

def test_train_ae():
    '\n    .. todo::\n\n        WRITEME\n    '
    GC = GaussianCorruptor
    gsn = GSN.new(layer_sizes=[ds.X.shape[1], 1000], activation_funcs=['sigmoid', 'tanh'], pre_corruptors=[None, GC(1.0)], post_corruptors=[SaltPepperCorruptor(0.5), GC(1.0)], layer_samplers=[BinomialSampler(), None], tied=False)
    _mbce = MeanBinaryCrossEntropy()
    reconstruction_cost = (lambda a, b: (_mbce.cost(a, b) / ds.X.shape[1]))
    c = GSNCost([(0, 1.0, reconstruction_cost)], walkback=WALKBACK)
    alg = SGD(LEARNING_RATE, init_momentum=MOMENTUM, cost=c, termination_criterion=EpochCounter(MAX_EPOCHS), batches_per_iter=BATCHES_PER_EPOCH, batch_size=BATCH_SIZE, monitoring_dataset=ds, monitoring_batches=10)
    trainer = Train(ds, gsn, algorithm=alg, save_path='gsn_ae_example.pkl', save_freq=5)
    trainer.main_loop()
    print('done training')

dataset/ETHPy150Open lisa-lab/pylearn2/pylearn2/scripts/gsn_example.py test_train_ae/original