frame_max = max(IDs_utter, key=lambda x: x[1])
train_data_length = len(pickList) * VAL_SET_RATIO
DATA_LAYER = [mem_pgram.shape[1]]
LABEL_LAYER = DATA_LAYER
LAYERS = DATA_LAYER + HIDDEN_LAYERS + LABEL_LAYER
########################
# Create lstm #
########################
print "Creating lstm..."
nn = LSTM_net(LAYERS,
batch_size=BATCH_SIZE,
momentum_type=MOMENTUM_TYPE,
act_type=ACT_FUNC,
cost_type=COST_FUNC)
########################
# Train lstm #
########################
val_label_vec = None
StateToVec = get_PhoneStateVec()
PhoneState = load_liststateto48()
PhoneIdx = load_dict_IdxPh48()
prev_err = float('inf')
prev_2 = float('inf')
prev_3 = float('inf')
cal_dev = 3
MEM_PGRAM = PGRAM_ROOT + DNN_MODEL + '_test.pgram'
MEM_PGRAM_shape = (180406, 48)
########################
# load lstm open file #
########################
print "Loading lstm..."
layers, W, Wi, Wf, Wo, b, bi, bf, bo = pickle.load(
open(MODEL_ROOT + MODEL, 'rb'))
nn = LSTM_net(layers,
W,
Wi,
Wf,
Wo,
b,
bi,
bf,
bo,
batch_size=BATCH_SIZE,
momentum_type=MOMENTUM_TYPE,
act_type=ACT_FUNC,
cost_type=COST_FUNC)
#IDs,TEST_DATA,VAL_DATA = readfile_for_test( TEST_ROOT+TEST,1 )
print "Reading data..."
mem_pgram = np.memmap(MEM_PGRAM,
dtype='float32',
mode='r',
shape=MEM_PGRAM_shape)
IDs = readID(PKL_ID)
