Caffe2 - Python API
A deep learning, cross platform ML framework
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caffe2.python.rnn_cell.MultiRNNCell Class Reference
Inheritance diagram for caffe2.python.rnn_cell.MultiRNNCell:
caffe2.python.rnn_cell.RNNCell

Public Member Functions

def __init__ (self, cells, residual_output_layers=None, kwargs)
 
def layer_scoper (self, layer_id)
 
def prepare_input (self, model, input_blob)
 
def get_state_names (self)
 
def get_output_state_index (self)
 
- Public Member Functions inherited from caffe2.python.rnn_cell.RNNCell
def __init__ (self, name=None, forward_only=False, initializer=None)
 
def initializer (self)
 
def initializer (self, value)
 
def scope (self, name)
 
def apply_over_sequence (self, model, inputs, seq_lengths=None, initial_states=None, outputs_with_grads=None)
 
def apply (self, model, input_t, seq_lengths, states, timestep)
 
def apply_override (self, model, input_t, seq_lengths, timestep, extra_inputs=None)
 
def prepare_input (self, model, input_blob)
 
def get_output_state_index (self)
 
def get_state_names (self)
 
def get_state_names_override (self)
 
def get_output_dim (self)
 

Public Attributes

 cells
 
 residual_output_layers
 
 output_connected_layers
 
 output_indices
 
 state_names
 
 initializer
 
- Public Attributes inherited from caffe2.python.rnn_cell.RNNCell
 name
 
 recompute_blobs
 
 forward_only
 

Detailed Description

Multilayer RNN via the composition of RNNCell instance.

It is the resposibility of calling code to ensure the compatibility
of the successive layers in terms of input/output dimensiality, etc.,
and to ensure that their blobs do not have name conflicts, typically by
creating the cells with names that specify layer number.

Assumes first state (recurrent output) for each layer should be the input
to the next layer.

Definition at line 926 of file rnn_cell.py.

Constructor & Destructor Documentation

def caffe2.python.rnn_cell.MultiRNNCell.__init__ (   self,
  cells,
  residual_output_layers = None,
  kwargs 
)
cells: list of RNNCell instances, from input to output side.

name: string designating network component (for scoping)

residual_output_layers: list of indices of layers whose input will
be added elementwise to their output elementwise. (It is the
responsibility of the client code to ensure shape compatibility.)
Note that layer 0 (zero) cannot have residual output because of the
timing of prepare_input().

forward_only: used to construct inference-only network.

Definition at line 939 of file rnn_cell.py.


The documentation for this class was generated from the following file: