コード例 #1
0
ファイル: darn.py プロジェクト: mehdidc/lasagnekit
def test():
    state = 10
    rng = rng_mrg.MRG_RandomStreams(seed=state)
    x_dim = 64
    h_dim = 100
    
    x_in = layers.InputLayer((None, x_dim))
    l_out = Layer(x_in, num_units=h_dim)
    model_encoder = LightweightModel([x_in], [l_out])

    h_in = layers.InputLayer((None, h_dim))
    l_out = Layer(h_in, num_units=x_dim)
    model_decoder = LightweightModel([h_in], [l_out])

    h_in = layers.InputLayer((None, 1))
    l_out = Layer(h_in, num_units=h_dim)
    model_prior = LightweightModel([h_in], [l_out])

    def loss_function(model, tensors):
        X = tensors["X"]
        (h, u), = model_encoder.get_output(X, mode="sample", rng=rng)
        #print(X.ndim, h.ndim)
        (q_h_given_x, _), = model_encoder.get_output(X, mode="evaluate", on=h)
        (p_x_given_h, _),  = model_decoder.get_output(h, mode="evaluate", on=X)
        ones = T.alloc(np.cast[theano.config.floatX](1.), *h.shape)
        zeros = T.alloc(np.cast[theano.config.floatX](0.), *h.shape)
        (p_h, _), = model_prior.get_output(zeros[:, 0:1], mode="evaluate", on=ones)
        L = -((T.log(p_x_given_h).sum(axis=1) + T.log(p_h).sum(axis=1) - T.log(q_h_given_x).sum(axis=1)))
        return (L.mean()), u

    input_variables = OrderedDict(
            X=dict(tensor_type=T.matrix),
    )
    
    functions = dict(
    )

    # sample function
    nb = T.iscalar()
    sample_input = T.alloc(np.cast[theano.config.floatX](0.), nb, 1) 
    from theano.updates import OrderedUpdates
    u = OrderedUpdates()
    (s_h, u_h), = model_prior.get_output(sample_input, mode="sample", rng=rng)
    ones = T.alloc(np.cast[theano.config.floatX](1.), s_h.shape[0], x_dim)
    (s_x, u_x), = model_decoder.get_output(s_h, mode="evaluate", on=ones)
    sample = theano.function([nb], s_x, updates=u_x)
    batch_optimizer = BatchOptimizer(verbose=1, max_nb_epochs=100,
            batch_size=256,
            optimization_procedure=(updates.rmsprop, {"learning_rate": 0.0001})
    )
    class Container(object):
        def __init__(self, models):
            self.models = models
        def get_all_params(self):
            return [p for m in self.models for p in m.get_all_params()]
    models = [model_encoder, model_decoder]
    models = Container(models)
    darn = Capsule(input_variables, 
                   models,
                   loss_function,
                   functions=functions,
                   batch_optimizer=batch_optimizer)

    from sklearn.datasets import load_digits
    def build_digits():
        digits = load_digits()
        X = digits.data
        X = X.astype(np.float32) / 16.
        return X, digits.images.shape[1:]

    X, imshape = build_digits()
    darn.fit(X=X)

    s=(sample(20))
    s = get_2d_square_image_view(s)
    grid_plot(s, imshow_options={"cmap": "gray"})
    plt.savefig("out.png")
コード例 #2
0
ファイル: nade.py プロジェクト: mehdidc/lasagnekit
    X[X > 0.5] = 1.
    X[X <= 0.5] = 0.

    X = shuffle(X)

    order = range(X.shape[1])
    #np.random.shuffle(order)
    X = X[:, order]

    nb_examples = X.shape[0]
    x_dim = X.shape[1]
    h_dim = 500

    x_in = layers.InputLayer((None, x_dim))
    l_out = NadeLayer(x_in, num_units=h_dim)
    model = LightweightModel([x_in], [l_out])

    def loss_function(model, tensors):
        o, = model.get_output(tensors.get("X"))
        return -T.log(o).sum(axis=1).mean()

    input_variables = OrderedDict(
            X=dict(tensor_type=T.matrix),
    )

    functions = dict(
           sample=dict(
               get_output=lambda model, X: model.get_output(X, sampler=True)[0],
               params=["X"]
           ),
           log_likelihood=dict(