88 int64_t last_out_h_ = 0;
89 int64_t last_out_w_ = 0;
90 bool has_forwarded_ =
false;
Average pooling, rank-4 (N, channels, H, W), matching Conv2DModule's convention. Stride fixed equal t...
Definition avg_pool2d_module.hpp:25
OpType op_type() const override
Pooling per charter's closed OpType set.
Definition avg_pool2d_module.hpp:54
AvgPool2DModule(int64_t kernel_h, int64_t kernel_w, DeviceBackend *backend, float eps=1e-6f)
Constructs an average-pool layer.
std::optional< DeviceType > compute_device() const override
Where this layer computes, so forward() rejects an input on another device (FND-8).
Definition avg_pool2d_module.hpp:71
Tensor forward_impl(const Tensor &input) override
The actual forward computation – per-window mean.
Tensor propagate_relevance(const Tensor &relevance_out, const LRPRuleConfig &config) override
Epsilon/z-rule LRP relevance propagation, weight = 1/K (Bach et al. 2015).
Tensor backward(const Tensor &grad_output) override
Computes the gradient w.r.t. this module's input – uniform 1/K per input position in each window (the...
Vendor-agnostic compute/memory backend. CPUBackend, CUDABackend (Phase 1.5), and HIPBackend (Phase 1....
Definition device_backend.hpp:219
virtual DeviceType device() const noexcept=0
Which device this backend's buffers reside on.
Base class for every layer type (LinearModule, Conv2DModule, activations, ...).
Definition module.hpp:58
N-dimensional tensor. Owns its data buffer exclusively; a DeviceBackend* is injected (not owned) – th...
Definition tensor.hpp:29
Abstract base every layer subclasses – NVI forward(), pure-virtual LRP contract.
Definition acquisition_functions.hpp:16
OpType
The op-type tag a Node carries. Charter Part 2 §3: nodes are tagged by a small closed set of op types...
Definition op_type.hpp:19
Configuration for LRP relevance propagation: which rule a module applies and its hyperparameters....
Definition lrp_rule_config.hpp:57