rnn.py 文件源码

def init_h(h_init, X, batch_size, models, **h_args):
    '''Initializes the RNN hidden state.

    Args:
        h_init (str): type of initialization.
        X (T.tensor): input tensor for initialization through MLP.
        batch_size (int)
        models (list): list of Layer, pulls 'h_net' for initialization
            (TODO change this).
        **h_args: kwargs for different initializations.

    Returns:
        T.tensor: Full 3D tensor returned to train `h_net`.

    '''
    if h_init is None:
        h0 = None
    elif h_init == 'last':
        print 'Initializing h0 from chain'
        h0 = theano.shared(np.zeros((batch_size, rnn.dim_h)).astype(floatX))
        h0s = h0[None, :, :]
    elif h_init == 'noise':
        noise_amount = h_args['noise_amount']
        print 'Initializing h0 from noise'
        h0 = trng.normal(avg=0, std=0.1, size=(batch_size, rnn.dim_h)).astype(floatX)
        h0s = h0[None, :, :]
    elif h_init == 'average':
        print 'Initializing h0 from running average'
        averager = models['averager']
        h0 = (T.alloc(0., batch_size, rnn.dim_h) + averager.m[None, :]).astype(floatX)
        h0s = h0[None, :, :]
    elif h_init == 'mlp':
        print 'Initializing h0 from MLP'
        mlp = models['h_net']
        h0s = mlp(X)

    return h0s