CODEWITHSUNDEEP
tensorflowmachine-learningneural-network
New Developer
New Developer

Reputation: 89

Build layers with fixed weights in TensorFlow

I want to build a fully-connected (dense) layer for a regression task. I usually do it with TF2, using Keras API like:

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(units=2, activation='sigmoid', input_shape=(1, )))
model.add(tf.keras.layers.Dense(units=2, activation='linear'))
model.compile(optimizer='adam', loss='mae')
model.fit(inp_data, out_data, epochs=1000)

Now I want to build a custom layer. The layer is composed of, say 10 units, in which 8 units have predefined, fixed, untrainable weights and biases and 2 units have randomly-chosen weights and biases, to be trained by the network. Has anyone any idea how can I define it in Tensorflow?

Upvotes: 2

Views: 3812

Answers (1)

javidcf
javidcf

Reputation: 59701

Keras layers may receive a trainable parameter, True by default, to indicate whether you want them to be trained. Non-trainable layers will just keep the value they are given by the initializer. If I understand correctly, you want to have one layer which is only partially trainable. That is not possible as such with existing layers. Maybe you could do it with a custom layer class, but you can have an equivalent behavior by using two simple layers and then concatenating them (as long as your activation works element-wise, and even it it doesn't, like in a softmax layer, you could apply that activation after the concatenation). This is how it could work:

inputs = tf.keras.Input(shape=(1,))
# This is the trainable part of the layer
layer_train = tf.keras.layers.Dense(units=8, activation='sigmoid')(inputs)
# This is the non-trainable part
layer_const = tf.keras.layers.Dense(units=2, activation='sigmoid', trainable=False)(inputs)
# Merge both parts
layer = tf.keras.layers.Concatenate()([layer_train, layer_const])
# Make model
model = tf.keras.Model(inputs=inputs, outputs=layer)
# ...

Upvotes: 6

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