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(3,[tf.reshape(x0,xs[:-1]+[1]), tf.reshape(x1,xs[:-1]+[1]), tf.reshape(x2,xs[:-1]+[1])])