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Combine LAR+L1 regularization

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This commit is contained in:
Jean-Marc Valin 2021-10-13 02:33:35 -04:00
parent 054d984bf3
commit fe7b54c0e8
4 changed files with 33 additions and 8 deletions
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6
dnn/training_tf2/train_lpcnet.py
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@ -125,7 +125,7 @@ with strategy.scope():
if not flag_e2e:
model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics='sparse_categorical_crossentropy')
else:
model.compile(optimizer=opt, loss = interp_mulaw(gamma=gamma),metrics=[metric_cel,metric_icel,metric_exc_sd,metric_oginterploss])
model.compile(optimizer=opt, loss = [interp_mulaw(gamma=gamma), loss_matchlar()], loss_weights = [1.0, 2.0], metrics={'pdf':[metric_cel,metric_icel,metric_exc_sd,metric_oginterploss]})
model.summary()
feature_file = args.features
@ -157,7 +157,7 @@ data = np.reshape(data, (nb_frames, pcm_chunk_size, 4))
sizeof = features.strides[-1]
features = np.lib.stride_tricks.as_strided(features, shape=(nb_frames, feature_chunk_size+4, nb_features),
strides=(feature_chunk_size*nb_features*sizeof, nb_features*sizeof, sizeof))
features = features[:, :, :nb_used_features]
#features = features[:, :, :nb_used_features]
periods = (.1 + 50*features[:,:,18:19]+100).astype('int16')
@ -185,5 +185,5 @@ else:
model.save_weights('{}_{}_initial.h5'.format(args.output, args.grua_size))
csv_logger = CSVLogger('training_vals.log')
loader = LPCNetLoader(data, features, periods, batch_size)
loader = LPCNetLoader(data, features, periods, batch_size, lpc_out=flag_e2e)
model.fit(loader, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, sparsify, grub_sparsify, csv_logger])