| #!/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() |
