mirror of
https://github.com/TorqueGameEngines/Torque3D.git
synced 2026-01-21 13:14:46 +00:00
a745fc3757
added libraries: opus flac libsndfile updated: libvorbis libogg openal - Everything works as expected for now. Bare in mind libsndfile needed the check for whether or not it could find the xiph libraries removed in order for this to work.
58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
#!/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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