1#!/usr/bin/python 2 3from __future__ import print_function 4 5from keras.models import Sequential 6from keras.models import Model 7from keras.layers import Input 8from keras.layers import Dense 9from keras.layers import LSTM 10from keras.layers import GRU 11from keras.models import load_model 12from keras import backend as K 13import sys 14 15import numpy as np 16 17def printVector(f, vector, name): 18 v = np.reshape(vector, (-1)); 19 #print('static const float ', name, '[', len(v), '] = \n', file=f) 20 f.write('static const opus_int8 {}[{}] = {{\n '.format(name, len(v))) 21 for i in range(0, len(v)): 22 f.write('{}'.format(max(-128,min(127,int(round(128*v[i])))))) 23 if (i!=len(v)-1): 24 f.write(',') 25 else: 26 break; 27 if (i%8==7): 28 f.write("\n ") 29 else: 30 f.write(" ") 31 #print(v, file=f) 32 f.write('\n};\n\n') 33 return; 34 35def binary_crossentrop2(y_true, y_pred): 36 return K.mean(2*K.abs(y_true-0.5) * K.binary_crossentropy(y_pred, y_true), axis=-1) 37 38 39#model = load_model(sys.argv[1], custom_objects={'binary_crossentrop2': binary_crossentrop2}) 40main_input = Input(shape=(None, 25), name='main_input') 41x = Dense(32, activation='tanh')(main_input) 42x = GRU(24, activation='tanh', recurrent_activation='sigmoid', return_sequences=True)(x) 43x = Dense(2, activation='sigmoid')(x) 44model = Model(inputs=main_input, outputs=x) 45model.load_weights(sys.argv[1]) 46 47weights = model.get_weights() 48 49f = open(sys.argv[2], 'w') 50 51f.write('/*This file is automatically generated from a Keras model*/\n\n') 52f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "mlp.h"\n\n') 53 54printVector(f, weights[0], 'layer0_weights') 55printVector(f, weights[1], 'layer0_bias') 56printVector(f, weights[2], 'layer1_weights') 57printVector(f, weights[3], 'layer1_recur_weights') 58printVector(f, weights[4], 'layer1_bias') 59printVector(f, weights[5], 'layer2_weights') 60printVector(f, weights[6], 'layer2_bias') 61 62f.write('const DenseLayer layer0 = {\n layer0_bias,\n layer0_weights,\n 25, 32, 0\n};\n\n') 63f.write('const GRULayer layer1 = {\n layer1_bias,\n layer1_weights,\n layer1_recur_weights,\n 32, 24\n};\n\n') 64f.write('const DenseLayer layer2 = {\n layer2_bias,\n layer2_weights,\n 24, 2, 1\n};\n\n') 65 66f.close() 67