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Using non-cudnn version of the GRU for the weights

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Not sure how the layout of the CuDNN version is
This commit is contained in:
Jean-Marc Valin 2018-11-23 20:07:42 -05:00
parent b0c61158f7
commit d93239e955
2 changed files with 12 additions and 7 deletions
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6
dnn/dump_lpcnet.py
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@ -47,8 +47,8 @@ def dump_gru_layer(self, f, hf):
activation = self.activation.__name__.upper()
else:
activation = 'TANH'
if hasattr(self, 'reset_after'):
reset_after = self.reset_after
if hasattr(self, 'reset_after') and not self.reset_after:
reset_after = 0
else:
reset_after = 1
f.write('const GRULayer {} = {{\n {}_bias,\n {}_weights,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}, {}\n}};\n\n'
@ -97,7 +97,7 @@ def dump_mdense_layer(self, f, hf):
MDense.dump_layer = dump_mdense_layer
model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=640)
model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=640, use_gpu=False)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
#model.summary()