Python blocks.roles.PARAMETER Examples

The following are 6 code examples of blocks.roles.PARAMETER(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module blocks.roles , or try the search function .
Example #1
Source File: test_graph.py    From attention-lvcsr with MIT License 6 votes vote down vote up 
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.)
Example #2
Source File: run.py    From ladder with MIT License 6 votes vote down vote up 
def setup_model(p):
    ladder = LadderAE(p)
    # Setup inputs
    input_type = TensorType('float32', [False] * (len(p.encoder_layers[0]) + 1))
    x_only = input_type('features_unlabeled')
    x = input_type('features_labeled')
    y = theano.tensor.lvector('targets_labeled')
    ladder.apply(x, y, x_only)

    # Load parameters if requested
    if p.get('load_from'):
        with open(p.load_from + '/trained_params.npz') as f:
            loaded = numpy.load(f)
            cg = ComputationGraph([ladder.costs.total])
            current_params = VariableFilter(roles=[PARAMETER])(cg.variables)
            logger.info('Loading parameters: %s' % ', '.join(loaded.keys()))
            for param in current_params:
                assert param.get_value().shape == loaded[param.name].shape
                param.set_value(loaded[param.name])

    return ladder
Example #3
Source File: base.py    From attention-lvcsr with MIT License 5 votes vote down vote up 
def _setitem(self, key, value):
        if isinstance(value, Variable):
            add_role(value, PARAMETER)
            add_annotation(value, self.brick)
Example #4
Source File: test_variable_filter.py    From attention-lvcsr with MIT License 5 votes vote down vote up 
def test_variable_filter_roles_error():
    # Creating computation graph
    brick1 = Linear(input_dim=2, output_dim=2, name='linear1')

    x = tensor.vector()
    h1 = brick1.apply(x)
    cg = ComputationGraph(h1)
    # testing role error
    VariableFilter(roles=PARAMETER)(cg.variables)
Example #5
Source File: ladder.py    From ladder with MIT License 5 votes vote down vote up 
def shared(self, init, name, cast_float32=True, role=PARAMETER, **kwargs):
        p = self.shareds.get(name)
        if p is None:
            p = shared_param(init, name, cast_float32, role, **kwargs)
            self.shareds[name] = p
        return p
Example #6
Source File: utils.py    From dl4mt-multi with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def init_tparams(params):
    tparams = OrderedDict()
    for kk, pp in params.iteritems():
        tparams[kk] = theano.shared(params[kk], name=kk, borrow=True)
        add_role(tparams[kk], PARAMETER)
    return tparams


# make prefix-appended name