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src/p/y/pylearn2-HEAD/pylearn2/training_algorithms/bgd.py   pylearn2(Download)
        # Methods of `self.cost` need args to be passed in a format compatible
        # with their data_specs
        nested_args = mapping.nest(theano_args)
        fixed_var_descr = self.cost.get_fixed_var_descr(model, nested_args)
        self.on_load_batch = fixed_var_descr.on_load_batch
 
            for on_load_batch in self.on_load_batch:
                on_load_batch(mapping.nest(data))
 
            self.before_step(model)

src/p/y/pylearn2-HEAD/pylearn2/training_algorithms/sgd.py   pylearn2(Download)
        # Methods of `self.cost` need args to be passed in a format compatible
        # with data_specs
        nested_args = mapping.nest(theano_args)
        fixed_var_descr = self.cost.get_fixed_var_descr(model, nested_args)
        self.on_load_batch = fixed_var_descr.on_load_batch

src/p/y/pylearn2-HEAD/pylearn2/monitor.py   pylearn2(Download)
        # Build a nested tuple from ipt, to dispatch the appropriate parts
        # of the ipt batch to each cost
        nested_ipt = mapping.nest(ipt)
 
        custom_channels = {}

src/p/y/pylearn2-HEAD/pylearn2/training_algorithms/default.py   pylearn2(Download)
            # Finally, organize them back into a structure expected by the
            # monitoring channels of the model
            nested_ipt = mapping.nest(ipt)
 
            channels = model.get_monitoring_channels(nested_ipt)

src/s/u/Sum-of-Functions-Optimizer-HEAD/generate_figures/nnet/model_gradient.py   Sum-of-Functions-Optimizer(Download)
        # Methods of `self.cost` need args to be passed in a format compatible
        # with data_specs
        nested_args = mapping.nest(theano_args)
        print self.cost
        fixed_var_descr = self.cost.get_fixed_var_descr(self.model, nested_args)

src/p/y/pylearn2-HEAD/pylearn2/utils/tests/test_data_specs.py   pylearn2(Download)
        flat_data = mapping.flatten(nested_data)
 
        renested_space = mapping.nest(flat_space)
        renested_source = mapping.nest(flat_source)
        renested_data = mapping.nest(flat_data)

src/p/y/pylearn2-HEAD/pylearn2/models/tests/test_dbm.py   pylearn2(Download)
        theano_args.append(arg)
    theano_args = tuple(theano_args)
    nested_args = mapping.nest(theano_args)
 
    grads, updates = cost.get_gradients(model, nested_args)