applications/data/data_fp32_nnlinear.h
Attributes
| Name | |
|---|---|
| network_fp32_t | nn_linear_baseline_t |
| float | nn_linear_baseline_weights_dram |
| float | nn_linear_baseline_biases_dram |
Attributes Documentation
variable nn_linear_baseline_t
network_fp32_t nn_linear_baseline_t = {.IN_CH1 = 28,
.IN_CH2 = 28,
.OUT_CH = 10,
.learning_rate = 0.5,
.dtype = FP32};
variable nn_linear_baseline_weights_dram
static float nn_linear_baseline_weights_dram;
variable nn_linear_baseline_biases_dram
static float nn_linear_baseline_biases_dram = {
0.029830838495251873, -0.027718893800693683, 0.014915538915354814,
0.013144414092396091, 0.026214015226942618, 0.005715480139309234,
-0.016521356227486333, 0.01685057481095459, 0.007825814410188814,
0.031028853769378233};
Source code
// Copyright 2022 ETH Zurich and University of Bologna.
// Licensed under the Apache License, Version 2.0, see LICENSE for details.
// SPDX-License-Identifier: Apache-2.0
#include "network.h"
// This file is used to initialize the network
// parameters with the same values as in the
// Python golden model implementation.
network_fp32_t nn_linear_baseline_t = {.IN_CH1 = 28,
.IN_CH2 = 28,
.OUT_CH = 10,
.learning_rate = 0.5,
.dtype = FP32};
static float nn_linear_baseline_weights_dram[10][784] = {
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0.011225175402720722, -0.024627834579852948, -0.01997264271839906,
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0.032618551756334734, 0.0059236093787837065, 0.0004371297093765777,
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-0.001997644361421318, 0.031234667592362382, 0.0018621332673934446,
0.02963520496739421};
static float nn_linear_baseline_biases_dram[10][1] = {
0.029830838495251873, -0.027718893800693683, 0.014915538915354814,
0.013144414092396091, 0.026214015226942618, 0.005715480139309234,
-0.016521356227486333, 0.01685057481095459, 0.007825814410188814,
0.031028853769378233};
Updated on 2023-06-19 at 09:43:56 +0000