def build(self):
f_init = self.get_function('weight_init')
f_inner_init = self.get_function('inner_init')
f_bias_init = self.get_function('bias_init')
f_forget_bias_init = self.get_function('forget_bias_init')
_W_i = f_init((self.input_dim, self.output_dim))
_U_i = f_inner_init((self.output_dim, self.output_dim))
_b_i = f_bias_init((self.output_dim, ))
_W_f = f_init((self.input_dim, self.output_dim))
_U_f = f_inner_init((self.output_dim, self.output_dim))
_b_f = f_forget_bias_init((self.output_dim, ))
_W_c = f_init((self.input_dim, self.output_dim))
_U_c = f_inner_init((self.output_dim, self.output_dim))
_b_c = f_bias_init((self.output_dim, ))
_W_o = f_init((self.input_dim, self.output_dim))
_U_o = f_inner_init((self.output_dim, self.output_dim))
_b_o = f_bias_init((self.output_dim, ), )
self.W = TU.shared(numpy.concatenate([_W_i, _W_f, _W_c, _W_o], axis=1),
name=self.name + '_W')
self.U = TU.shared(numpy.concatenate([_U_i, _U_f, _U_c, _U_o], axis=1),
name=self.name + '_U')
self.b = TU.shared(numpy.concatenate([_b_i, _b_f, _b_c, _b_o]),
name=self.name + '_b')
self.params = [self.W, self.U, self.b]
def build(self):
f_init = self.get_function('weight_init')
f_inner_init = self.get_function('inner_init')
f_bias_init = self.get_function('bias_init')
f_forget_bias_init = self.get_function('forget_bias_init')
_W_i = f_init((self.input_dim, self.output_dim))
_U_i = f_inner_init((self.output_dim, self.output_dim))
_b_i = f_bias_init((self.output_dim,))
_W_f = f_init((self.input_dim, self.output_dim))
_U_f = f_inner_init((self.output_dim, self.output_dim))
_b_f = f_forget_bias_init((self.output_dim,))
_W_c = f_init((self.input_dim, self.output_dim))
_U_c = f_inner_init((self.output_dim, self.output_dim))
_b_c = f_bias_init((self.output_dim,))
_W_o = f_init((self.input_dim, self.output_dim))
_U_o = f_inner_init((self.output_dim, self.output_dim))
_b_o = f_bias_init((self.output_dim,),)
self.W = TU.shared(numpy.concatenate([_W_i, _W_f, _W_c, _W_o], axis=1), name=self.name + '_W')
self.U = TU.shared(numpy.concatenate([_U_i, _U_f, _U_c, _U_o], axis=1), name=self.name + '_U')
self.b = TU.shared(numpy.concatenate([_b_i, _b_f, _b_c, _b_o]), name=self.name + '_b')
self.params = [self.W, self.U, self.b]
def build(self):
self.W = TU.shared(self.get_function('weight_init')(
(self.input_dim, self.output_dim)),
name=self.name + '_W')
self.b = TU.shared(self.get_function('bias_init')((self.output_dim, )),
name=self.name + '_b')
self.params = [self.W, self.b]
def build(self):
f_init = self.get_function('weight_init')
f_inner_init = self.get_function('inner_init')
f_bias_init = self.get_function('bias_init')
f_forget_bias_init = self.get_function('forget_bias_init')
'''
Attention hidden dense and projector
'''
self.W_h_att = TU.shared(f_init(
(self.output_dim, self.attention_hidden_dim)),
name=self.name + '_W_h_att')
self.W_x_att = TU.shared(f_init(
(self.input_dim, self.attention_hidden_dim)),
name=self.name + '_W_x_att')
self.W_ctx_att = TU.shared(f_init(
(self.context_dim, self.attention_hidden_dim)),
name=self.name + '_W_ctx_att')
self.b_att = TU.shared(f_bias_init((self.attention_hidden_dim, )),
name=self.name + '_b_att')
self.w_att_prj = TU.shared(f_init((self.attention_hidden_dim, 1)),
name=self.name + '_w_att_prj')
'''
LSTM {W: x, U: h, V: weighted context}
'''
W_i = f_init((self.input_dim, self.output_dim))
V_i = f_init((self.context_dim, self.output_dim))
U_i = f_inner_init((self.output_dim, self.output_dim))
b_i = f_bias_init((self.output_dim, ))
W_f = f_init((self.input_dim, self.output_dim))
V_f = f_init((self.context_dim, self.output_dim))
U_f = f_inner_init((self.output_dim, self.output_dim))
b_f = f_forget_bias_init((self.output_dim, ))
W_c = f_init((self.input_dim, self.output_dim))
V_c = f_init((self.context_dim, self.output_dim))
U_c = f_inner_init((self.output_dim, self.output_dim))
b_c = f_bias_init((self.output_dim, ))
W_o = f_init((self.input_dim, self.output_dim))
V_o = f_init((self.context_dim, self.output_dim))
U_o = f_inner_init((self.output_dim, self.output_dim))
b_o = f_bias_init((self.output_dim, ))
# theano variables
self.W = TU.shared(numpy.concatenate([W_i, W_f, W_c, W_o], axis=1),
name=self.name + '_W')
self.V = TU.shared(numpy.concatenate([V_i, V_f, V_c, V_o], axis=1),
name=self.name + '_V')
self.U = TU.shared(numpy.concatenate([U_i, U_f, U_c, U_o], axis=1),
name=self.name + '_U')
self.b = TU.shared(numpy.concatenate([b_i, b_f, b_c, b_o]),
name=self.name + '_b')
self.params = [
self.W, self.V, self.U, self.b, self.W_h_att, self.W_ctx_att,
self.b_att, self.w_att_prj
]
def build(self):
f_init = self.get_function('weight_init')
f_inner_init = self.get_function('inner_init')
f_bias_init = self.get_function('bias_init')
f_forget_bias_init = self.get_function('forget_bias_init')
'''
Attention hidden dense and projector
'''
self.W_h_att = TU.shared(f_init((self.output_dim, self.attention_hidden_dim)), name=self.name + '_W_h_att')
self.W_x_att = TU.shared(f_init((self.input_dim, self.attention_hidden_dim)), name=self.name + '_W_x_att')
self.W_ctx_att = TU.shared(f_init((self.context_dim, self.attention_hidden_dim)), name=self.name + '_W_ctx_att')
self.b_att = TU.shared(f_bias_init((self.attention_hidden_dim,)), name=self.name + '_b_att')
self.w_att_prj = TU.shared(f_init((self.attention_hidden_dim, 1)), name=self.name + '_w_att_prj')
'''
LSTM {W: x, U: h, V: weighted context}
'''
W_i = f_init((self.input_dim, self.output_dim))
V_i = f_init((self.context_dim, self.output_dim))
U_i = f_inner_init((self.output_dim, self.output_dim))
b_i = f_bias_init((self.output_dim,))
W_f = f_init((self.input_dim, self.output_dim))
V_f = f_init((self.context_dim, self.output_dim))
U_f = f_inner_init((self.output_dim, self.output_dim))
b_f = f_forget_bias_init((self.output_dim,))
W_c = f_init((self.input_dim, self.output_dim))
V_c = f_init((self.context_dim, self.output_dim))
U_c = f_inner_init((self.output_dim, self.output_dim))
b_c = f_bias_init((self.output_dim,))
W_o = f_init((self.input_dim, self.output_dim))
V_o = f_init((self.context_dim, self.output_dim))
U_o = f_inner_init((self.output_dim, self.output_dim))
b_o = f_bias_init((self.output_dim,))
# theano variables
self.W = TU.shared(numpy.concatenate([W_i, W_f, W_c, W_o], axis=1), name=self.name + '_W')
self.V = TU.shared(numpy.concatenate([V_i, V_f, V_c, V_o], axis=1), name=self.name + '_V')
self.U = TU.shared(numpy.concatenate([U_i, U_f, U_c, U_o], axis=1), name=self.name + '_U')
self.b = TU.shared(numpy.concatenate([b_i, b_f, b_c, b_o]), name=self.name + '_b')
self.params = [self.W, self.V, self.U, self.b, self.W_h_att, self.W_ctx_att, self.b_att, self.w_att_prj]
def build(self):
self.W = TU.shared(self.get_function('weight_init')((self.input_dim, self.output_dim)), name=self.name+'_W')
self.b = TU.shared(self.get_function('bias_init')((self.output_dim,)), name=self.name+'_b')
self.params = [self.W, self.b]
