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Dump embedding

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This commit is contained in:
Jean-Marc Valin 2018-11-23 23:33:35 -05:00
parent d93239e955
commit 477d08734d
2 changed files with 13 additions and 4 deletions
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15
dnn/dump_lpcnet.py
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@ -42,7 +42,6 @@ def dump_gru_layer(self, f, hf):
printVector(f, weights[0], name + '_weights')
printVector(f, weights[1], name + '_recurrent_weights')
printVector(f, weights[-1], name + '_bias')
#activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
if hasattr(self, 'activation'):
activation = self.activation.__name__.upper()
else:
@ -65,7 +64,6 @@ def dump_dense_layer(self, f, hf):
weights = self.get_weights()
printVector(f, weights[0], name + '_weights')
printVector(f, weights[-1], name + '_bias')
#activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
if hasattr(self, 'activation'):
activation = self.activation.__name__.upper()
else:
@ -84,7 +82,6 @@ def dump_mdense_layer(self, f, hf):
printVector(f, weights[0], name + '_weights')
printVector(f, weights[1], name + '_bias')
printVector(f, weights[1], name + '_factor')
#activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
if hasattr(self, 'activation'):
activation = self.activation.__name__.upper()
else:
@ -96,6 +93,18 @@ def dump_mdense_layer(self, f, hf):
return False
MDense.dump_layer = dump_mdense_layer
def dump_embedding_layer(self, f, hf):
name = self.name
print("printing layer " + name + " of type " + self.__class__.__name__)
weights = self.get_weights()
printVector(f, weights[0], name + '_weights')
f.write('const EmbeddingLayer {} = {{\n {}_weights,\n {}, {}\n}};\n\n'
.format(name, name, weights[0].shape[0], weights[0].shape[1]))
hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[1]))
hf.write('extern const EmbeddingLayer {};\n\n'.format(name));
return False
Embedding.dump_layer = dump_embedding_layer
model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=640, use_gpu=False)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])

2
dnn/lpcnet.py
View file

@ -102,7 +102,7 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38, use_g
embed2 = Embedding(256, embed_size, embeddings_initializer=PCMInit(), name='embed_exc')
cexc = Reshape((-1, embed_size))(embed2(exc))
pembed = Embedding(256, 64)
pembed = Embedding(256, 64, name='embed_pitch')
cat_feat = Concatenate()([feat, Reshape((-1, 64))(pembed(pitch))])
cfeat = fconv2(fconv1(cat_feat))