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bcjr_rnn_train.py
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__author__ = 'yihanjiang'
'''
bcjr_rnn_train.py: train a BCJR-like RNN for Turbo Decoder.
'''
from keras import backend as K
import tensorflow as tf
import keras
from keras.models import Model
from keras.layers import Dense, Input
from keras.layers.normalization import BatchNormalization
from keras.layers.core import Lambda
from keras.layers import TimeDistributed
from keras.layers import LSTM, GRU, SimpleRNN
from keras.layers.wrappers import Bidirectional
from keras import regularizers
import sys
import pickle
import numpy as np
import math
from bcjr_util import generate_bcjr_example
from conv_decoder import build_decoder, errors
import commpy.channelcoding.interleavers as RandInterlv
import commpy.channelcoding.convcode as cc
def get_args():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-num_block_train', type=int, default=100)
parser.add_argument('-num_block_test', type=int, default=100)
parser.add_argument('-block_len', type=int, default=100)
parser.add_argument('-num_dec_iteration', type=int, default=6)
parser.add_argument('-code_rate', type=int, default=3, help='if using BCJR, use code rate 3')
parser.add_argument('-enc1', type=int, default=7)
parser.add_argument('-enc2', type=int, default=5)
parser.add_argument('-feedback', type=int, default=7)
parser.add_argument('-M', type=int, default=2, help="Number of delay elements in the convolutional encoder")
parser.add_argument('-num_cpu', type=int, default=4)
parser.add_argument('-snr_test_start', type=float, default=-1.5)
parser.add_argument('-snr_test_end', type=float, default=2.0)
parser.add_argument('-snr_points', type=int, default=8)
parser.add_argument('-init_nw_model', type=str, default='default')
parser.add_argument('-rnn_setup', choices = ['lstm', 'gru'], default = 'lstm')
parser.add_argument('-rnn_direction', choices = ['bd', 'sd'], default = 'bd')
parser.add_argument('-num_Dec_layer', type=int, default=2)
parser.add_argument('-num_Dec_unit', type=int, default=200)
parser.add_argument('-batch_size', type=int, default=10)
parser.add_argument('-learning_rate', type=float, default=0.001)
parser.add_argument('-num_epoch', type=int, default=20)
parser.add_argument('-noise_type', choices = ['awgn', 't-dist','hyeji_bursty'], default='awgn')
parser.add_argument('-train_snr', type=float, default=-1.0)
parser.add_argument('-loss', choices = ['binary_crossentropy', 'mse', 'mae'], default='mse')
parser.add_argument('-train_channel_low', type=float, default=0.0)
parser.add_argument('-train_channel_high', type=float, default=8.0)
parser.add_argument('-radar_power', type=float, default=20.0)
parser.add_argument('-radar_prob', type=float, default=0.05)
parser.add_argument('-fixed_var', type=float, default=0.00)
parser.add_argument('--GPU_proportion', type=float, default=1.00)
parser.add_argument('-id', type=str, default=str(np.random.random())[2:8])
args = parser.parse_args()
print args
print '[ID]', args.id
return args
def test_bcjr_ber(args, model_path):
'''
under construction. ugly code available via requirement. ETA 0228.
'''
pass
if __name__ == '__main__':
# get arguments
args = get_args()
print '[BCJR Setting Parameters] Network starting path is ', args.init_nw_model
print '[BCJR Setting Parameters] Initial learning_rate is ', args.learning_rate
print '[BCJR Setting Parameters] Training batch_size is ', args.batch_size
print '[BCJR Setting Parameters] Training num_epoch is ', args.num_epoch
print '[BCJR Setting Parameters] Turbo Decoding Iteration ', args.num_dec_iteration
print '[BCJR Setting Parameters] RNN Direction is ', args.rnn_direction
print '[BCJR Setting Parameters] RNN Model Type is ', args.rnn_setup
print '[BCJR Setting Parameters] Number of RNN layer is ', args.num_Dec_layer
print '[BCJR Setting Parameters] Number of RNN unit is ', args.num_Dec_unit
M = np.array([args.M])
generator_matrix = np.array([[args.enc1,args.enc2]])
feedback = args.feedback
trellis1 = cc.Trellis(M, generator_matrix,feedback=feedback)# Create trellis data structure
trellis2 = cc.Trellis(M, generator_matrix,feedback=feedback)# Create trellis data structure
interleaver = RandInterlv.RandInterlv(args.block_len, 0)
p_array = interleaver.p_array
print '[BCJR Code Codec] Encoder', 'M ', M, ' Generator Matrix ', generator_matrix, ' Feedback ', feedback
codec = [trellis1, trellis2, interleaver]
print '[BCJR Setting Parameters] Training Data SNR is ', args.train_snr, ' dB'
print '[BCJR Setting Parameters] Code Block Length is ', args.block_len
print '[BCJR Setting Parameters] Number of Train Block is ', args.num_block_train, ' Test Block ', args.num_block_test
model = build_decoder(args)
bcjr_inputs_train, bcjr_outputs_train = generate_bcjr_example(args.num_block_train, args.block_len,
codec, is_save = False,num_iteration = args.num_dec_iteration,
train_snr_db = args.train_snr, save_path = './tmp/')
bcjr_inputs_test, bcjr_outputs_test = generate_bcjr_example(args.num_block_test, args.block_len,
codec, is_save = False, num_iteration = args.num_dec_iteration,
train_snr_db = args.train_snr, save_path = './tmp/')
train_batch_size = args.batch_size # 100 good.
test_batch_size = args.batch_size
input_feature_num = 3
optimizer= keras.optimizers.adam(lr=args.learning_rate)
model.compile(optimizer=optimizer,loss=args.loss, metrics=['mae'])
if args.init_nw_model != 'default':
model.load_weights(args.init_nw_model)
print '[BCJR][Warning] Loaded Some init weight', args.init_nw_model
else:
print '[BCJR][Warning] Train from scratch, not loading weight!'
model.fit(x=bcjr_inputs_train, y=bcjr_outputs_train, batch_size=train_batch_size,
epochs=args.num_epoch, validation_data= (bcjr_inputs_test, bcjr_outputs_test))
model.save_weights('./tmp/bcjr_train'+args.id +'_1.h5')
print '[BCJR] Saved Model at', './tmp/bcjr_train'+args.id +'_1.h5'
test_bcjr_ber(args,'./tmp/bcjr_train'+args.id +'_1.h5' )