def __init__(self, activator_type="linear",
input_dim = None, output_dim = None,
W=None, b=None,
):
# `W` is initialized with `W_values` which is uniformely sampled
# from sqrt(-6./(n_in+n_hidden)) and sqrt(6./(n_in+n_hidden))
# for tanh activation function
# the output of uniform if converted using asarray to dtype
# theano.config.floatX so that the code is runable on GPU
# Note : optimal initialization of weights is dependent on the
# activation function used (among other things).
# For example, results presented in [Xavier10] suggest that you
# should use 4 times larger initial weights for sigmoid
# compared to tanh
# We have no info for other function, so we use the same as
# tanh.
assert activator_type is not None, "Activation must be provided"
self.activator_type = activator_type
self.activator = get_activation(self.activator_type)
if input_dim is not None and output_dim is not None:
if W is None:
rng = get_numpy_rng()
W = numpy.asarray(rng.uniform(
low=-numpy.sqrt(6. / (input_dim + output_dim)),
high=numpy.sqrt(6. / (input_dim + output_dim)),
size=(input_dim, output_dim)), dtype=theano.config.floatX)
if self.activator == theano.tensor.nnet.sigmoid:
W *= 4
else:
assert input_dim == W.shape[0] and input_dim == W.shape[1]
if b is None:
b = numpy.zeros((output_dim,), dtype=theano.config.floatX)
else:
assert output_dim == b.shape[0]
self.W = theano.shared(value=W, borrow=True)
self.b = theano.shared(value=b, borrow=True)
self.input_dim_, self.output_dim_ = W.shape
elif W is not None and b is not None:
self.W = theano.shared(value=W, borrow=True)
self.b = theano.shared(value=b, borrow=True)
self.input_dim_, self.output_dim_ = W.shape
else:
raise Exception("Perception Layer needs parameter "
"in pair of (W,b) or (n_in, n_out) besides activation")
def __init__(self,
activator_type="linear",
tensor_shape = None,
init_W=None,
init_b=None, ):
assert activator_type is not None, "Activation must be provided"
self.activator_type = activator_type
self.activator = get_activation(self.activator_type)
if init_W is not None and tensor_shape is not None:
assert init_W.shape == tensor_shape, "init tensor size is not equal to the given tensor shape"
# Input: a 4D tensor corresponding to a mini-batch of input images with shape:
# [mini-batch size, number of input feature maps, image height, image width].
# Weight: a 4D tensor with shape :
# [number of feature maps at layer m, number of feature maps at layer m-1, filter height, filter width]
rng = get_numpy_rng()
if init_W is None and tensor_shape is None:
raise Exception("neither W now tensor shape is provided.")
elif init_W is not None:
self.W = theano.shared(init_W.astype(theano.config.floatX), borrow=True)
self.tensor_shape = init_W.shape
elif init_W is None:
self.tensor_shape = tensor_shape
(output_feature_map_num, input_feature_map_num, conv_window , filter_width) = tensor_shape
w_bound = np.sqrt(input_feature_map_num * filter_width)
init_W = rng.uniform(low=-1.0 / w_bound, high=1.0 / w_bound, size=tensor_shape)
self.W = theano.shared(init_W.astype(theano.config.floatX), borrow=True)
if init_b is None and tensor_shape is None:
raise Exception("neither b now tensor shape is provided.")
elif init_b is not None:
self.b = theano.shared(init_b.astype(theano.config.floatX), borrow=True)
elif init_b is None:
(output_feature_map_num, input_feature_map_num, conv_window , filter_width) = tensor_shape
b_shape = (output_feature_map_num,)
init_b = rng.uniform(low=-.5, high=.5, size=b_shape)
self.b = theano.shared(init_b.astype(theano.config.floatX), borrow=True)
def __init__(self, name, class_num, trans_mat_prior = None):
""" Initialize the parameters of the logistic regression
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
assert isinstance(name, str) and len(name) > 0
self.name = name
rng = get_numpy_rng()
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
# self.W = theano.shared(value=np.asarray(rng.uniform(low=-2.0, high=2.0, size=(n_in, n_out)),
# dtype=theano.config.floatX),
# name='W', borrow=True)
# initialize the baises b as a vector of n_out 0s
# self.b = theano.shared(value=np.asarray(rng.uniform(low=-2.0, high=2.0, size=(n_out,)),
# dtype=theano.config.floatX),
# name='b', borrow=True)
# trasition matrix of class tags
# A_{i,j} means the transition prob from class i to class j
# A_{0, i} means the prob of start with class i
# self.tag_trans_matrix = theano.shared(value = np.asarray(rng.uniform(low=-2.0, high=2.0, size=(class_num + 1 ,class_num )),
# dtype=theano.config.floatX),
# name='tag_trans', borrow = True)
if trans_mat_prior == None:
self.tag_trans_matrix = theano.shared(value = np.zeros((class_num + 1 ,class_num ), dtype=theano.config.floatX),
name='path_layer_trans_%s' % (self.name), borrow = True)
else:
self.tag_trans_matrix = theano.shared(value = np.asarray(trans_mat_prior, dtype=theano.config.floatX),
name='path_layer_trans_%s' % (self.name), borrow = True)
def __init__(self,
input_dim = None, output_dim = None,
W = None, b = None):
""" Initialize the parameters of the logistic regression
:type input: theano.tensor.TensorType
:param input: symbolic variable that describes the input of the
architecture (one minibatch)
:type n_in: int
:param n_in: number of input units, the dimension of the space in
which the datapoints lie
:type n_out: int
:param n_out: number of output units, the dimension of the space in
which the labels lie
"""
# param_valid = (W is not None and b is not None ) or (n_in is not None and n_out is not None)
# assert param_valid, "The construction param is not valid"
if input_dim is not None and output_dim is not None:
if W is None:
rng = get_numpy_rng()
W = numpy.asarray(rng.uniform(
low=-numpy.sqrt(6. / (input_dim + output_dim)),
high=numpy.sqrt(6. / (input_dim + output_dim)),
size=(input_dim, output_dim)), dtype=theano.config.floatX)
if self.activator == theano.tensor.nnet.sigmoid:
W *= 4
else:
assert input_dim == W.shape[0] and input_dim == W.shape[1]
if b is None:
b = numpy.zeros((output_dim,), dtype=theano.config.floatX)
else:
assert output_dim == b.shape[0]
self.W = theano.shared(value=W, borrow=True)
self.b = theano.shared(value=b, borrow=True)
self.input_dim_, self.output_dim_ = W.shape
elif W is not None and b is not None:
self.W = theano.shared(value=W, borrow=True)
self.b = theano.shared(value=b, borrow=True)
self.input_dim_, self.output_dim_ = W.shape
else:
raise Exception("Perception Layer needs parameter "
"in pair of (W,b) or (n_in, n_out) besides activation")
if W:
self.W = theano.shared(value=W.astype(theano.config.floatX),
name='softmax_W_%s' % (self.name), borrow=True)
elif (n_in is not None and n_out is not None) :
# initialize with 0 the weights W as a matrix of shape (n_in, n_out)
rng = get_numpy_rng()
W_values = numpy.asarray(rng.uniform(
low=-numpy.sqrt(6. / (n_in + n_out)),
high=numpy.sqrt(6. / (n_in + n_out)),
size=(n_in, n_out)), dtype=theano.config.floatX)
self.W = theano.shared(value=W_values,
name='softmax_W_%s' % (self.name), borrow=True)
if b:
self.b = theano.shared(value=b.astype(theano.config.floatX),
name='softmax_b_%s' % (self.name), borrow=True)
elif (n_in is not None and n_out is not None) :
# initialize the baises b as a vector of n_out 0s
self.b = theano.shared(value=numpy.zeros((n_out,),
dtype=theano.config.floatX),
name='softmax_b_%s' % (self.name), borrow=True)