brainstorm/structure/network.py
def forward_pass(self, training_pass=False, context=None):
        """
        Perform a forward pass on all the provided data.

        Note:
            All the input data to be used during this forward pass have to be
            passed to the network beforehand using
            :meth:`.provide_external_data`

        Args:
            training_pass (Optional[bool]):
                Indicates whether this forward pass belongs to training or not.
                This might change the behaviour of some layers.
            context (Optional[dict]):
                An optional network state as created by net.get_context().
                If provided the network will treat this as if it was the the
                state of the network at the t=-1. This is useful for continuing
                the computations of a recurrent neural network.
                Defaults to None.
        """
        if context is None:
            self._buffer_manager.clear_context()
        else:
            self._buffer_manager.apply_context(context)
        for layer_name, layer in list(self.layers.items())[1:]:
            layer.forward_pass(self.buffer[layer_name], training_pass)

        Perform a forward pass on all the provided data.

        Note:
            All the input data to be used during this forward pass have to be
            passed to the network beforehand using
            :meth:`.provide_external_data`

        Args:
            training_pass (Optional[bool]):
                Indicates whether this forward pass belongs to training or not.
                This might change the behaviour of some layers.
            context (Optional[dict]):
                An optional network state as created by net.get_context().
                If provided the network will treat this as if it was the the
                state of the network at the t=-1. This is useful for continuing
                the computations of a recurrent neural network.
                Defaults to None.
        
Perform a forward pass on all the provided data.
Note:
All the input data to be used during this forward pass have to be
passed to the network beforehand using
:meth:`.provide_external_data`
Perform a forward pass on all the provided data . Note : All the input data to be used during this forward pass have to be passed to the network beforehand using : meth : ` . provide_external_data `
def forward_pass(self, training_pass=False, context=None):
        if context is None:
            self._buffer_manager.clear_context()
        else:
            self._buffer_manager.apply_context(context)
        for layer_name, layer in list(self.layers.items())[1:]:
            layer.forward_pass(self.buffer[layer_name], training_pass)
def forward_pass ( self , training_pass = False , context = None ) : if context is None : self . _buffer_manager . clear_context ( ) else : self . _buffer_manager . apply_context ( context ) for layer_name , layer in list ( self . layers . items ( ) ) [ 1 : ] : layer . forward_pass ( self . buffer [ layer_name ] , training_pass )
Perform a forward pass on all the provided data.
Perform a forward pass on all the provided data .
"""
        Perform a forward pass on all the provided data.

        Note:
            All the input data to be used during this forward pass have to be
            passed to the network beforehand using
            :meth:`.provide_external_data`

        Args:
            training_pass (Optional[bool]):
                Indicates whether this forward pass belongs to training or not.
                This might change the behaviour of some layers.
            context (Optional[dict]):
                An optional network state as created by net.get_context().
                If provided the network will treat this as if it was the the
                state of the network at the t=-1. This is useful for continuing
                the computations of a recurrent neural network.
                Defaults to None.
        """
self
training_pass
Indicates whether this forward pass belongs to training or not.
This might change the behaviour of some layers.
Indicates whether this forward pass belongs to training or not . This might change the behaviour of some layers .
context
An optional network state as created by net.get_context().
If provided the network will treat this as if it was the the
state of the network at the t=-1. This is useful for continuing
the computations of a recurrent neural network.
Defaults to None.
An optional network state as created by net . get_context () . If provided the network will treat this as if it was the the state of the network at the t = - 1 . This is useful for continuing the computations of a recurrent neural network . Defaults to None .