Esempio n. 1
0
        def get_layer_monitoring_channels(self, state_below=None,
                                        state=None, targets=None):

            rval = OrderedDict()
            state = state_below

            for layer in self.layers:
                # We don't go through all the inner layers recursively
                state_below = state
                state = layer.fprop(state)
                args = [state_below, state]
                if layer is self.layers[-1] and targets is not None:
                    args.append(targets)
                ch = layer.get_layer_monitoring_channels(*args)
                if not isinstance(ch, OrderedDict):
                    raise TypeError(str((type(ch), layer.layer_name)))
                for key in ch:
                    value = ch[key]
                    doc = get_monitor_doc(value)
                    if doc is None:
                        doc = str(type(layer)) + \
                            ".get_monitoring_channels_from_state did" + \
                            " not provide any further documentation for" + \
                            " this channel."
                    doc = 'This channel came from a layer called "' + \
                            layer.layer_name + '" of an MLP.\n' + doc
                    value.__doc__ = doc
                    rval[layer.layer_name+'_'+key] = value
            
            return rval
Esempio n. 2
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    def get_monitoring_channels(self, data):
        state_below = data
        targets = None
        rval = OrderedDict()
        if self.encoder is not None:
            rval = OrderedDict()

            """
            state = state_below

            for layer in self.layers:
                state_below = state
                state = layer.fprop(state)
                args = [state_below, state]
                if layer is self.layers[-1] and targets is not None:
                    args.append(targets)
                ch = layer.get_layer_monitoring_channels(*args)
                if not isinstance(ch, OrderedDict):
                    raise TypeError(str((type(ch), layer.layer_name)))
                for key in ch:
                    value = ch[key]
                    doc = get_monitor_doc(value)
                    if doc is None:
                        doc = str(type(layer)) + \
                            ".get_monitoring_channels_from_state did" + \
                            " not provide any further documentation for" + \
                            " this channel."
                    doc = 'This channel came from a layer called "' + \
                        layer.layer_name + '" of an MLP.\n' + doc
                    value.__doc__ = doc
                    rval[layer.layer_name + '_' + key] = value
               """
            top_layer = self.layers[-1]
            ch = top_layer.get_layer_monitoring_channels()
            for key, value in ch.iteritems():
                doc = get_monitor_doc(value)
                if doc is None:
                    doc = str(type(top_layer)) + \
                        ".get_monitoring_channels_from_state did" + \
                        " not provide any further documentation for" + \
                        " this channel."
                doc = 'This channel came from a layer called "' + \
                    top_layer.layer_name + '" of an MLP.\n' + doc
                value.__doc__ = doc
                rval[top_layer.layer_name + '_' + key] = value

            Z, log_det_jac = self.get_fprop_and_log_det_jacobian(data)
            prior = self.log_p_z(Z)
            rval['ave_output'] = Z.mean()
            rval['ave_log_det_jac'] = log_det_jac.mean()
            rval['ave_prior'] = prior.mean()
            rval['cumulative_sum'] = self.prior.get_cumulative(Z).sum(axis=1).mean(axis=0)

        return rval
Esempio n. 3
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    def get_layer_monitoring_channels(self,
                                      state_below=None,
                                      state=None,
                                      targets=None):
        """
        Block monitoring channels if not necessary

        Parameters
        ---------
        : todo
        """

        rval = OrderedDict()
        if self.use_monitoring_channels:
            state = state_below
            x = state
            state_conc = None

            for layer in self.layers:
                # We don't go through all the inner layers recursively
                state_below = state
                if ((self.x_shortcut and layer is not self.layers[0]
                     and layer is not self.layers[-1])):
                    state = self.create_shortcut_batch(state, x, 2, 1)
                if self.y_shortcut and layer is self.layers[-1]:
                    state = layer.fprop(state_conc)
                else:
                    state = layer.fprop(state)
                if self.y_shortcut and layer is not self.layers[-1]:
                    if layer is self.layers[0]:
                        state_conc = state
                    else:
                        state_conc = self.create_shortcut_batch(
                            state_conc, state, 2)
                args = [state_below, state]
                if layer is self.layers[-1] and targets is not None:
                    args.append(targets)
                ch = layer.get_layer_monitoring_channels(*args)
                if not isinstance(ch, OrderedDict):
                    raise TypeError(str((type(ch), layer.layer_name)))
                for key in ch:
                    value = ch[key]
                    doc = get_monitor_doc(value)
                    if doc is None:
                        doc = str(type(layer)) + \
                            ".get_monitoring_channels_from_state did" + \
                            " not provide any further documentation for" + \
                            " this channel."
                    doc = 'This channel came from a layer called "' + \
                        layer.layer_name + '" of an MLP.\n' + doc
                    value.__doc__ = doc
                    rval[layer.layer_name + '_' + key] = value

        return rval
Esempio n. 4
0
File: rnn.py Progetto: dwf/pylearn2
    def get_layer_monitoring_channels(self, state_below=None,
                                      state=None, targets=None):
        """
        Block monitoring channels if not necessary

        Parameters
        ---------
        : todo
        """

        rval = OrderedDict()
        if self.use_monitoring_channels:
            state = state_below
            x = state
            state_conc = None

            for layer in self.layers:
                # We don't go through all the inner layers recursively
                state_below = state
                if ((self.x_shortcut and
                    layer is not self.layers[0] and
                        layer is not self.layers[-1])):
                    state = self.create_shortcut_batch(state, x, 2, 1)
                if self.y_shortcut and layer is self.layers[-1]:
                    state = layer.fprop(state_conc)
                else:
                    state = layer.fprop(state)
                if self.y_shortcut and layer is not self.layers[-1]:
                    if layer is self.layers[0]:
                        state_conc = state
                    else:
                        state_conc = self.create_shortcut_batch(state_conc,
                                                                state, 2)
                args = [state_below, state]
                if layer is self.layers[-1] and targets is not None:
                    args.append(targets)
                ch = layer.get_layer_monitoring_channels(*args)
                if not isinstance(ch, OrderedDict):
                    raise TypeError(str((type(ch), layer.layer_name)))
                for key in ch:
                    value = ch[key]
                    doc = get_monitor_doc(value)
                    if doc is None:
                        doc = str(type(layer)) + \
                            ".get_monitoring_channels_from_state did" + \
                            " not provide any further documentation for" + \
                            " this channel."
                    doc = 'This channel came from a layer called "' + \
                        layer.layer_name + '" of an MLP.\n' + doc
                    value.__doc__ = doc
                    rval[layer.layer_name + '_' + key] = value

        return rval