31 void set(
const Index = 0,
Loss* =
nullptr);
Trainable loss function attached to a NeuralNetwork and a Dataset.
Definition loss.h:44
Stack of Layers forming a trainable model.
Definition neural_network.h:44
Definition adaptive_moment_estimation.h:19
@ CUDA
Definition configuration.h:16
vector< Shape > per_layer_output_delta_shapes
Definition back_propagation.h:44
vector< vector< TensorView > > gradient_views
Definition back_propagation.h:38
Buffer gradient
Definition back_propagation.h:37
Shape output_delta_dimensions
Definition back_propagation.h:60
virtual ~BackPropagation()=default
vector< vector< BackwardEdge > > backward_edges
Definition back_propagation.h:45
BackPropagation(const Index=0, Loss *=nullptr)
Index active_tokens_count
Definition back_propagation.h:58
vector< vector< vector< TensorView > > > delta_views
Definition back_propagation.h:41
void accumulate_output_deltas(size_t layer_index)
NeuralNetwork * neural_network
Definition back_propagation.h:35
float accuracy
Definition back_propagation.h:56
Index batch_size
Definition back_propagation.h:51
float loss_value
Definition back_propagation.h:57
Buffer per_layer_output_deltas
Definition back_propagation.h:43
float error
Definition back_propagation.h:55
void set(const Index=0, Loss *=nullptr)
Buffer backward
Definition back_propagation.h:40
TensorView get_output_deltas() const
Loss * loss
Definition back_propagation.h:53
Definition back_propagation.h:20
size_t port
Definition back_propagation.h:22
size_t consumer_idx
Definition back_propagation.h:21
Definition tensor_utilities.h:144
Definition tensor_utilities.h:46
Definition tensor_utilities.h:236