def __init__(self, dim, w_param=None, **kwargs):
super(RNNLM, self).__init__(name=generate_name('hidden', 1),
user_type='kHidden')
if w_param == None:
# default: uniform
w_param = Parameter(name=generate_name('w'), init=init)
else:
set_param_field(w_param.param, 'w', True, **kwargs)
setval(self.layer, param=w_param.param)
def __init__(self, dim, w_param=None, **kwargs):
super(RNNLM, self).__init__(name=generate_name('hidden', 1),
user_type='kHidden')
if w_param == None:
# default: uniform
w_param = Parameter(name=generate_name('w'), init=init)
else:
set_param_field(w_param.param, 'w', True, **kwargs)
setval(self.layer, param=w_param.param)
def __init__(self, in_dim, out_dim, w_param=None, **kwargs):
super(Embedding, self).__init__(name=generate_name('embedding', 1),
user_type='kEmbedding')
fields = {'vocab_size': in_dim, 'word_dim': out_dim}
setval(self.layer.Extensions[embedding_conf], **fields)
if w_param == None:
# default: uniform
w_param = Parameter(name=generate_name('w'), init=init)
else:
set_param_field(w_param.param, 'w', True, **kwargs)
setval(self.layer, param=w_param.param)
def __init__(self, in_dim, out_dim, w_param=None, **kwargs):
super(Embedding, self).__init__(name=generate_name('embedding', 1),
user_type='kEmbedding')
fields = {'vocab_size': in_dim,
'word_dim': out_dim}
setval(self.layer.Extensions[embedding_conf], **fields)
if w_param == None:
# default: uniform
w_param = Parameter(name=generate_name('w'), init=init)
else:
set_param_field(w_param.param, 'w', True, **kwargs)
setval(self.layer, param=w_param.param)
def __init__(self, nb_filter=0, kernel=0, stride=1, pad=0,
init=None, w_param=None, b_param=None,
activation=None, **kwargs):
'''
required
nb_filter = (int) // the number of filters
kernel = (int) // the size of filter
optional
stride = (int) // the size of stride
pad = (int) // the size of padding
init = (string) // 'unirom', 'gaussian', 'constant'
w_param = (Parameter) // Parameter object for weight
b_param = (Parameter) // Parameter object for bias
**kwargs (KEY=VALUE)
w_lr = (float) // learning rate multiplier for weight, used to
// scale the learning rate when updating parameters.
w_wd = (float) // weight decay multiplier for weight, used to
// scale the weight decay when updating parameters.
b_lr = (float) // learning rate multiplier for bias
b_wd = (float) // weight decay multiplier for bias
'''
assert nb_filter > 0 and kernel > 0, 'should be set as positive int'
super(Convolution2D, self).__init__(name=generate_name('conv', 1),
type=kCConvolution)
fields = {'num_filters' : nb_filter,
'kernel' : kernel,
'stride' : stride,
'pad' : pad}
setval(self.layer.convolution_conf, **fields)
# parameter w
if w_param == None:
self.init = 'gaussian' if init == None else init
w_param = Parameter(init=self.init)
set_param_field(w_param.param, 'w', True, **kwargs)
setval(self.layer, param=w_param.param)
# parameter b
if b_param == None:
self.init = 'constant' if init == None else init
b_param = Parameter(init=self.init) # default: constant
set_param_field(b_param.param, 'b', True, **kwargs)
setval(self.layer, param=b_param.param)
# following layers: e.g., activation, dropout, etc.
if activation:
self.mask = Activation(activation=activation).layer
def __init__(self,
output_dim=0,
activation=None,
init=None,
w_param=None,
b_param=None,
input_dim=None,
**kwargs):
'''
required
output_dim = (int)
optional
activation = (string)
init = (string) // 'uniform', 'gaussian', 'constant'
w_param = (Parameter) // Parameter object for weight
b_param = (Parameter) // Parameter object for bias
**kwargs
w_lr = (float) // learning rate multiplier for weight, used to
// scale the learning rate when updating parameters.
w_wd = (float) // weight decay multiplier for weight, used to
// scale the weight decay when updating parameters.
b_lr = (float) // learning rate multiplier for bias
b_wd = (float) // weight decay multiplier for bias
'''
# required
assert output_dim > 0, 'output_dim should be set'
super(Dense, self).__init__(type=kInnerProduct, **kwargs)
self.layer.innerproduct_conf.num_output = output_dim
if 'transpose' in kwargs:
self.layer.innerproduct_conf.transpose = kwargs['transpose']
# parameter w (default: gaussian)
if w_param == None:
self.init = 'gaussian' if init == None else init
w_param = Parameter(init=self.init)
set_param_field(w_param.param, 'w', False, **kwargs)
setval(self.layer, param=w_param.param)
# parameter b (default: constant)
if b_param == None:
self.init = 'constant' if init == None else init
b_param = Parameter(init=self.init)
set_param_field(b_param.param, 'b', False, **kwargs)
setval(self.layer, param=b_param.param)
# following layers: e.g., activation, dropout, etc.
if activation:
self.mask = Activation(activation=activation).layer
def __init__(self, output_dim=0, activation=None,
init=None, w_param=None, b_param=None, input_dim=None,
**kwargs):
'''
required
output_dim = (int)
optional
activation = (string)
init = (string) // 'unirom', 'gaussian', 'constant'
w_param = (Parameter) // Parameter object for weight
b_param = (Parameter) // Parameter object for bias
**kwargs
w_lr = (float) // learning rate multiplier for weight, used to
// scale the learning rate when updating parameters.
w_wd = (float) // weight decay multiplier for weight, used to
// scale the weight decay when updating parameters.
b_lr = (float) // learning rate multiplier for bias
b_wd = (float) // weight decay multiplier for bias
'''
# required
assert output_dim > 0, 'output_dim should be set'
super(Dense, self).__init__(type=kInnerProduct, **kwargs)
self.layer.innerproduct_conf.num_output = output_dim
if 'transpose' in kwargs:
self.layer.innerproduct_conf.transpose = kwargs['transpose']
# parameter w (default: gaussian)
if w_param == None:
self.init = 'gaussian' if init == None else init
w_param = Parameter(init=self.init)
set_param_field(w_param.param, 'w', False, **kwargs)
setval(self.layer, param=w_param.param)
# parameter b (default: constant)
if b_param == None:
self.init = 'constant' if init == None else init
b_param = Parameter(init=self.init)
set_param_field(b_param.param, 'b', False, **kwargs)
setval(self.layer, param=b_param.param)
# following layers: e.g., activation, dropout, etc.
if activation:
self.mask = Activation(activation=activation).layer
def __init__(self, out_dim=None, w_param=None, b_param=None,
sampling=None, **kwargs):
'''
Generate a set of layers (like MLP) according to the number of elements
in out_dim, and on top of it, two layers RBMVis and RBMHid with
bidirectional connection
The layers are expanded and added in Energy.add()
required
out_dim = (int) or (int list) // the number of hidden nodes
optional
w_param = (Parameter) // Parameter object for weight
b_param = (Parameter) // Parameter object for bias
sampling = (string)
'''
assert out_dim > 0, 'out_dim should be set'
self.out_dim = [out_dim] if type(out_dim) == int else out_dim
self.name = kwargs['name'] if 'name' in kwargs else 'RBMVis'
self.layer_type = kwargs['type'] if 'type' in kwargs else kRBMVis
super(RBM, self).__init__(name=generate_name(self.name,
withnumber=False), type=self.layer_type)
setval(self.layer.rbm_conf, hdim=self.out_dim[-1])
if self.layer_type == kRBMHid and sampling != None:
if sampling == 'gaussian':
setval(self.layer.rbm_conf, gaussian=True)
# parameter w
if w_param == None:
w_param = Parameter(init='gaussian', **kwargs)
set_param_field(w_param.param, 'w', withnumber=False,
level=len(self.out_dim), **kwargs)
else:
if self.layer_type == kRBMHid:
del kwargs['name']
else:
set_param_field(w_param.param, 'w', withnumber=False,
level=len(self.out_dim), **kwargs)
setval(self.layer, param=w_param.param)
# parameter b
if b_param == None:
b_param = Parameter(init='constant', **kwargs)
set_param_field(b_param.param, 'b', withnumber=False,
level=len(self.out_dim), **kwargs)
else:
if self.layer_type == kRBMHid:
pass
else:
set_param_field(b_param.param, 'b', withnumber=False,
level=len(self.out_dim), **kwargs)
setval(self.layer, param=b_param.param)
if self.layer_type == kRBMVis:
wname = w_param.param.name
parw = Parameter(name=wname+"_", init='none', share_from=wname)
bname = b_param.param.name
parb = Parameter(name=bname+"2", wd=0, init='constant')
self.bidirect = RBM(self.out_dim, name='RBMHid', type=kRBMHid,
w_param=parw, b_param=parb, sampling=sampling).layer
def __init__(self,
nb_filter=0,
kernel=0,
stride=1,
pad=0,
init=None,
w_param=None,
b_param=None,
activation=None,
**kwargs):
'''
required
nb_filter = (int) // the number of filters
kernel = (int/tuple) // the size of filter
optional
stride = (int/tuple) // the size of stride
pad = (int/tuple) // the size of padding
init = (string) // 'uniform', 'gaussian', 'constant'
w_param = (Parameter) // Parameter object for weight
b_param = (Parameter) // Parameter object for bias
**kwargs (KEY=VALUE)
w_lr = (float) // learning rate multiplier for weight, used to
// scale the learning rate when updating parameters.
w_wd = (float) // weight decay multiplier for weight, used to
// scale the weight decay when updating parameters.
b_lr = (float) // learning rate multiplier for bias
b_wd = (float) // weight decay multiplier for bias
'''
assert nb_filter > 0, 'nb_filter should be set as positive int'
super(Convolution2D, self).__init__(name=generate_name('conv', 1),
type=kCConvolution)
fields = {}
# for kernel
if type(kernel) == int:
fields['kernel'] = kernel
else:
fields['kernel_x'] = kernel[0]
fields['kernel_y'] = kernel[1]
# for stride
if type(stride) == int:
fields['stride'] = stride
else:
fields['stride_x'] = stride[0]
fields['stride_y'] = stride[1]
# for pad
if type(pad) == int:
fields['pad'] = pad
else:
fields['pad_x'] = pad[0]
fields['pad_y'] = pad[1]
setval(self.layer.convolution_conf, **fields)
# parameter w
if w_param == None:
self.init = 'gaussian' if init == None else init
w_param = Parameter(init=self.init)
set_param_field(w_param.param, 'w', True, **kwargs)
setval(self.layer, param=w_param.param)
# parameter b
if b_param == None:
self.init = 'constant' if init == None else init
b_param = Parameter(init=self.init) # default: constant
set_param_field(b_param.param, 'b', True, **kwargs)
setval(self.layer, param=b_param.param)
# following layers: e.g., activation, dropout, etc.
if activation:
self.mask = Activation(activation=activation).layer
def __init__(self,
out_dim=None,
w_param=None,
b_param=None,
sampling=None,
**kwargs):
'''
Generate a set of layers (like MLP) according to the number of elements
in out_dim, and on top of it, two layers RBMVis and RBMHid with
bidirectional connection
The layers are expanded and added in Energy.add()
required
out_dim = (int) or (int list) // the number of hidden nodes
optional
w_param = (Parameter) // Parameter object for weight
b_param = (Parameter) // Parameter object for bias
sampling = (string)
'''
assert out_dim > 0, 'out_dim should be set'
self.out_dim = [out_dim] if type(out_dim) == int else out_dim
self.name = kwargs['name'] if 'name' in kwargs else 'RBMVis'
self.layer_type = kwargs['type'] if 'type' in kwargs else kRBMVis
super(RBM, self).__init__(name=generate_name(self.name,
withnumber=False),
type=self.layer_type)
setval(self.layer.rbm_conf, hdim=self.out_dim[-1])
if self.layer_type == kRBMHid and sampling != None:
if sampling == 'gaussian':
setval(self.layer.rbm_conf, gaussian=True)
# parameter w
if w_param == None:
w_param = Parameter(init='gaussian', **kwargs)
set_param_field(w_param.param,
'w',
withnumber=False,
level=len(self.out_dim),
**kwargs)
else:
if self.layer_type == kRBMHid:
del kwargs['name']
else:
set_param_field(w_param.param,
'w',
withnumber=False,
level=len(self.out_dim),
**kwargs)
setval(self.layer, param=w_param.param)
# parameter b
if b_param == None:
b_param = Parameter(init='constant', **kwargs)
set_param_field(b_param.param,
'b',
withnumber=False,
level=len(self.out_dim),
**kwargs)
else:
if self.layer_type == kRBMHid:
pass
else:
set_param_field(b_param.param,
'b',
withnumber=False,
level=len(self.out_dim),
**kwargs)
setval(self.layer, param=b_param.param)
if self.layer_type == kRBMVis:
wname = w_param.param.name
parw = Parameter(name=wname + "_", init='none', share_from=wname)
bname = b_param.param.name
parb = Parameter(name=bname + "2", wd=0, init='constant')
self.bidirect = RBM(self.out_dim,
name='RBMHid',
type=kRBMHid,
w_param=parw,
b_param=parb,
sampling=sampling).layer
def __init__(self, nb_filter=0, kernel=0, stride=1, pad=0,
init=None, w_param=None, b_param=None,
activation=None, **kwargs):
'''
required
nb_filter = (int) // the number of filters
kernel = (int/tuple) // the size of filter
optional
stride = (int/tuple) // the size of stride
pad = (int/tuple) // the size of padding
init = (string) // 'uniform', 'gaussian', 'constant'
w_param = (Parameter) // Parameter object for weight
b_param = (Parameter) // Parameter object for bias
**kwargs (KEY=VALUE)
w_lr = (float) // learning rate multiplier for weight, used to
// scale the learning rate when updating parameters.
w_wd = (float) // weight decay multiplier for weight, used to
// scale the weight decay when updating parameters.
b_lr = (float) // learning rate multiplier for bias
b_wd = (float) // weight decay multiplier for bias
'''
assert nb_filter > 0, 'nb_filter should be set as positive int'
super(Convolution2D, self).__init__(name=generate_name('conv', 1),
type=kCConvolution, **kwargs)
fields = {"num_filters":nb_filter}
# for kernel
if type(kernel) == int:
fields['kernel'] = kernel
else:
fields['kernel_x'] = kernel[0]
fields['kernel_y'] = kernel[1]
# for stride
if type(stride) == int:
fields['stride'] = stride
else:
fields['stride_x'] = stride[0]
fields['stride_y'] = stride[1]
# for pad
if type(pad) == int:
fields['pad'] = pad
else:
fields['pad_x'] = pad[0]
fields['pad_y'] = pad[1]
setval(self.layer.convolution_conf, **fields)
# parameter w
if w_param == None:
self.init = 'gaussian' if init == None else init
w_param = Parameter(init=self.init)
set_param_field(w_param.param, 'w', True, **kwargs)
setval(self.layer, param=w_param.param)
# parameter b
if b_param == None:
self.init = 'constant' if init == None else init
b_param = Parameter(init=self.init) # default: constant
set_param_field(b_param.param, 'b', True, **kwargs)
setval(self.layer, param=b_param.param)
# following layers: e.g., activation, dropout, etc.
if activation:
self.mask = Activation(activation=activation).layer
