shithub: opus

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Support for biased loss

--- a/dnn/training_tf2/train_plc.py

+++ b/dnn/training_tf2/train_plc.py

@@ -46,6 +46,8 @@

 parser.add_argument('--seq-length', metavar='<sequence length>', default=1000, type=int, help='sequence length to use (default 1000)')

 parser.add_argument('--lr', metavar='<learning rate>', type=float, help='learning rate')

 parser.add_argument('--decay', metavar='<decay>', type=float, help='learning rate decay')

+parser.add_argument('--band-loss', metavar='<weight>', default=1.0, type=float, help='weight of band loss (default 1.0)')

+parser.add_argument('--loss-bias', metavar='<bias>', default=0.0, type=float, help='loss bias towards low energy (default 0.0)')

 parser.add_argument('--logdir', metavar='<log dir>', help='directory for tensorboard log files')

@@ -94,13 +96,13 @@

 if retrain:

     input_model = args.retrain

-def plc_loss(alpha=1.0):

+def plc_loss(alpha=1.0, bias=0.):

     def loss(y_true,y_pred):

         mask = y_true[:,:,-1:]

         y_true = y_true[:,:,:-1]

-        e = (y_true - y_pred)*mask

+        e = (y_pred - y_true)*mask

         e_bands = tf.signal.idct(e[:,:,:-2], norm='ortho')

-        l1_loss = K.mean(K.abs(e)) + alpha*K.mean(K.abs(e_bands))

+        l1_loss = K.mean(K.abs(e)) + bias*K.mean(K.maximum(e[:,:,:1], 0.)) + alpha*K.mean(K.abs(e_bands) + bias*K.maximum(e_bands, 0.))

         return l1_loss

     return loss

@@ -108,7 +110,7 @@

     def L1_loss(y_true,y_pred):

         mask = y_true[:,:,-1:]

         y_true = y_true[:,:,:-1]

-        e = (y_true - y_pred)*mask

+        e = (y_pred - y_true)*mask

         l1_loss = K.mean(K.abs(e))

         return l1_loss

     return L1_loss

@@ -117,7 +119,7 @@

     def L1_band_loss(y_true,y_pred):

         mask = y_true[:,:,-1:]

         y_true = y_true[:,:,:-1]

-        e = (y_true - y_pred)*mask

+        e = (y_pred - y_true)*mask

         e_bands = tf.signal.idct(e[:,:,:-2], norm='ortho')

         l1_loss = K.mean(K.abs(e_bands))

         return l1_loss

@@ -128,7 +130,7 @@

 with strategy.scope():

     model = lpcnet.new_lpcnet_plc_model(rnn_units=args.gru_size, batch_size=batch_size, training=True, quantize=quantize, cond_size=args.cond_size)

-    model.compile(optimizer=opt, loss=plc_loss(alpha=1.), metrics=[plc_l1_loss(), plc_band_loss()])

+    model.compile(optimizer=opt, loss=plc_loss(alpha=args.band_loss, bias=args.loss_bias), metrics=[plc_l1_loss(), plc_band_loss()])

     model.summary()

 lpc_order = 16

-- 

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