rnnlm.py 文件源码

def __init__(self, n_input, n_hidden, n_output, cell='gru', optimizer='sgd', p=0.5,bptt=-1):
        self.x = T.imatrix('batched_sequence_x')  # n_batch, maxlen 
        self.x_mask = T.fmatrix('x_mask')
        self.y = T.imatrix('batched_sequence_y') 
        self.y_mask = T.fmatrix('y_mask')
        self.n_input = n_input 
        self.n_hidden = n_hidden 
        self.n_output = n_output 

        init_Embd = np.asarray(np.random.uniform(low=-np.sqrt(1. / n_output), high=np.sqrt(1. / n_output), size=(n_output, n_input)), dtype=theano.config.floatX) 

        self.E = theano.shared(value=init_Embd, name='word_embedding',borrow=True) 
        self.cell = cell 
        self.optimizer = optimizer 
        self.p = p
        self.bptt=bptt


        self.is_train = T.iscalar('is_train')

        self.rng = RandomStreams(1234)
        self.build()