Making it easier to adapt (or not) a model

dnn/train_lpcnet.py

@@ -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])