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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()

13
dnn/lpcnet.py
View file

@ -2,7 +2,7 @@
import math
from keras.models import Model
from keras.layers import Input, LSTM, CuDNNGRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation
from keras.layers import Input, GRU, CuDNNGRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation
from keras import backend as K
from keras.initializers import Initializer
from keras.callbacks import Callback
@ -85,7 +85,7 @@ class PCMInit(Initializer):
'seed': self.seed
}
def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38):
def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38, use_gpu=True):
pcm = Input(shape=(None, 2))
exc = Input(shape=(None, 1))
feat = Input(shape=(None, nb_used_features))
@ -115,8 +115,13 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38):
rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))
rnn = CuDNNGRU(rnn_units1, return_sequences=True, return_state=True, name='gru_a')
rnn2 = CuDNNGRU(rnn_units2, return_sequences=True, return_state=True, name='gru_b')
if use_gpu:
rnn = CuDNNGRU(rnn_units1, return_sequences=True, return_state=True, name='gru_a')
rnn2 = CuDNNGRU(rnn_units2, return_sequences=True, return_state=True, name='gru_b')
else:
rnn = GRU(rnn_units1, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_a')
rnn2 = GRU(rnn_units2, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_b')
rnn_in = Concatenate()([cpcm, cexc, rep(cfeat)])
md = MDense(pcm_levels, activation='softmax', name='dual_fc')
gru_out1, _ = rnn(rnn_in)