Python RNN_netの例

コード例 #1

ファイル: testRNN.py プロジェクト: iammrhelo/MLDS

BATCH_SIZE = 37
PKL_ID = './ID_test.pkl'
PGRAM_ROOT= 'dnn_result/posteriorgram/'
DNN_MODEL = 'Angus_2'
MEM_PGRAM = PGRAM_ROOT+DNN_MODEL+'_test.pgram'
MEM_PGRAM_shape = (180406,48)

########################
#  load RNN open file  #
########################

print "Loading RNN..."
layers,Ws,Whs,bs = pickle.load(open(MODEL_ROOT+MODEL,'rb')) 
nn = RNN_net(layers,Ws,Whs,bs,
             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)
idx = 0
IDs_utter = []
while idx <= len(IDs)-1:
    IDs_utter.append(["_".join(IDs[idx][0].split('_')[0:2]),IDs[idx][1]])
    #IDs_utter = [utter_name,utter_max]
    idx+=IDs[idx][1]

コード例 #2

ファイル: trainRNN.py プロジェクト: iammrhelo/MLDS

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 RNN       #
########################

print "Creating RNN..."
nn = RNN_net(LAYERS,
             batch_size = BATCH_SIZE,
             momentum_type = MOMENTUM_TYPE,
             act_type = ACT_FUNC,
             cost_type = COST_FUNC)

########################
#      Train RNN       #
########################
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

コード例 #3

BATCH_SIZE = 1
PKL_ID = './ID_test.pkl'
PGRAM_ROOT= 'dnn_result/posteriorgram/'
DNN_MODEL = 'Angus_2'
MEM_PGRAM = PGRAM_ROOT+DNN_MODEL+'_test.pgram'
MEM_PGRAM_shape = (180406,48)

########################
#  load RNN open file  #
########################

print "Loading RNN..."
layers,Ws,Whs,bs = pickle.load(open(MODEL_ROOT+MODEL,'rb')) 
nn = RNN_net(layers,Ws,Whs,bs,
             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)
idx = 0
IDs_utter = []
while idx <= len(IDs)-1:
    IDs_utter.append(["_".join(IDs[idx][0].split('_')[0:2]),IDs[idx][1]])
    #IDs_utter = [utter_name,utter_max]
    idx+=IDs[idx][1]

コード例 #4

ファイル: trainRNN_sgd_norm.py プロジェクト: ChunHungLiu/MLDS

frame_max = max(IDs_utter, key=lambda x: x[1])
train_data_length = len(pickList) * VAL_SET_RATIO
pdb.set_trace()
DATA_LAYER = [mem_pgram.shape[1]]
LABEL_LAYER = DATA_LAYER

LAYERS = DATA_LAYER + HIDDEN_LAYERS + LABEL_LAYER

########################
#     Create RNN       #
########################

print "Creating RNN..."
nn = RNN_net(LAYERS,
             batch_size=BATCH_SIZE,
             momentum_type=MOMENTUM_TYPE,
             act_type=ACT_FUNC,
             cost_type=COST_FUNC)

########################
#      Train RNN       #
########################
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

コード例 #5

ファイル: trainRNN_1_utter.py プロジェクト: ChunHungLiu/MLDS

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 RNN       #
########################

print "Creating RNN..."
nn = RNN_net(LAYERS,
             batch_size=BATCH_SIZE,
             momentum_type="rmsprop",
             act_type="ReLU",
             cost_type="EU") 

########################
#      Train RNN       #
########################
val_label_vec = None
StateToVec = get_PhoneStateVec()
PhoneState = load_liststateto48()
PhoneIdx   = load_dict_IdxPh48()

p_s,p_e = 0,1
if p_e > len(pickList):
    p_e = len(pickList)

コード例 #6

ファイル: trainRNN_batch_fill0_norm_toEJ.py プロジェクト: iammrhelo/MLDS

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 RNN       #
########################

print "Creating RNN..."
nn = RNN_net(LAYERS,
             batch_size = BATCH_SIZE,
             momentum_type = MOMENTUM_TYPE,
             act_type = ACT_FUNC,
             cost_type = COST_FUNC)

########################
#      Train RNN       #
########################
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

コード例 #7

ファイル: testRNN_sgd_multi.py プロジェクト: iammrhelo/MLDS

BATCH_SIZE = 1
PKL_ID = './ID_test.pkl'
PGRAM_ROOT= 'dnn_result/posteriorgram/'
DNN_MODEL = 'Angus_2'
MEM_PGRAM = PGRAM_ROOT+DNN_MODEL+'_test.pgram'
MEM_PGRAM_shape = (180406,48)

########################
#  load RNN open file  #
########################

print "Loading RNN..."
layers,Ws,Whs,bs = pickle.load(open(MODEL_ROOT+MODEL,'rb')) 
nn = RNN_net(layers,Ws,Whs,bs,
             batch_size=BATCH_SIZE,
             momentum_type=MOMENTUM_TYPE,
             act_type=ACT_FUNC,
             cost_type=COST_FUNC)

nn2 = RNN_net(layers,Ws,Whs,bs,
              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)
idx = 0