MEM_DATA = './data.fbank.memmap'
MEM_TEST = './test.fbank.memmap'
PKL_ID = './ID.pkl'
PKL_ID_TEST = './ID_test.pkl'
MEM_DATA_shape = (621, 180406)
STATE_LENGTH = 1943
PHONE_LENGTH = 48
BATCH_SIZE = 2
########################
# load DNN open file #
########################
layers,Ws,bs = pickle.load(open(MODEL_ROOT+MODEL,'rb'))
nn = DNN(layers,Ws,bs,
act=ACT_FUNC,
cost=COST_FUNC)
# read Data #
mem_data = np.memmap(MEM_TEST,dtype='float32',mode='r',shape=MEM_DATA_shape)
IDs = readID(PKL_ID_TEST)
print "Data parsed"
########################
# Save posteriorgram #
########################
mem_shape = (len(IDs),PHONE_LENGTH)
posteriorgram = np.memmap(PGRAM_ROOT+PGRAM,
dtype='float32',
mode='w+',
MODEL = "DATA_fbank_LABEL_phonemeState_HIDDEN_LAYERS_2048-2048-2048_L_RATE_0.01_MOMENTUM_0.9_DROPOUT_0.1_EPOCH_200_at_150" TEST_ROOT = './Data/fbank/' TEST = 'test.ark' #TEST = './train_ant.ark' PREDICTION_ROOT ='./result/prediction/' PREDICTION = MODEL + '.csv' ######################## # load DNN open file # ######################## layers,Ws,bs = pickle.load(open(MODEL_ROOT+MODEL,'rb')) nn = DNN(layers,Ws,bs) TEST_DATA,VAL_DATA = readfile_( TEST_ROOT+TEST,1 ) PRED_FILE = open( PREDICTION_ROOT + PREDICTION ,'wb') # Get Dictionaries Phone48 = load_liststateto48() PhoneMap48to39 = load_dict_48to39() # For CSV HEADER = ["Id","Prediction"] ######################## # Predict # ########################
LABEL_LAYER = [len(BATCHED_VECTORS[0][0])]
# pdb.set_trace()
LAYERS = DATA_LAYER + HIDDEN_LAYERS + LABEL_LAYER
print "Data parsed!!!"
########################
# Create Neural Net #
########################
model_path = "./result/model/DATA_fbank_LABEL_phoneme48_HIDDEN_LAYERS_1024-1024_L_RATE_0.01_MOMENTUM_0.9_DROPOUT_0.1_EPOCH_500_at_100"
ll, ww, bb = pickle.load(open(model_path, 'rb'))
nn = DNN(ll,
ww,
bb,
m_norm=MAX_NORM,
act=ACT_FUNC,
cost=COST_FUNC,
momentum_type=MOMENTUM_TYPE)
########################
# pre-Train Neural Net #
########################
'''
print "Start pre-training. pretrain {0} epoches".format(PRETRAIN_EPOCH)
prop_input = data
for l,da in enumerate(DAs):
for epoch in xrange(PRETRAIN_EPOCH):
batch_cost = 0
tStart = time.time()
for i in xrange( (data.shape[1]-1)/PRETRAIN_BATCH_SIZE + 1):
DATA_LAYER = [ len( BATCHED_TRAINING_SET[0][0] ) ]
LABEL_LAYER = [ len( BATCHED_VECTORS[0][0] ) ]
del BATCHED_TRAINING_SET
del BATCHED_VECTORS
# pdb.set_trace()
LAYERS = DATA_LAYER + HIDDEN_LAYERS + LABEL_LAYER
print "Data parsed!!!"
########################
# Create Neural Net #
########################
nn = DNN(LAYERS)
########################
# pre-Train Neural Net #
########################
'''
print "Start pre-training. pretrain {0} epoches".format(PRETRAIN_EPOCH)
prop_input = data
for l,da in enumerate(DAs):
for epoch in xrange(PRETRAIN_EPOCH):
batch_cost = 0
tStart = time.time()
for i in xrange( (data.shape[1]-1)/PRETRAIN_BATCH_SIZE + 1):
begin = i * PRETRAIN_BATCH_SIZE
if (i+1)*PRETRAIN_BATCH_SIZE > data.shape[1]:
end = data.shape[1]
DATA_LAYER = [mem_data.shape[0]]
LABEL_LAYER = [LABEL_VARIETY]
# pdb.set_trace()
LAYERS = DATA_LAYER + HIDDEN_LAYERS + LABEL_LAYER
print "Data parsed!!!"
########################
# Create Neural Net #
########################
nn = DNN(LAYERS,
m_norm=MAX_NORM,
act=ACT_FUNC,
cost=COST_FUNC,
momentum_type=MOMENTUM_TYPE)
########################
# pre-Train Neural Net #
########################
'''
print "Start pre-training. pretrain {0} epoches".format(PRETRAIN_EPOCH)
prop_input = data
for l,da in enumerate(DAs):
for epoch in xrange(PRETRAIN_EPOCH):
batch_cost = 0
tStart = time.time()
for i in xrange( (data.shape[1]-1)/PRETRAIN_BATCH_SIZE + 1):
begin = i * PRETRAIN_BATCH_SIZE
