ref: d1811399301dfab6241bd0e89c19abc411210f0c
parent: a06e9a96ad85d545facb38fc6d3c77c28d58526d
author: Jean-Marc Valin <jmvalin@jmvalin.ca>
date: Wed Jan 9 11:52:26 EST 2019
Cleanup Remove the metric because it wasn't too useful and it's buggy in Keras 2.2.4.
--- a/dnn/train_lpcnet.py
+++ b/dnn/train_lpcnet.py
@@ -97,8 +97,8 @@
del pred
# dump models to disk as we go
-checkpoint = ModelCheckpoint('lpcnet18_384_10_G16_{epoch:02d}.h5')+checkpoint = ModelCheckpoint('lpcnet20_384_10_G16_{epoch:02d}.h5')-model.load_weights('lpcnet9b_384_10_G16_01.h5')-model.compile(optimizer=Adam(0.001, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
+#model.load_weights('lpcnet9b_384_10_G16_01.h5')+model.compile(optimizer=Adam(0.001, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy')
model.fit([in_data, in_exc, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, (0.05, 0.05, 0.2))])
--
⑨