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Using 8-bit recurrent weights for GRU B

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Jean-Marc Valin 2021-07-21 16:38:35 -04:00
parent 8783ef0088
commit 51ef273e06
4 changed files with 13 additions and 6 deletions
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4
dnn/training_tf2/lpcnet.py
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@ -259,12 +259,12 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 20, train
rnn = CuDNNGRU(rnn_units1, return_sequences=True, return_state=True, name='gru_a',
recurrent_constraint = constraint, recurrent_regularizer=quant)
rnn2 = CuDNNGRU(rnn_units2, return_sequences=True, return_state=True, name='gru_b',
kernel_constraint=constraint, kernel_regularizer=quant)
kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant)
else:
rnn = GRU(rnn_units1, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_a',
recurrent_constraint = constraint, recurrent_regularizer=quant)
rnn2 = GRU(rnn_units2, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_b',
kernel_constraint=constraint, kernel_regularizer=quant)
kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant)
rnn_in = Concatenate()([cpcm, rep(cfeat)])
md = MDense(pcm_levels, activation='sigmoid', name='dual_fc')