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#!/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 sys
import re
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 rnn_weight {}[{}] = {{\n '.format(name, len(v)))
for i in range(0, len(v)):
f.write('{}'.format(min(127, int(round(256*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 printLayer(f, hf, layer):
weights = layer.get_weights()
printVector(f, weights[0], layer.name + '_weights')
if len(weights) > 2:
printVector(f, weights[1], layer.name + '_recurrent_weights')
printVector(f, weights[-1], layer.name + '_bias')
name = layer.name
activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
if len(weights) > 2:
f.write('const GRULayer {} = {{\n {}_bias,\n {}_weights,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, name, weights[0].shape[0], weights[0].shape[1]/3, activation))
hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[1]/3))
hf.write('extern const GRULayer {};\n\n'.format(name));
else:
f.write('const DenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, weights[0].shape[0], weights[0].shape[1], activation))
hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[1]))
hf.write('extern const DenseLayer {};\n\n'.format(name));
def foo(c, name):
return 1
def mean_squared_sqrt_error(y_true, y_pred):
return K.mean(K.square(K.sqrt(y_pred) - K.sqrt(y_true)), axis=-1)
model = load_model(sys.argv[1], custom_objects={'msse': mean_squared_sqrt_error, 'mean_squared_sqrt_error': mean_squared_sqrt_error, 'my_crossentropy': mean_squared_sqrt_error, 'mycost': mean_squared_sqrt_error, 'WeightClip': foo})
weights = model.get_weights()
f = open(sys.argv[2], 'w')
hf = open(sys.argv[3], '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 "rnn.h"\n\n')
hf.write('/*This file is automatically generated from a Keras model*/\n\n')
hf.write('#ifndef RNN_DATA_H\n#define RNN_DATA_H\n\n#include "rnn.h"\n\n')
layer_list = []
for i, layer in enumerate(model.layers):
if len(layer.get_weights()) > 0:
printLayer(f, hf, layer)
if len(layer.get_weights()) > 2:
layer_list.append(layer.name)
hf.write('struct RNNState {\n')
for i, name in enumerate(layer_list):
hf.write(' float {}_state[{}_SIZE];\n'.format(name, name.upper()))
hf.write('};\n')
hf.write('\n\n#endif\n')
f.close()
hf.close()
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