layer_design.py 文件源码

def callback(itr):
    def samplefun(num_samps):
        import numpy as np
        z = np.array(np.random.randn(num_samps, zdim), dtype=np.float32)
        return decode(z).eval(session=sess)
    viz.plot_samples(itr, samplefun, savedir='vae_mnist_samples')

    def sample_z(mu, log_sigmasq, M=5):
        eps = tf.random_normal((M, zdim), dtype=tf.float32)
        return mu + tf.exp(0.5 * log_sigmasq) * eps

    def recons(num_samps):
        # random subset
        subset = X[np.random.choice(X.shape[0], 1)]
        mu, log_sigmasq = encode(subset)
        imgs = decode(sample_z(mu, log_sigmasq, M=24)).eval(session=sess)
        return np.row_stack([subset, imgs])
    viz.plot_samples(itr, recons, savedir='vae_mnist_samples', stub='recon')
    test_lb = test_lb_fun.eval(session=sess) * Ntest
    print "test data VLB: ", np.mean(test_lb)

##########################################
# Make gradient descent fitting function #
##########################################