def weights_init_func(self, rng, n_inputs, n_outputs):
"""
this function should be rewrote by children classes
default would return all zeros
:param rng: passed random state to generate random values
:param n_inputs: number of inputs
:param n_outputs: number of outputs
"""
return tu.shared_zeros((n_inputs, n_outputs), name='W')
def weights_init_func(self, rng, n_inputs, n_outputs):
"""
this function should be rewrote by children classes
default would return all zeros
:param rng: passed random state to generate random values
:param n_inputs: number of inputs
:param n_outputs: number of outputs
"""
return tu.shared_zeros((n_inputs, n_outputs), name='W')
def init_biases(self):
biases = []
for i, outputs in enumerate(self.layers[1:]):
b = tu.shared_zeros((outputs,), name='b%d' % (i + 1))
biases.append(b)
return biases
def init_weights(self):
weights = []
for i, (inputs, outputs) in enumerate(zip(self.layers[:-1], self.layers[1:])):
w = tu.shared_zeros((inputs, outputs), name='w%d%d' % (i, i + 1))
weights.append(w)
return weights
def init_biases(self):
self.b = tu.shared_zeros(self.get_outputs_shape(), 'b')
def init_biases(self):
self.b = tu.shared_zeros(self.n_feature_map, 'b')
