shithub: opus

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changed distortion loss weighting back to 0.5, 0.5

--- a/dnn/training_tf2/encode_rdovae.py

+++ b/dnn/training_tf2/encode_rdovae.py

@@ -104,7 +104,7 @@

 nbits=80

 bits.astype('float32').tofile(args.output + "-syms.f32")

-lambda_val = 0.001 * np.ones((nb_sequences, sequence_size//2, 1))

+lambda_val = 0.0002 * np.ones((nb_sequences, sequence_size//2, 1))

 quant_id = np.round(3.8*np.log(lambda_val/.0002)).astype('int16')

 quant_id = quant_id[:,:,0]

 quant_embed = qembedding(quant_id)

--- a/dnn/training_tf2/train_rdovae.py

+++ b/dnn/training_tf2/train_rdovae.py

@@ -99,8 +99,8 @@

 opt = Adam(lr, decay=decay, beta_2=0.99)

 with strategy.scope():

-    model, encoder, decoder, _ = rdovae.new_rdovae_model(nb_used_features=20, nb_bits=80, batch_size=batch_size, cond_size=args.cond_size)

-    model.compile(optimizer=opt, loss=[rdovae.feat_dist_loss, rdovae.feat_dist_loss, rdovae.sq1_rate_loss, rdovae.sq2_rate_loss], loss_weights=[.1, .9, 1., .1], metrics={'hard_bits':rdovae.sq_rate_metric})

+    model, encoder, decoder, _ = rdovae.new_rdovae_model(nb_used_features=20, nb_bits=80, batch_size=batch_size, cond_size=args.cond_size, nb_quant=16)

+    model.compile(optimizer=opt, loss=[rdovae.feat_dist_loss, rdovae.feat_dist_loss, rdovae.sq1_rate_loss, rdovae.sq2_rate_loss], loss_weights=[.5, .5, 1., .1], metrics={'hard_bits':rdovae.sq_rate_metric})

     model.summary()

 lpc_order = 16

-- 

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