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Making it easier to adapt (or not) a model

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This commit is contained in:
Jean-Marc Valin 2019-03-24 03:48:26 -04:00
parent edee9cd8f2
commit 219fbff4e6
1 changed files with 17 additions and 3 deletions
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20
dnn/train_lpcnet.py
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@ -105,6 +105,20 @@ del in_exc
# dump models to disk as we go
checkpoint = ModelCheckpoint('lpcnet24g_384_10_G16_{epoch:02d}.h5')
model.load_weights('lpcnet24c_384_10_G16_120.h5')
model.compile(optimizer=Adam(0.0001, amsgrad=True), loss='sparse_categorical_crossentropy')
model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(0, 0, 1, (0.05, 0.05, 0.2))])
#Set this to True to adapt an existing model (e.g. on new data)
adaptation = False
if adaptation:
#Adapting from an existing model
model.load_weights('lpcnet24c_384_10_G16_120.h5')
sparsify = lpcnet.Sparsify(0, 0, 1, (0.05, 0.05, 0.2))
lr = 0.0001
decay = 0
else:
#Training from scratch
sparsify = lpcnet.Sparsify(2000, 40000, 400, (0.05, 0.05, 0.2))
lr = 0.001
decay = 5e-5
model.compile(optimizer=Adam(lr, amsgrad=True, decay=decay), loss='sparse_categorical_crossentropy')
model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, sparsify])