layers.py 文件源码

def kl_train(z,prior,posterior,hps):
    # push prior through AR layer
    logqs = posterior.logps(z)
    if hps.n_flow > 0:
        nice_layers = []
        print('Does this print')
        for i in range(hps.n_flow):
            nice_layers.append(nice_layer(tf.shape(z),hps,'nice{}'.format(i),ar=hps.ar))

        for i,layer in enumerate(nice_layers):
            z,log_det = layer.forward(z)
            logqs += log_det

    # track the KL divergence after transformation     
    logps = prior.logps(z)
    kl = logqs - logps
    return z, kl

### Autoregressive layers