CODEWITHSUNDEEP
pythontensorflowkerastransfer-learning
glamredhel
glamredhel

Reputation: 336

In Tensorflow - Is it possible to lock specific convolution filters in a layer, or to remove them altogether?

When using transfer learning in Tensorflow, I know that one can lock layers from further training, by doing:

for layer in pre_trained_model.layers:
    layer.trainable = False

Is it possible to lock specific filters in the layer instead? As in - if the whole layer contains 64 filters, is it possible to:

OR

Upvotes: 3

Views: 430

Answers (1)

Vlad
Vlad

Reputation: 8595

One possible solution is to implement custom layer that splits convolution into separate number of filters convolutions and sets each channel (which is a convolution with one output channel) to trainable or to not trainable. For example:

import tensorflow as tf
import numpy as np

class Conv2DExtended(tf.keras.layers.Layer):
    def __init__(self, filters, kernel_size, **kwargs):
        self.filters = filters
        self.conv_layers = [tf.keras.layers.Conv2D(1, kernel_size, **kwargs) for _ in range(filters)]
        super().__init__()

    def build(self, input_shape):
        _ = [l.build(input_shape) for l in self.conv_layers]
        super().build(input_shape)

    def set_trainable(self, channels):
        """Sets trainable channels."""
        for i in channels:
            self.conv_layers[i].trainable = True

    def set_non_trainable(self, channels):
        """Sets not trainable channels."""
        for i in channels:
            self.conv_layers[i].trainable = False

    def call(self, inputs):
        results = [l(inputs) for l in self.conv_layers]
        return tf.concat(results, -1)

And usage example:

inputs = tf.keras.layers.Input((28, 28, 1))
conv = Conv2DExtended(filters=4, kernel_size=(3, 3))
conv.set_non_trainable([1, 2]) # only channels 0 and 3 are trainable
res = conv(inputs)
res = tf.keras.layers.Flatten()(res)
res = tf.keras.layers.Dense(1, activation=tf.nn.sigmoid)(res)

model = tf.keras.models.Model(inputs, res)
model.compile(optimizer=tf.keras.optimizers.SGD(),
              loss='binary_crossentropy',
              metrics=['accuracy'])
model.fit(np.random.normal(0, 1, (10, 28, 28, 1)),
          np.random.randint(0, 2, (10)),
          batch_size=2,
          epochs=5)

Upvotes: 3

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