train_mnist_feature_matching_tf.py 文件源码

def lrelu(x, leak=0.2, name="lrelu"):
    """Leaky rectifier.
    """
    with tf.variable_scope(name):
        f1 = 0.5 * (1 + leak)
        f2 = 0.5 * (1 - leak)
        return f1 * x + f2 * abs(x)


# load CIFAR-10
# trainx, trainy = cifar10_data.load(args.data_dir, subset='train')
# trainx = trainx.transpose(0, 2, 3, 1)

# trainx_unl = trainx.copy()
# trainx_unl2 = trainx.copy()

# testx, testy = cifar10_data.load(args.data_dir, subset='test')
# testx = testx.transpose(0, 2, 3, 1)

# nr_batches_train = int(trainx.shape[0]/args.batch_size)
# nr_batches_test = int(testx.shape[0]/args.batch_size)


# load MNIST data