def __init__(self, inputs, outputs = None, updates = None):
     """
         Standin for a theano function with the given inputs, outputs, updates.
         Here in the __init__ method you give the same expression as usual.
         However, instead of passing __call__ the input variables directly, you pass it batches,
         where each batch is a list containing the inputs for that batch.
         It returns the average value of the function, averaged across batches,
         taking batch size into account. The average of all updates is also applied.
         One extra change: if any of the inputs is a shared variable, then this can
             assign to that variable, while theano.function would refuse to.
             Those shared variables will be left with the value of the last batch when __call__ returns.
     """
     batch_size = T.cast(inputs[0].shape[0], 'float32')
     total_examples = T.scalar()
     transformed_updates = OrderedDict()
     self.has_updates = updates is not None
     if self.has_updates:
         self._clear = function([], updates = [ (var, 0. * var) for var in updates])
         for var in updates:
             update = updates[var]
             transformed_updates[var] = var + (batch_size / total_examples) * update
     self._shared_mask = [ hasattr(elem, 'get_value') for elem in inputs]
     true_inputs = self._true_inputs(inputs)
     self._shared = self._shared_inputs(inputs)
     if outputs is not None:
         if not isinstance(outputs, list):
             outputs = [ outputs ]
         outputs = [ output * (batch_size / total_examples) for output in outputs]
     self._func = function(true_inputs + [total_examples], outputs=outputs, updates=transformed_updates)

def test_vector_to_conv_c01b_invertible():

    """
    Tests that the format_as methods between Conv2DSpace
    and VectorSpace are invertible for the ('c', 0, 1, 'b')
    axis format.
    """

    rng = np.random.RandomState([2013, 5, 1])

    batch_size = 3
    rows = 4
    cols = 5
    channels = 2

    conv = Conv2DSpace([rows, cols], channels = channels, axes = ('c', 0, 1, 'b'))
    vec = VectorSpace(conv.get_total_dimension())

    X = conv.make_batch_theano()
    Y = conv.format_as(X, vec)
    Z = vec.format_as(Y, conv)

    A = vec.make_batch_theano()
    B = vec.format_as(A, conv)
    C = conv.format_as(B, vec)

    f = function([X, A], [Z, C])

    X = rng.randn(*(conv.get_origin_batch(batch_size).shape)).astype(X.dtype)
    A = rng.randn(*(vec.get_origin_batch(batch_size).shape)).astype(A.dtype)

    Z, C = f(X,A)

    np.testing.assert_allclose(Z, X)
    np.testing.assert_allclose(C, A)

    def get_fixed_var_descr(self, model, X, Y):
        """
        .. todo::

            WRITEME
        """

        assert Y is not None

        batch_size = model.batch_size

        drop_mask_X = sharedX(model.get_input_space().get_origin_batch(batch_size))
        drop_mask_X.name = 'drop_mask'

        X_space = model.get_input_space()

        updates = OrderedDict()
        rval = FixedVarDescr()
        inputs=[X, Y]

        if not self.supervised:
            update_X = self.mask_gen(X, X_space = X_space)
        else:
            drop_mask_Y = sharedX(np.ones(batch_size,))
            drop_mask_Y.name = 'drop_mask_Y'
            update_X, update_Y = self.mask_gen(X, Y, X_space)
            updates[drop_mask_Y] = update_Y
            rval.fixed_vars['drop_mask_Y'] =  drop_mask_Y
        if self.mask_gen.sync_channels:
            n = update_X.ndim
            assert n == drop_mask_X.ndim - 1
            update_X.name = 'raw_update_X'
            zeros_like_X = T.zeros_like(X)
            zeros_like_X.name = 'zeros_like_X'
            update_X = zeros_like_X + update_X.dimshuffle(0,1,2,'x')
            update_X.name = 'update_X'
        updates[drop_mask_X] = update_X

        rval.fixed_vars['drop_mask'] = drop_mask_X

        if hasattr(model.inference_procedure, 'V_dropout'):
            include_prob = model.inference_procedure.include_prob
            include_prob_V = model.inference_procedure.include_prob_V
            include_prob_Y = model.inference_procedure.include_prob_Y

            theano_rng = MRG_RandomStreams(2012+11+20)
            for elem in flatten([model.inference_procedure.V_dropout]):
                updates[elem] = theano_rng.binomial(p=include_prob_V, size=elem.shape, dtype=elem.dtype, n=1) / include_prob_V
            if "Softmax" in str(type(model.hidden_layers[-1])):
                hid = model.inference_procedure.H_dropout[:-1]
                y = model.inference_procedure.H_dropout[-1]
                updates[y] = theano_rng.binomial(p=include_prob_Y, size=y.shape, dtype=y.dtype, n=1) / include_prob_Y
            else:
                hid = model.inference_procedure.H_dropout
            for elem in flatten(hid):
                updates[elem] =  theano_rng.binomial(p=include_prob, size=elem.shape, dtype=elem.dtype, n=1) / include_prob

        rval.on_load_batch = [utils.function(inputs, updates=updates)]

        return rval

    def __init__(self, dataset, batch_size, num_batches, topo, targets, rng):


        if rng is None:
            rng = np.random.RandomState([2013, 4, 22])
        if isinstance(rng, list):
            rng = np.random.RandomState(rng)

        self.__dict__.update(locals())
        del self.self

        theano_rng = MRG_RandomStreams(rng.randint(2 ** 16))

        if batch_size is None:
            raise ValueError("must specify batch size, there is infinite data.")

        samples = dataset.s3c.random_design_matrix(batch_size, theano_rng = theano_rng,
                            return_all = targets)
        assert samples is not None
        if targets:
            assert len(samples) == 3
            assert not any(sample is None for sample in samples)
        else:
            assert isinstance(samples, Variable)

        warnings.warn("This is recompiled every time we make a new iterator, just compile it once per iteration mode. Keep in mind the rng is part of the mode though-- the monitor wants to see the same stuff every time.")
        self.f = function([], samples)

        if num_batches is None:
            raise ValueError("must specify a number of batches, there is infinite 'data'")

        self.num_examples = num_batches * batch_size

        def get_fixed_var_descr(self, model, X, Y, **kwargs):
            rval = FixedVarDescr()
            rval.fixed_vars = {'unsup_aux_var': unsup_counter}
            Y=T.matrix()
            theano_func = function([X, Y], updates=[(unsup_counter, unsup_counter + 1)])
            rval.on_load_batch = [theano_func]

            return rval

 def enforce_constraints(self):
     """
     Enforces all constraints encoded by self.modify_updates.
     """
     params = self.get_params()
     updates = OrderedDict(izip_no_length_check(params, params))
     self.modify_updates(updates)
     f = function([], updates=updates)
     f()

 def __init__(self, inputs, outputs = None, updates = None):
     batch_size = T.cast(inputs[0].shape[0], 'float32')
     total_examples = T.scalar()
     transformed_updates = OrderedDict()
     self.has_updates = updates is not None
     if self.has_updates:
         self._clear = function([], updates = [ (var, 0. * var) for var in updates])
         for var in updates:
             update = updates[var]
             transformed_updates[var] = var + (batch_size / total_examples) * update
     self._shared_mask = [ hasattr(elem, 'get_value') for elem in inputs]
     true_inputs = self._true_inputs(inputs)
     self._shared = self._shared_inputs(inputs)
     if outputs is not None:
         if not isinstance(outputs, list):
             outputs = [ outputs ]
         outputs = [ output * (batch_size / total_examples) for output in outputs]
     self._func = function(true_inputs + [total_examples], outputs=outputs, updates=transformed_updates)

        def get_fixed_var_descr(self, model, data):
            data_specs = self.get_data_specs(model)
            data_specs[0].validate(data)
            rval = FixedVarDescr()
            rval.fixed_vars = {'sup_aux_var': sup_counter}

            theano_func = function([], updates=[(sup_counter,
                sup_counter + 1)])
            def on_load(data):
                theano_func()
            rval.on_load_batch = [on_load]
            return rval

def get_obj_func(model):
    X = model.get_input_space().make_batch_theano()
    Y = model.get_output_space().make_batch_theano()
    y = T.argmax(Y, axis=1)
    drop_mask = mask_gen(X, X_space=model.get_input_space())
    if isinstance(model, MLP_Wrapper):
        Q = model.mf_missing(X, drop_mask)
    else:
        Q = model.inference_procedure.do_inpainting(X, Y = T.zeros_like(Y), drop_mask = drop_mask, drop_mask_Y = T.ones_like(T.cast(y, 'float32')))
    Y_hat = Q[-1]
    y_hat = T.argmax(Y_hat, axis=1)
    obj = T.neq(y, y_hat).mean()
    return function([X,Y], obj)

    def get_weights_topo(self):

        if not isinstance(self.input_space, Conv2DSpace):
            raise NotImplementedError()

        W ,= self.transformer.get_params()

        W = W.T

        W = W.reshape((self.dim, self.input_space.shape[0],
                       self.input_space.shape[1], self.input_space.nchannels))

        W = Conv2DSpace.convert(W, self.input_space.axes, ('b', 0, 1, 'c'))

        return function([], W)()

        def get_fixed_var_descr(self, model, data):
            data_specs = self.get_data_specs(model)
            data_specs[0].validate(data)
            rval = FixedVarDescr()
            rval.fixed_vars = {'sup_aux_var': sup_counter}
            rval.data_specs = data_specs

            # data has to be flattened into a tuple before being passed
            # to `function`.
            mapping = DataSpecsMapping(data_specs)
            flat_data = mapping.flatten(data, return_tuple=True)
            theano_func = function(flat_data,
                                 updates=[(sup_counter, sup_counter + 1)])
            # the on_load_batch function will take numerical data formatted
            # as rval.data_specs, so we have to flatten it inside the
            # returned function too.
            # Using default argument binds the variables used in the lambda
            # function to the value they have when the lambda is defined.
            on_load = (lambda batch, mapping=mapping, theano_func=theano_func:
                    theano_func(*mapping.flatten(batch, return_tuple=True)))
            rval.on_load_batch = [on_load]
            return rval

    def setup(self, model, dataset, algorithm):
        self.origin = model.get_param_vector()

        cost = algorithm.cost
        # Cargo cult all the Pascal bullshit needed to evaluate the fucking cost function now
        # =======================================
        data_specs = cost.get_data_specs(model)
        mapping = DataSpecsMapping(data_specs)
        space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
        source_tuple = mapping.flatten(data_specs[1], return_tuple=True)

        # Build a flat tuple of Theano Variables, one for each space.
        # We want that so that if the same space/source is specified
        # more than once in data_specs, only one Theano Variable
        # is generated for it, and the corresponding value is passed
        # only once to the compiled Theano function.
        theano_args = []
        for space, source in safe_zip(space_tuple, source_tuple):
            name = '%s[%s]' % (self.__class__.__name__, source)
            arg = space.make_theano_batch(name=name,
                                          batch_size=self.batch_size)
            theano_args.append(arg)
        theano_args = tuple(theano_args)

        # Methods of `cost` need args to be passed in a format compatible
        # with data_specs
        nested_args = mapping.nest(theano_args)
        fixed_var_descr = cost.get_fixed_var_descr(model, nested_args)
        self.on_load_batch = fixed_var_descr.on_load_batch

        cost_value = cost.expr(model, nested_args,
                                    ** fixed_var_descr.fixed_vars)
        # End cargo culting
        # ======================

        print "Compiling cost function..."
        cost_fn = function(theano_args, cost_value)
        self.cost_fn = cost_fn

    def redo_theano(self):
        """
        Recompiles Theano functions used by this monitor.

        This is needed so that if new channels are added, Theano's
        optimizations make sure (to the extent that they can) that the new
        channels and old channels don't have any redundant calculations.

        It is also needed to regenerate Theano functions after pickling and
        unpickling, since Theano functions should not be pickled.
        """
        self._dirty = False

        init_names = dir(self)
        self.prereqs = OrderedDict()
        for channel in self.channels.values():
            if channel.prereqs is not None:
                dataset = channel.dataset
                if dataset not in self.prereqs:
                    self.prereqs[dataset] = []
                prereqs = self.prereqs[dataset]
                for prereq in channel.prereqs:
                    if prereq not in prereqs:
                        prereqs.append(prereq)

        updates = OrderedDict()
        for channel in self.channels.values():
            updates[channel.val_shared] = np.cast[config.floatX](0.0)
        with log_timing(log, "compiling begin_record_entry"):
            self.begin_record_entry = function(inputs=[], updates=updates, mode=self.theano_function_mode,
                    name = 'Monitor.begin_record_entry')
        updates = OrderedDict()
        givens = OrderedDict()
        #Get the appropriate kind of theano variable to represent the data the model
        #acts on
        X = self.model.get_input_space().make_theano_batch(name = "monitoring_X")
        if config.compute_test_value != 'off':
            m = self.model.get_test_batch_size()
            test_value = self.model.get_input_space().get_origin_batch(m)
            X.tag.test_value = np.cast[X.type.dtype](test_value)
        if self.require_label:
            Y = self.model.get_output_space().make_theano_batch(name = "monitoring_Y")

        log.info('Monitored channels: ')
        for key in sorted(self.channels.keys()):
            mode = self.theano_function_mode
            if mode is not None and hasattr(mode, 'record'):
                mode.record.handle_line('compiling monitor including channel '+key+'\n')
            log.info('\t%s' % key)
        it = [d.iterator(mode=i, num_batches=n, batch_size=b, topo=self.topo) \
              for d, i, n, b in safe_izip(self._datasets, self._iteration_mode,
                                    self._num_batches, self._batch_size)]
        num_examples = [np.cast[config.floatX](float(i.num_examples)) for i in it]
        givens = [OrderedDict() for d in self._datasets]
        updates = [OrderedDict() for d in self._datasets]
        for channel in self.channels.values():
            index = self._datasets.index(channel.dataset)
            d = self._datasets[index]
            g = givens[index]
            n = num_examples[index]
            u = updates[index]
            if isinstance(channel.graph_input, (list, tuple)):
                g[channel.graph_input[0]] = X
                g[channel.graph_input[1]] = Y
            else:
                g[channel.graph_input] = X
            if n == 0:
                raise ValueError("Iterating over 0 examples results in divide by 0")
            if self.topo:
                batch_index = d.get_topo_batch_axis()
            else:
                batch_index = 0
            val = channel.val * T.cast(X.shape[batch_index], config.floatX) / n
            u[channel.val_shared] = channel.val_shared + val

        with log_timing(log, "Compiling accum"):
            # Check type of update expressions
            for up in updates:
                for key in up:
                    if key.dtype != up[key].dtype:
                        raise TypeError('Monitoring channel shared variable ' \
                                + key.name + ' has dtype ' + key.dtype + \
                                ' but is driven by an expression with type ' + \
                                up[key].dtype)

            self.accum = []
            for idx, packed in enumerate(safe_izip(givens, updates)):
                g, u = packed
                mode = self.theano_function_mode
                if mode is not None and hasattr(mode, 'record'):
                    for elem in g:
                        mode.record.handle_line('g key '+var_descriptor(elem)+'\n')
                        mode.record.handle_line('g val '+var_descriptor(g[elem])+'\n')
                    for elem in u:
                        mode.record.handle_line('u key '+var_descriptor(elem)+'\n')
                        mode.record.handle_line('u val '+var_descriptor(u[elem])+'\n')
                function_name = 'Monitor.accum[%d]' % idx
                if self.require_label:
                    if mode is not None and hasattr(mode, 'record'):
                        mode.record.handle_line('compiling supervised accum\n')
#.........这里部分代码省略.........

from galatea.maxout import GCN_C01B2

layer = GCN_C01B2(layer_name='unused')

from pylearn2.space import Conv2DSpace

space = Conv2DSpace(shape=[32, 32], num_channels=3, axes = ('c', 0, 1, 'b'))

layer.set_input_space(space)

from pylearn2.utils import function

X = space.make_batch_theano()

gcn = function([X], layer.fprop(X))

from pylearn2.space import VectorSpace
vector_space = VectorSpace(32*32*3)

flatten = function([X], space.format_as(X, vector_space))

mean = np.zeros((32*32*3,), dtype='float32')
cov = np.zeros((32*32*3, 32*32*3), dtype='float32')

dataset.X = dataset.X.astype('float32')

r_ofs = 8
c_ofs = 8

    def __init__(self, objective, params, inputs=None,
                 param_constrainers=None, max_iter=-1,
                 lr_scalers=None, verbose=0, tol=None,
                 init_alpha=None, min_init_alpha=1e-3,
                 reset_alpha=True, conjugate=False,
                 reset_conjugate=True, gradients=None,
                 gradient_updates=None, line_search_mode=None,
                 accumulate=False, theano_function_mode=None):

        self.__dict__.update(locals())
        del self.self

        if line_search_mode is None:
            if init_alpha is None:
                init_alpha = (.001, .005, .01, .05, .1)
        else:
            assert line_search_mode == 'exhaustive'
            if init_alpha is None:
                init_alpha = (.5, 1.)

        self.init_alpha = tuple([float(elem) for elem in init_alpha])

        if inputs is None:
            inputs = []

        if param_constrainers is None:
            param_constrainers = []

        obj = objective

        self.verbose = verbose

        param_to_grad_sym = OrderedDict()
        param_to_grad_shared = OrderedDict()
        updates = OrderedDict()
        if self.gradient_updates is not None:
            updates.update(self.gradient_updates)

        self.params = [param for param in params]

        for param in params:
            if self.gradients is not None and param in self.gradients:
                g = self.gradients[param]
            else:
                g = grad(objective, param)
            param_to_grad_sym[param] = g
            if param.name is not None:
                param_name = param.name
            else:
                param_name = 'anon_param'
            grad_name = 'BatchGradientDescent.grad_' + param_name
            grad_shared = sharedX(param.get_value() * 0., name=grad_name)
            param_to_grad_shared[param] = grad_shared
            updates[grad_shared] = g

        self.param_to_grad_shared = param_to_grad_shared

        if self.verbose:
            logger.info('batch gradient class compiling gradient function')
        t1 = time.time()
        if self.accumulate:
            self._compute_grad = Accumulator(inputs, updates=updates)
        else:
            self._compute_grad = function(
                inputs,
                updates=updates,
                mode=self.theano_function_mode,
                name='BatchGradientDescent._compute_grad')
        if self.verbose:
            t2 = time.time()
            logger.info('done. Took {0}'.format(t2-t1))

        if self.verbose:
            logger.info('batch gradient class compiling objective function')
        if self.accumulate:
            self.obj = Accumulator(inputs, obj)
        else:
            self.obj = function(inputs, obj, mode=self.theano_function_mode,
                                name='BatchGradientDescent.obj')

        if self.verbose:
            logger.info('done')

        self.param_to_cache = OrderedDict()
        alpha = T.scalar(name='alpha')
        alpha.tag.test_value = np.cast[alpha.dtype](.01)
        cache_updates = OrderedDict()
        goto_updates = OrderedDict()
        for param in params:
            if param.name is None:
                param_name = 'anon_param'
            else:
                param_name = param.name
            cache_name = 'BatchGradientDescent.param_to_cache[%s]' % param_name
            self.param_to_cache[param] = sharedX(param.get_value(borrow=False),
                                                 name=cache_name)
            cache_updates[self.param_to_cache[param]] = param
            cached = self.param_to_cache[param]
            g = self.param_to_grad_shared[param]
            if lr_scalers is not None and param in lr_scalers:
#.........这里部分代码省略.........

    def run(self):
        mm = self.monitor        

        updates = OrderedDict()
        for channel in mm.channels.values():
            updates[channel.val_shared] = np.cast[config.floatX](0.0)        
        mm.begin_record_entry = function(inputs=[], updates=updates, mode=mm.theano_function_mode,
                    name = 'Monitor.begin_record_entry')


        updates = OrderedDict()
        givens = OrderedDict()
        theano_args = mm._flat_data_specs[0].make_theano_batch(
                ['monitoring_%s' % s for s in mm._flat_data_specs[1]])

        # Get a symbolic expression of the batch size
        # We do it here, rather than for each channel, because channels with an
        # empty data_specs do not use data, and are unable to extract the batch
        # size. The case where the whole data specs is empty is not supported.
        batch_size = mm._flat_data_specs[0].batch_size(theano_args)
        nested_theano_args = mm._data_specs_mapping.nest(theano_args)
        if not isinstance(nested_theano_args, tuple):
            nested_theano_args = (nested_theano_args,)        
        
        assert len(nested_theano_args) == (len(mm.channels) + 1)

        for key in sorted(mm.channels.keys()):
            mode = mm.theano_function_mode
            if mode is not None and hasattr(mode, 'record'):
                mode.record.handle_line('compiling monitor including channel '+key+'\n')
            #log.info('\t%s' % key)
        it = [d.iterator(mode=i, num_batches=n, batch_size=b,
                         data_specs=mm._flat_data_specs,
                         return_tuple=True) \
              for d, i, n, b in safe_izip(mm._datasets, mm._iteration_mode,
                                    mm._num_batches, mm._batch_size)]
        mm.num_examples = [np.cast[config.floatX](float(i.num_examples)) for i in it]


        givens = [OrderedDict() for d in mm._datasets]
        updates = [OrderedDict() for d in mm._datasets]

        #for i, channel in enumerate(mm.channels.values()):
        for i, dw_name in enumerate(mm.channels.keys()):            
            if dw_name in self.p_channel:
                channel = mm.channels[dw_name]
                
                index = mm._datasets.index(channel.dataset)
                d = mm._datasets[index]
                g = givens[index]
                cur_num_examples = mm.num_examples[index]
                u = updates[index]

                # Flatten channel.graph_input and the appropriate part of
                # nested_theano_args, to iterate jointly over them.
                c_mapping = DataSpecsMapping(channel.data_specs)
                channel_inputs = c_mapping.flatten(channel.graph_input,
                                                   return_tuple=True)                
                inputs = c_mapping.flatten(nested_theano_args[i + 1],
                                           return_tuple=True)

                for (channel_X, X) in safe_izip(channel_inputs, inputs):
                    assert channel_X not in g or g[channel_X] is X
                    #print channel_X.type , X.type
                    assert channel_X.type == X.type
                    g[channel_X] = X

                if batch_size == 0:
                    # No channel does need any data, so there is not need to
                    # average results, and we will call the accum functions only
                    # once.
                    # TODO: better handling of channels not needing data when
                    # some other channels need data.
                    assert len(mm._flat_data_specs[1]) == 0
                    val = channel.val
                else:
                    if n == 0:
                        raise ValueError("Iterating over 0 examples results in divide by 0")
                    val = (channel.val * T.cast(batch_size, config.floatX)
                            / cur_num_examples)
                u[channel.val_shared] = channel.val_shared + val
            
        mm.accum = []
        for idx, packed in enumerate(safe_izip(givens, updates)):
            g, u = packed
            mode = mm.theano_function_mode
            if mode is not None and hasattr(mode, 'record'):
                for elem in g:
                    mode.record.handle_line('g key '+var_descriptor(elem)+'\n')
                    mode.record.handle_line('g val '+var_descriptor(g[elem])+'\n')
                for elem in u:
                    mode.record.handle_line('u key '+var_descriptor(elem)+'\n')
                    mode.record.handle_line('u val '+var_descriptor(u[elem])+'\n')
            function_name = 'Monitor.accum[%d]' % idx
            if mode is not None and hasattr(mode, 'record'):
                mode.record.handle_line('compiling supervised accum\n')
            # Some channels may not depend on the data, ie, they might just monitor the model
            # parameters, or some shared variable updated by the training algorithm, so we
            # need to ignore the unused input error
            mm.accum.append(function(theano_args,
#.........这里部分代码省略.........

    def redo_theano(self):
        """
        Recompiles Theano functions used by this monitor.

        This is called any time we need to evaluate the channels and
        the channel definitions have changed since last we called it,
        or if the theano functions are unavailable for any other reason
        (first time they are needed after construction or
        deserialization, etc.)

        All channels are compiled as part of the same theano function
        so that the theano optimizations can eliminate subexpressions
        that are shared between multiple channels.
        """
        self._dirty = False

        # Recompute the data specs, since the channels may have changed.
        self._build_data_specs()

        init_names = dir(self)
        self.prereqs = OrderedDict()
        for channel in self.channels.values():
            if channel.prereqs is not None:
                dataset = channel.dataset
                if dataset not in self.prereqs:
                    self.prereqs[dataset] = []
                prereqs = self.prereqs[dataset]
                for prereq in channel.prereqs:
                    if prereq not in prereqs:
                        prereqs.append(prereq)

        updates = OrderedDict()
        for channel in self.channels.values():
            updates[channel.val_shared] = np.cast[config.floatX](0.0)
        with log_timing(log, "compiling begin_record_entry"):
            self.begin_record_entry = function(
                inputs=[],
                updates=updates,
                mode=self.theano_function_mode,
                name='Monitor.begin_record_entry'
            )
        updates = OrderedDict()
        givens = OrderedDict()
        # Get the appropriate kind of theano variable to represent the data
        # the model acts on
        batch_names = ['monitoring_%s' % s for s in self._flat_data_specs[1]]
        theano_args = self._flat_data_specs[0].make_theano_batch(batch_names)

        # Get a symbolic expression of the batch size
        # We do it here, rather than for each channel, because channels with an
        # empty data_specs do not use data, and are unable to extract the batch
        # size. The case where the whole data specs is empty is not supported.
        batch_size = self._flat_data_specs[0].batch_size(theano_args)

        # Also get a nested representation, for joint iteration
        # with each of channel.graph_input
        nested_theano_args = self._data_specs_mapping.nest(theano_args)
        if not isinstance(nested_theano_args, tuple):
            nested_theano_args = (nested_theano_args,)
        assert len(nested_theano_args) == (len(self.channels) + 1)

        log.info('Monitored channels: ')
        for key in sorted(self.channels.keys()):
            mode = self.theano_function_mode
            if mode is not None and hasattr(mode, 'record'):
                mode.record.handle_line('compiling monitor including ' +
                                        'channel ' + key + '\n')
            log.info('\t%s' % key)
        it = [d.iterator(mode=i, num_batches=n, batch_size=b,
                         data_specs=self._flat_data_specs,
                         return_tuple=True)
              for d, i, n, b in safe_izip(self._datasets, self._iteration_mode,
                                          self._num_batches, self._batch_size)]
        self.num_examples = [np.cast[config.floatX](float(i.num_examples))
                             for i in it]
        self.num_examples = [float(i.num_examples) for i in it]

        givens = [OrderedDict() for d in self._datasets]
        updates = [OrderedDict() for d in self._datasets]
        for i, channel in enumerate(self.channels.values()):
            index = self._datasets.index(channel.dataset)
            d = self._datasets[index]
            g = givens[index]
            inv_cur_num_examples = as_floatX(1./self.num_examples[index])
            u = updates[index]

            # Flatten channel.graph_input and the appropriate part of
            # nested_theano_args, to iterate jointly over them.
            c_mapping = DataSpecsMapping(channel.data_specs)
            channel_inputs = c_mapping.flatten(channel.graph_input,
                                               return_tuple=True)
            inputs = c_mapping.flatten(nested_theano_args[i + 1],
                                       return_tuple=True)

            for (channel_X, X) in safe_izip(channel_inputs, inputs):
                assert channel_X not in g or g[channel_X] is X
                assert channel_X.type == X.type, (channel_X.type, X.type)
                g[channel_X] = X

            if batch_size == 0:
#.........这里部分代码省略.........

def get_obj_func(model):
    X = model.get_input_space().make_batch_theano()
    Y = model.get_output_space().make_batch_theano()
    obj = cost(model, X, Y)
    return function([X,Y], obj)

    def get_fixed_var_descr(self, model, data):
        """
        .. todo::

            WRITEME
        """

        X, Y = data

        assert Y is not None

        batch_size = model.batch_size

        drop_mask_X = sharedX(model.get_input_space().get_origin_batch(batch_size))
        drop_mask_X.name = "drop_mask"

        X_space = model.get_input_space()

        updates = OrderedDict()
        rval = FixedVarDescr()
        inputs = [X, Y]

        if not self.supervised:
            update_X = self.mask_gen(X, X_space=X_space)
        else:
            drop_mask_Y = sharedX(np.ones(batch_size))
            drop_mask_Y.name = "drop_mask_Y"
            update_X, update_Y = self.mask_gen(X, Y, X_space)
            updates[drop_mask_Y] = update_Y
            rval.fixed_vars["drop_mask_Y"] = drop_mask_Y
        if self.mask_gen.sync_channels:
            n = update_X.ndim
            assert n == drop_mask_X.ndim - 1
            update_X.name = "raw_update_X"
            zeros_like_X = T.zeros_like(X)
            zeros_like_X.name = "zeros_like_X"
            update_X = zeros_like_X + update_X.dimshuffle(0, 1, 2, "x")
            update_X.name = "update_X"
        updates[drop_mask_X] = update_X

        rval.fixed_vars["drop_mask"] = drop_mask_X

        if hasattr(model.inference_procedure, "V_dropout"):
            include_prob = model.inference_procedure.include_prob
            include_prob_V = model.inference_procedure.include_prob_V
            include_prob_Y = model.inference_procedure.include_prob_Y

            theano_rng = make_theano_rng(None, 2012 + 10 + 20, which_method="binomial")
            for elem in flatten([model.inference_procedure.V_dropout]):
                updates[elem] = (
                    theano_rng.binomial(p=include_prob_V, size=elem.shape, dtype=elem.dtype, n=1) / include_prob_V
                )
            if "Softmax" in str(type(model.hidden_layers[-1])):
                hid = model.inference_procedure.H_dropout[:-1]
                y = model.inference_procedure.H_dropout[-1]
                updates[y] = theano_rng.binomial(p=include_prob_Y, size=y.shape, dtype=y.dtype, n=1) / include_prob_Y
            else:
                hid = model.inference_procedure.H_dropout
            for elem in flatten(hid):
                updates[elem] = (
                    theano_rng.binomial(p=include_prob, size=elem.shape, dtype=elem.dtype, n=1) / include_prob
                )

        rval.on_load_batch = [utils.function(inputs, updates=updates)]

        return rval

    def setup_impl(self, model, dataset, algorithm):
        cost = algorithm.cost

        root = model.get_param_vector()

        dim = root.size

        rng = self.rng


        points = rng.randn(self.num_points, self.num_basis_vectors)
        points = points.astype(root.dtype)
        points *= self.scale

        if self.include_root:
            points[0, :] = 0.

        if not hasattr(self, 'cost_fn'):
            # Cargo cult all the Pascal bullshit needed to evaluate the fucking cost function now
            # =======================================
            data_specs = cost.get_data_specs(model)
            mapping = DataSpecsMapping(data_specs)
            space_tuple = mapping.flatten(data_specs[0], return_tuple=True)
            source_tuple = mapping.flatten(data_specs[1], return_tuple=True)

            # Build a flat tuple of Theano Variables, one for each space.
            # We want that so that if the same space/source is specified
            # more than once in data_specs, only one Theano Variable
            # is generated for it, and the corresponding value is passed
            # only once to the compiled Theano function.
            theano_args = []
            for space, source in safe_zip(space_tuple, source_tuple):
                name = '%s[%s]' % (self.__class__.__name__, source)
                arg = space.make_theano_batch(name=name,
                                              batch_size=self.batch_size)
                theano_args.append(arg)
            theano_args = tuple(theano_args)

            # Methods of `cost` need args to be passed in a format compatible
            # with data_specs
            nested_args = mapping.nest(theano_args)
            fixed_var_descr = cost.get_fixed_var_descr(model, nested_args)
            self.on_load_batch = fixed_var_descr.on_load_batch

            cost_value = cost.expr(model, nested_args,
                                        ** fixed_var_descr.fixed_vars)