def initialize(self, weight_type="none"):
"""Initialize weights and bias
Parameters
----------
weight_type : string
type of weights: "none", "tanh", "sigmoid"
"""
if self.W==None:
self.W=util.init_weights("W", self.out_dim, self.in_dim, weight_type=weight_type);
if self.use_bias==True and self.bias==None:
self.bias=util.init_weights("bias", self.out_dim, weight_type=weight_type);
def initialize(self, weight_type="none"):
"""Initialize weights and bias
Parameters
----------
weight_type : string
type of weights: "none", "tanh", "sigmoid"
"""
# should have better implementation for convnet weights
fan_in = self.num_channels * np.prod(self.filter_size)
fan_out = self.num_filters * np.prod(self.filter_size)
filter_bound = np.sqrt(6. / (fan_in + fan_out))
filter_shape = (self.num_filters,
self.num_channels) + (self.filter_size)
self.filters = theano.shared(np.asarray(np.random.uniform(
low=-filter_bound, high=filter_bound, size=filter_shape),
dtype='float32'),
borrow=True)
if self.use_bias == True:
self.bias = util.init_weights("bias",
self.num_filters,
weight_type=weight_type)
def initialize(self, weight_type="none"):
"""Initialize weights and bias
Parameters
----------
weight_type : string
type of weights: "none", "tanh", "sigmoid"
"""
# should have better implementation for convnet weights
fan_in = self.num_channels*np.prod(self.filter_size);
fan_out = self.num_filters*np.prod(self.filter_size);
filter_bound=np.sqrt(6./(fan_in + fan_out));
filter_shape=(self.num_filters, self.num_channels)+(self.filter_size);
self.filters = theano.shared(np.asarray(np.random.uniform(low=-filter_bound,
high=filter_bound,
size=filter_shape),
dtype='float32'),
borrow=True);
if self.use_bias==True:
self.bias=util.init_weights("bias", self.num_filters, weight_type=weight_type);
