lstm dimension match in function _step

[XiaoLiuAI]

Hello

I'am learning the deep learning models following your code. I have a question while reading the code for lstm. As I understand, in the function lstm_layer, your input state_below is a 3d tensor with dimension [time_stamp, n_samples, dim_proj], this is used in function _step with theano.scan.

rval, updates = theano.scan(_step,
                                sequences=[mask, state_below],
                                outputs_info=[tensor.alloc(numpy_floatX(0.),
                                                           n_samples,
                                                           dim_proj),
                                              tensor.alloc(numpy_floatX(0.),
                                                           n_samples,
                                                           dim_proj)],
                                name=_p(prefix, '_layers'),
                                n_steps=nsteps)

But in the function _step, I saw

preact = tensor.dot(h_, tparams[_p(prefix, 'U')])
preact += x_
i = tensor.nnet.sigmoid(_slice(preact, 0, options['dim_proj']))
f = tensor.nnet.sigmoid(_slice(preact, 1, options['dim_proj']))
o = tensor.nnet.sigmoid(_slice(preact, 2, options['dim_proj']))
c = tensor.tanh(_slice(preact, 3, options['dim_proj']))
c = f * c_ + i * c
c = m_[:, None] * c + (1. - m_)[:, None] * c_
h = o * tensor.tanh(c)
h = m_[:, None] * h + (1. - m_)[:, None] * h_
return h, c

It seems that h_ has the dimension [n_samples, dim_proj] and x_ is state_below, how can the h returned by this function keeps the same dimension and contains the values computed based on x_k in step k?

[XiaoLiuAI]

I understand the code. I am sorry to trouble you. Please close this issue.

[nouiz]

No problem.