Decoder's weights of Autoencoder with tied weights in Keras : <p>I have implemented a tied weights Auto-encoder in Keras and have successfully trained it. </p>

<p>My goal is to use only the decoder part of the Auto-encoder as the last layer of another network, to fine tune both the network and the decoder.</p>

<p>Thing is, as you can see below from the summary, the decoder has no parameters with my tied weights implementation, so there is nothing to be fine tuned. (<code>decoder.get_weights()</code> returns <code>[]</code>)</p>

<p>My question is: Should I change the implementation of the tied weights, so that the tied layer can still hold weights, that is the transposed weights of the encoder? If yes, how? </p>

<p>Or am I just way off?</p>

<p>Below is the summary of the autoencoder model as well as the class of the tied Dense layer (slightly modified from <a href="https://github.com/nanopony/keras-convautoencoder/blob/master/autoencoder_layers.py" rel="noreferrer">https://github.com/nanopony/keras-convautoencoder/blob/master/autoencoder_layers.py.</a>)</p>

<hr>

<pre><code>Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
encoded (Dense)                  (None, Enc_dim)          33000       dense_input_1[0][0]              
____________________________________________________________________________________________________
tieddense_1 (TiedtDense)          (None, Out_Dim)            0           encoded[0][0]                    
====================================================================================================
Total params: 33,000
Trainable params: 33,000
Non-trainable params: 0
________________________________________________________________________


class TiedtDense(Dense):
def __init__(self, output_dim, master_layer, init='glorot_uniform', activation='linear', weights=None,
             W_regularizer=None, b_regularizer=None, activity_regularizer=None,
             W_constraint=None, b_constraint=None, input_dim=None, **kwargs):
    self.master_layer = master_layer
    super(TiedtDense, self).__init__(output_dim, **kwargs)

def build(self, input_shape):
    assert len(input_shape) &gt;= 2
    input_dim = input_shape[-1]
    self.input_dim = input_dim


    self.W = tf.transpose(self.master_layer.W)
    self.b = K.zeros((self.output_dim,))
    self.params = [self.b]
    self.regularizers = []
    if self.W_regularizer:
        self.W_regularizer.set_param(self.W)
        self.regularizers.append(self.W_regularizer)

    if self.b_regularizer:
        self.b_regularizer.set_param(self.b)
        self.regularizers.append(self.b_regularizer)

    if self.activity_regularizer:
        self.activity_regularizer.set_layer(self)
        self.regularizers.append(self.activity_regularizer)

    if self.initial_weights is not None:
        self.set_weights(self.initial_weights)
        del self.initial_weights
</code></pre>
