def test_collect():
x = tensor.matrix()
mlp = MLP(activations=[Logistic(), Logistic()], dims=[784, 100, 784],
use_bias=False)
cost = SquaredError().apply(x, mlp.apply(x))
cg = ComputationGraph(cost)
var_filter = VariableFilter(roles=[PARAMETER])
W1, W2 = var_filter(cg.variables)
for i, W in enumerate([W1, W2]):
W.set_value(numpy.ones_like(W.get_value()) * (i + 1))
new_cg = collect_parameters(cg, cg.shared_variables)
collected_parameters, = new_cg.shared_variables
assert numpy.all(collected_parameters.get_value()[:784 * 100] == 1.)
assert numpy.all(collected_parameters.get_value()[784 * 100:] == 2.)
assert collected_parameters.ndim == 1
W1, W2 = VariableFilter(roles=[COLLECTED])(new_cg.variables)
assert W1.eval().shape == (784, 100)
assert numpy.all(W1.eval() == 1.)
assert W2.eval().shape == (100, 784)
assert numpy.all(W2.eval() == 2.)
def test_collect():
x = tensor.matrix()
mlp = MLP(activations=[Logistic(), Logistic()], dims=[784, 100, 784],
use_bias=False)
cost = SquaredError().apply(x, mlp.apply(x))
cg = ComputationGraph(cost)
var_filter = VariableFilter(roles=[PARAMETER])
W1, W2 = var_filter(cg.variables)
for i, W in enumerate([W1, W2]):
W.set_value(numpy.ones_like(W.get_value()) * (i + 1))
new_cg = collect_parameters(cg, cg.shared_variables)
collected_parameters, = new_cg.shared_variables
assert numpy.all(collected_parameters.get_value()[:784 * 100] == 1.)
assert numpy.all(collected_parameters.get_value()[784 * 100:] == 2.)
assert collected_parameters.ndim == 1
W1, W2 = VariableFilter(roles=[COLLECTED])(new_cg.variables)
assert W1.eval().shape == (784, 100)
assert numpy.all(W1.eval() == 1.)
assert W2.eval().shape == (100, 784)
assert numpy.all(W2.eval() == 2.)
