nn.py 文件源码

def sample_from_discretized_mix_logistic(l, nr_mix):
    ls = int_shape(l)
    xs = ls[:-1] + [3]
    # unpack parameters
    logit_probs = l[:, :, :, :nr_mix]
    l = tf.reshape(l[:, :, :, nr_mix:], xs + [nr_mix * 3])
    # sample mixture indicator from softmax
    sel = tf.one_hot(tf.argmax(logit_probs - tf.log(-tf.log(tf.random_uniform(
        logit_probs.get_shape(), minval=1e-5, maxval=1. - 1e-5))), 3), depth=nr_mix, dtype=tf.float32)
    sel = tf.reshape(sel, xs[:-1] + [1, nr_mix])
    # select logistic parameters
    means = tf.reduce_sum(l[:, :, :, :, :nr_mix] * sel, 4)
    log_scales = tf.maximum(tf.reduce_sum(
        l[:, :, :, :, nr_mix:2 * nr_mix] * sel, 4), -7.)
    coeffs = tf.reduce_sum(tf.nn.tanh(
        l[:, :, :, :, 2 * nr_mix:3 * nr_mix]) * sel, 4)
    # sample from logistic & clip to interval
    # we don't actually round to the nearest 8bit value when sampling
    u = tf.random_uniform(means.get_shape(), minval=1e-5, maxval=1. - 1e-5)
    x = means + tf.exp(log_scales) * (tf.log(u) - tf.log(1. - u))
    x0 = tf.minimum(tf.maximum(x[:, :, :, 0], -1.), 1.)
    x1 = tf.minimum(tf.maximum(
        x[:, :, :, 1] + coeffs[:, :, :, 0] * x0, -1.), 1.)
    x2 = tf.minimum(tf.maximum(
        x[:, :, :, 2] + coeffs[:, :, :, 1] * x0 + coeffs[:, :, :, 2] * x1, -1.), 1.)
    return tf.concat([tf.reshape(x0, xs[:-1] + [1]), tf.reshape(x1, xs[:-1] + [1]), tf.reshape(x2, xs[:-1] + [1])], 3)