utils.py 文件源码

def lstm(xs, ms, s, scope, nh, init_scale=1.0):
    nbatch, nin = [v.value for v in xs[0].get_shape()]
    nsteps = len(xs)
    with tf.variable_scope(scope):
        wx = tf.get_variable("wx", [nin, nh*4], initializer=ortho_init(init_scale))
        wh = tf.get_variable("wh", [nh, nh*4], initializer=ortho_init(init_scale))
        b = tf.get_variable("b", [nh*4], initializer=tf.constant_initializer(0.0))

    c, h = tf.split(axis=1, num_or_size_splits=2, value=s)
    for idx, (x, m) in enumerate(zip(xs, ms)):
        c = c*(1-m)
        h = h*(1-m)
        z = tf.matmul(x, wx) + tf.matmul(h, wh) + b
        i, f, o, u = tf.split(axis=1, num_or_size_splits=4, value=z)
        i = tf.nn.sigmoid(i)
        f = tf.nn.sigmoid(f)
        o = tf.nn.sigmoid(o)
        u = tf.tanh(u)
        c = f*c + i*u
        h = o*tf.tanh(c)
        xs[idx] = h
    s = tf.concat(axis=1, values=[c, h])
    return xs, s