ref: 93fe345080654071181025efa6d8e289c132dfc9
dir: /scripts/dump_rnn.py/
#!/usr/bin/python
from __future__ import print_function
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import GRU
from keras.models import load_model
from keras import backend as K
import numpy as np
def printVector(f, vector, name):
v = np.reshape(vector, (-1));
#print('static const float ', name, '[', len(v), '] = \n', file=f)
f.write('static const opus_int16 {}[{}] = {{\n '.format(name, len(v)))
for i in range(0, len(v)):
f.write('{}'.format(int(round(8192*v[i]))))
if (i!=len(v)-1):
f.write(',')
else:
break;
if (i%8==7):
f.write("\n ")
else:
f.write(" ")
#print(v, file=f)
f.write('\n};\n\n')
return;
def binary_crossentrop2(y_true, y_pred):
return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1)
model = load_model("weights.hdf5", custom_objects={'binary_crossentrop2': binary_crossentrop2})
weights = model.get_weights()
f = open('rnn_weights.c', 'w')
f.write('/*This file is automatically generated from a Keras model*/\n\n')
f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "mlp.h"\n\n')
printVector(f, weights[0], 'layer0_weights')
printVector(f, weights[1], 'layer0_bias')
printVector(f, weights[2], 'layer1_weights')
printVector(f, weights[3], 'layer1_recur_weights')
printVector(f, weights[4], 'layer1_bias')
printVector(f, weights[5], 'layer2_weights')
printVector(f, weights[6], 'layer2_bias')
f.write('const DenseLayer layer0 = {\n layer0_bias,\n layer0_weights,\n 25, 16, 0\n};\n\n')
f.write('const GRULayer layer1 = {\n layer1_bias,\n layer1_weights,\n layer1_recur_weights,\n 16, 12\n};\n\n')
f.write('const DenseLayer layer2 = {\n layer2_bias,\n layer2_weights,\n 12, 2, 1\n};\n\n')
f.close()