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Controlling per-gate sparsity

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This commit is contained in:
Jean-Marc Valin 2018-12-10 16:15:50 -05:00
parent b9e0ea23e0
commit 8c271d60c4
2 changed files with 5 additions and 5 deletions
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8
dnn/lpcnet.py
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@ -64,12 +64,12 @@ class Sparsify(Callback):
N = p.shape[0]
#print("nb = ", nb, ", N = ", N);
#print(p.shape)
density = self.final_density
if self.batch < self.t_end:
r = 1 - (self.batch-self.t_start)/(self.t_end - self.t_start)
density = 1 - (1-self.final_density)*(1 - r*r*r)
#print ("density = ", density)
for k in range(nb):
density = self.final_density[k]
if self.batch < self.t_end:
r = 1 - (self.batch-self.t_start)/(self.t_end - self.t_start)
density = 1 - (1-self.final_density[k])*(1 - r*r*r)
A = p[:, k*N:(k+1)*N]
A = A - np.diag(np.diag(A))
A = np.transpose(A, (1, 0))

2
dnn/train_lpcnet.py
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@ -147,4 +147,4 @@ checkpoint = ModelCheckpoint('lpcnet15_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.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, 0.1)])
model.fit([in_data, in_exc, features, periods], out_data, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, (0.1, 0.1, 0.1))])