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")
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(