def __init__(self, name, x, attn1, attn2, mask, x_in, attn1_in, attn2_in, n_out, path=None, init_func=normal_weight): SuperLayer.__init__(self, name, path) if path is None: self.params['Wx'] = to_shared(init_func((x_in, n_out)), self.name + '_Wx') self.params['Wv'] = to_shared(init_func((n_out, )), self.name + '_Wv') self.params['Wa_1'] = to_shared(init_func((attn1_in, n_out)), self.name + '_Wa_1') self.params['Wa_2'] = to_shared(init_func((attn2_in, n_out)), self.name + '_Wa_2') self.params['b'] = to_shared(init_bias((n_out, )), self.name + '_b') self.output = self.stream(x, attn1, attn2, mask)
def __init__(self, name, x, n_in, n_out, path=None, activation=T.tanh, init_func=normal_weight): SuperLayer.__init__(self, name, path) self.activation = activation if path is None: self.params['W'] = to_shared(init_func((n_in, n_out)), self.name + '_W') self.params['b'] = to_shared(init_bias((n_out,)), self.name + '_b') self.output = self.stream(x)
def __init__(self, name, x, n_voc, ndim, init_value=None, path=None, init_func=normal_weight): SuperLayer.__init__(self, name, path) if path is None: if init_value == None: self.params['E'] = to_shared(init_func((n_voc, ndim)), self.name + '_E') else: self.params['E'] = to_shared(init_value, self.name + '_E') self.output = self.stream(x)
def __init__(self, name, image, filter_shape, image_shape, path=None, init_func=normal_weight): SuperLayer.__init__(self, name, path) self.filter_shape = filter_shape self.image_shape = image_shape if path is None: self.params['W'] = to_shared(init_func(filter_shape), self.name + '_W') self.params['b'] = to_shared(init_bias((filter_shape[0], )), self.name + '_b') self.output = self.stream(image)
def __init__(self, name, x, mask, n_in, hidden_size, n_out, path=None, init_func=normal_weight): SuperLayer.__init__(self, name, path) if path is None: self.params['Wi'] = to_shared(init_func((n_in, hidden_size)), self.name + '_Wi') self.params['Wf'] = to_shared(init_func((n_in, hidden_size)), self.name + '_Wf') self.params['Wo'] = to_shared(init_func((n_in, hidden_size)), self.name + '_Wo') self.params['Wc'] = to_shared(init_func((n_in, hidden_size)), self.name + '_Wc') self.params['Ui'] = to_shared( init_func((hidden_size, hidden_size)), self.name + '_Ui') self.params['Uf'] = to_shared( init_func((hidden_size, hidden_size)), self.name + '_Uf') self.params['Uo'] = to_shared( init_func((hidden_size, hidden_size)), self.name + '_Uo') self.params['Uc'] = to_shared( init_func((hidden_size, hidden_size)), self.name + '_Uc') self.params['bi'] = to_shared(init_bias((n_out, )), self.name + '_bi') self.params['bf'] = to_shared(init_bias((n_out, )), self.name + '_bf') self.params['bo'] = to_shared(init_bias((n_out, )), self.name + '_bo') self.params['bc'] = to_shared(init_bias((n_out, )), self.name + '_bc') self.output = self.stream(x, mask)
def __init__(self, name, x, rate, is_train, path=None): SuperLayer.__init__(self, name, None) self.output = self.stream(x, rate, is_train)
def __init__(self, name, x, pooling_size, path=None): SuperLayer.__init__(self, name, path) self.pooling_size = pooling_size self.output = self.stream(x)