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