doctorAI.py 文件源码

def gru_layer(tparams, emb, layerIndex, hiddenDimSize, mask=None):
    timesteps = emb.shape[0]
    if emb.ndim == 3: n_samples = emb.shape[1]
    else: n_samples = 1

    W_rx = T.dot(emb, tparams['W_r_'+layerIndex])
    W_zx = T.dot(emb, tparams['W_z_'+layerIndex])
    Wx = T.dot(emb, tparams['W_'+layerIndex])

    def stepFn(stepMask, wrx, wzx, wx, h):
        r = T.nnet.sigmoid(wrx + T.dot(h, tparams['U_r_'+layerIndex]) + tparams['b_r_'+layerIndex])
        z = T.nnet.sigmoid(wzx + T.dot(h, tparams['U_z_'+layerIndex]) + tparams['b_z_'+layerIndex])
        h_tilde = T.tanh(wx + T.dot(r*h, tparams['U_'+layerIndex]) + tparams['b_'+layerIndex])
        h_new = z * h + ((1. - z) * h_tilde)
        h_new = stepMask[:, None] * h_new + (1. - stepMask)[:, None] * h
        return h_new

    results, updates = theano.scan(fn=stepFn, sequences=[mask,W_rx,W_zx,Wx], outputs_info=T.alloc(numpy_floatX(0.0), n_samples, hiddenDimSize), name='gru_layer'+layerIndex, n_steps=timesteps)

    return results