def __init__(self, cell, rng, layer_id, shape, X, mask, is_train = 1, batch_size = 1, p = 0.5):

prefix = "SentDecoderLayer_"

layer_id = "_" + layer_id

self.in_size, self.out_size = shape

self.X = X

self.summs = batch_size

self.W_hy = init_weights((self.in_size, self.out_size), prefix + "W_hy" + layer_id)

self.b_y = init_bias(self.out_size, prefix + "b_y" + layer_id)

if cell == "gru":

self.decoder = GRULayer(rng, prefix + layer_id, shape, self.X, mask, is_train, 1, p)

def _active(pre_h, x):

h = self.decoder._active(x, pre_h)

y = T.tanh(T.dot(h, self.W_hy) + self.b_y)

return h, y

[h, y], updates = theano.scan(_active, n_steps = self.summs, sequences = [],

outputs_info = [{'initial':self.X, 'taps':[-1]},

T.alloc(floatX(0.), 1, self.out_size)])

elif cell == "lstm":

self.decoder = LSTMLayer(rng, prefix + layer_id, shape, self.X, mask, is_train, 1, p)

def _active(pre_h, pre_c, x):

h, c = self.decoder._active(x, pre_h, pre_c)

y = T.tanh(T.dot(h, self.W_hy) + self.b_y)

return h, c, y

[h, c, y], updates = theano.scan(_active, n_steps = self.summs, sequences = [],

outputs_info = [{'initial':self.X, 'taps':[-1]},

{'initial':self.X, 'taps':[-1]},

T.alloc(floatX(0.), 1, self.out_size)])

y = T.reshape(y, (self.summs, self.out_size))

self.activation = y