroupSliceLayer(start, end)(inputs)
        group_list.append(x)

answered Dec 7, 2024 at 6:51

Seven

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