def __init__(self, LABS, LAYER_PARAMS):
assert LAYER_PARAMS['input_shape_w'] > LAYER_PARAMS['layer_num_hidd']
H = LAYER_PARAMS['input_shape_h']
W = LAYER_PARAMS['input_shape_w']
WEIGHT_SHAPE = LAYER_PARAMS['w_shape']
HIDD_SHAPE = LAYER_PARAMS['h_shape']
BIN_SHAPE = LAYER_PARAMS['bin_shape']
BOUT_SHAPE = LAYER_PARAMS['bout_shape']
NUM_HIDD = LAYER_PARAMS['layer_num_hidd']
NOISE_LEVEL = LAYER_PARAMS['layer_base_noise']
self.IS_LAST_LAY = LAYER_PARAMS['is_last_lay']
if LABS is not None:
self.LINDS = labin().fit_transform(LABS)
self.LABS = LABS
else:
self.LABS = np.zeros((H, 1))
self.LINDS = labin().fit_transform(self.LABS)
T_WEIGHT = np.random.randint(-1000, 1000, WEIGHT_SHAPE).astype(np.float32)
self.WEIGHT = (T_WEIGHT / np.sum(T_WEIGHT)) + \
(np.random.randn(WEIGHT_SHAPE[0],
WEIGHT_SHAPE[1]) * NOISE_LEVEL)
self.B_IN = np.zeros(BIN_SHAPE, dtype=np.float32, order='c')
self.B_OUT = np.zeros(BOUT_SHAPE, dtype=np.float32, order='c')
self.DROP_WEIGHT = np.ones(WEIGHT_SHAPE, dtype=np.int32, order='c')
self.OLD_DW = np.zeros(WEIGHT_SHAPE, dtype=np.float32, order='c')
self.IN1 = np.zeros(WEIGHT_SHAPE, dtype=np.float32, order='c')
self.IN2 = np.zeros((H, W), dtype=np.float32, order='c')
self.TUNE_HELPER = {'I' : None,
'H' : None,
'O' : None,
'ERR' : None,
'OLD_I' : None,
'OLD_O' : None,
'W_UPD' : (None, None)}
self.DELTA_H2O = None
self.DELTA_I2H = None
self.DELTA = None
self.ID_NUM = LAYER_PARAMS['id']
self.VERB = LAYER_PARAMS['verbose']
self.LAYER_PARAMS = LAYER_PARAMS
self.CONFIG_LOADED = True
if self.LAYER_PARAMS['per_init_to_zero'] > 0:
NUM_ZERO = int(np.floor(np.size(self.WEIGHT) *
self.LAYER_PARAMS['per_init_to_zero']))
for i in xrange(NUM_ZERO):
x = np.random.randint(0, np.size(self.WEIGHT, 0))
y = np.random.randint(0, np.size(self.WEIGHT, 1))
self.WEIGHT[x, y] = 0.0
def __init__(self, IN_DIMS, LABS, PARAMS, verbose):
self.METRIC_PARAMS= PARAMS
self.NUM_CENTS = self.METRIC_PARAMS['num_classes']
self.CLUSTER = np.zeros((self.NUM_CENTS, IN_DIMS[1]))
self.LINSD = labin().fit_transform(LABS)
self.LABS = LABS
def __init__(self, INPUT, LABS, NUM_HIDD, LAYER_PARAMS,
ID, NOISE_LEVEL, verbose = False):
if NUM_HIDD >= np.size(INPUT, 1):
raise IndexError, 'Number of hidden elements should be less that Input elements'
self.LAYER_PARAMS = LAYER_PARAMS
self.I_WIDTH = np.size(INPUT, 1)
self.I_HEIGHT = np.size(INPUT, 0)
self.ID_NUM = ID
if LABS is not None:
self.LINDS = labin().fit_transform(LABS)
self.LABS = LABS
else:
self.LABS = np.zeros((np.size(INPUT, 0), 1))
self.LINDS = labin().fit_transform(self.LABS)
self.WEIGHT = np.random.randint(-100, 100,
(self.I_WIDTH, NUM_HIDD)) * NOISE_LEVEL
self.B_IN = np.zeros((1, NUM_HIDD), dtype = np.float32, order ='c')
self.B_OUT = np.zeros((1, self.I_WIDTH), dtype = np.float32, order=c')
self.DROP_WEIGHT = np.ones(np.shape(self.WEIGHT),
dtype = np.int32, order ='c')
self.OLD_DW = np.zeros(np.shape(self.WEIGHT),
dtype = np.float32, order ='c')
self.IN1 = np.zeros((np.size(INPUT,0), np.size(self.WEIGHT, 1)),
dtype = np.float32, order = 'c')
self.IN2 = np.zeros(INPUT.shape, dtype = np.float32, order = 'c')
self.DELTA = None
if self.LAYER_PARAMS['per_init_to_zero'] > 0:
NUM_ZERO = int(np.floor(np.size(self.WEIGHT) * self.LAYER_PARAMS['per_init_to_zero']))
for i in range(NUM_ZERO):
x = np.random.randint(0, np.size(self.WEIGHT, 0))
y = np.random.randint(0, np.size(self.WEIGHT, 1))
self.WEIGHT[x, y] = 0.0
self.VERB = verbose
self.CONFIG_LOADED = True
