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Task: dl-activation-function

DL Activation Function Design

Research Question

Design a novel activation function for deep convolutional neural networks that improves test accuracy across different architectures (ResNet, VGG) and datasets (CIFAR-10, CIFAR-100).

Background

Activation functions introduce nonlinearity into neural networks and critically affect training dynamics and generalization. Classic choices include:

ReLU (2010): max(0, x) — simple, sparse, but zero gradient for negative inputs ("dying ReLU")
GELU (2016): x * Phi(x) — smooth approximation weighting by Gaussian CDF
Swish/SiLU (2017): x * sigmoid(x) — self-gated, smooth, non-monotonic
Mish (2019): x * tanh(softplus(x)) — self-regularized, smooth

These functions differ in smoothness, gating behavior, and negative-domain behavior, and may interact differently with modern network components such as residual connections and batch normalization.

What You Can Modify

The CustomActivation class (lines 31-48) in custom_activation.py. This is an nn.Module used as a drop-in replacement for ReLU throughout the network.

You can modify:

The forward computation (any element-wise or channel-wise operation)
Learnable parameters (registered in __init__)
The shape of the activation curve (monotonic, non-monotonic, bounded, etc.)
Negative-domain behavior (zero, linear, bounded, learnable)
Any stateless or stateful activation logic

The activation is used in:

ResNet: BasicBlock (2x per block) + initial conv
VGG: after every Conv-BN pair + in the classifier head

Evaluation

Metric: Best test accuracy (%, higher is better)
Architectures & datasets:
- ResNet-20 on CIFAR-10 (shallow residual, 10 classes)
- VGG-16-BN on CIFAR-100 (deep non-residual with BatchNorm, 100 classes)
- MobileNetV2 on FashionMNIST (lightweight inverted-residual with ReLU6 baseline, 10 classes) — hidden, evaluated on final submission only
Training: SGD (lr=0.1, momentum=0.9, wd=5e-4), cosine annealing, 200 epochs
Data augmentation: RandomCrop(32, pad=4) + RandomHorizontalFlip

pytorch-vision/custom_activation.py [EDITABLE — lines 32–49 only]

     1: """CV Activation Function Benchmark.
     2: 
     3: Train vision models (ResNet, VGG, MobileNetV2) on CIFAR-10/100/FashionMNIST
     4: to evaluate custom activation functions.
     5: 
     6: FIXED: Model architectures, data pipeline, training loop.
     7: EDITABLE: CustomActivation class.
     8: 
     9: Usage:
    10:     python custom_activation.py --arch resnet20 --dataset cifar10 --seed 42
    11:     python custom_activation.py --arch mobilenetv2 --dataset fmnist --seed 42
    12: """
    13: 
    14: import argparse
    15: import math
    16: import os
    17: import time
    18: 
    19: import torch
    20: import torch.nn as nn
    21: import torch.nn.functional as F
    22: import torch.optim as optim
    23: import torchvision
    24: import torchvision.t

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "pytorch-vision/custom_activation.py",
4  "start_line": 32,
5  "end_line": 49,
6  "content": "class CustomActivation(nn.Module):\n    \"\"\"Parametric Swish activation with learnable temperature and shift.\n\n    PSwish(x) = x * sigmoid(α * x + β)\n    where α > 0 (via softplus) and β are learnable parameters per activation instance.\n    This generalizes Swish (α=1, β=0) and allows adaptive gating behavior.\n    \"\"\"\n\n    def __init__(self):\n        super().__init__()\n        # Learnable parameters\n        self.theta = nn.Parameter(torch.tensor(0.0))  # unconstrained for α\n        self.beta = nn.Parameter(torch.tensor(0.0))   # shift\n\n    def forward(self, x):\n        alpha = F.softplus(self.theta)  # ensure positivity\n        return x * torch.sigmoid(alpha * x + self.beta)"
7}

Tool ResultStep 1

OK: Replaced lines 32..49 with 17 line(s) in pytorch-vision/custom_activation.py. Editable range: 32–48.

[Current file: pytorch-vision/custom_activation.py | editable: 32–48 | total: 428 lines]
    29| # ============================================================================
    30| 
    31| # -- EDITABLE REGION START (lines 32-49) --------------------------------------
    32  class CustomActivation(nn.Module):
    33      """Parametric Swish activation with learnable temperature and shift.
    34  
    35      PSwish(x) = x * sigmoid(α * x + β)
    36      where α > 0 (via softplus) and β are learnable parameters per activation instance.
    37      This generalizes Swish (α=1, β=0) and allows adaptive gating behavior.
    38      """
    39  
    40      def __init__(self):
    41          super().__init__()
    42          # Learnable parameters
    43          self.theta = nn.Parameter(torch.tensor(0.0))  # unconstrained for α
    44          self.beta = nn.Parameter(torch.t

AssistantStep 2test

Tool ResultStep 2

[Test #1] (2 tests remaining; call submit(n=N) to choose which test result to submit as final)

### resnet20-cifar10 (scripts/resnet20_cifar10.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=0.0804 train_acc=97.31 test_loss=0.3131 test_acc=90.52 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=0.0446 train_acc=98.64 test_loss=0.3103 test_acc=91.32 lr=0.005450
TRAIN_METRICS: epoch=180 train_loss=0.0213 train_acc=99.53 test_loss=0.2910 test_acc=92.43 lr=0.002447
TRAIN_METRICS: epoch=190 train_loss=0.0128 train_acc=99.78 test_loss=0.2793 test_acc=92.79 lr=0.000616
TRAIN_METRICS: epoch=200 train_loss=0.0115 train_acc=99.84 test_loss=0.2779 test_acc=92.87 lr=0.000000
Final metrics (resnet20-cifar10): test_acc_resnet20-cifar10=93.01

### vgg16bn-cifar100 (scripts/vgg16bn_cifar100.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=0.1574 train_acc=95.29 test_loss=1.5851 test_acc=69.12 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=0.0551 train_ac

AssistantStep 3undo

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 3

Undo complete:
Restored: pytorch-vision/custom_activation.py

AssistantStep 4edit

Tool Input

1{
2  "op": "replace",
3  "filename": "pytorch-vision/custom_activation.py",
4  "start_line": 32,
5  "end_line": 49,
6  "content": "class CustomActivation(nn.Module):\n    \"\"\"Learnable Exponential Linear Unit (LELU).\n\n    LELU(x) = x if x >= 0 else α * (exp(x) - 1)\n    where α > 0 is a learnable parameter per activation instance.\n    This generalizes ELU (α=1) and allows adaptive negative saturation level.\n    \"\"\"\n\n    def __init__(self):\n        super().__init__()\n        # Learnable parameter α, initialized to 1.0 (standard ELU)\n        self.log_alpha = nn.Parameter(torch.tensor(0.0))  # log(α)\n\n    def forward(self, x):\n        alpha = torch.exp(self.log_alpha)  # ensures positivity\n        return torch.where(x >= 0, x, alpha * (torch.exp(x) - 1))"
7}

Tool ResultStep 4

OK: Replaced lines 32..49 with 16 line(s) in pytorch-vision/custom_activation.py. Editable range: 32–47.

[Current file: pytorch-vision/custom_activation.py | editable: 32–47 | total: 427 lines]
    29| # ============================================================================
    30| 
    31| # -- EDITABLE REGION START (lines 32-49) --------------------------------------
    32  class CustomActivation(nn.Module):
    33      """Learnable Exponential Linear Unit (LELU).
    34  
    35      LELU(x) = x if x >= 0 else α * (exp(x) - 1)
    36      where α > 0 is a learnable parameter per activation instance.
    37      This generalizes ELU (α=1) and allows adaptive negative saturation level.
    38      """
    39  
    40      def __init__(self):
    41          super().__init__()
    42          # Learnable parameter α, initialized to 1.0 (standard ELU)
    43          self.log_alpha = nn.Parameter(torch.tensor(0.0))  # log(α)
    44  
    45      def forward(self, x):
    46

AssistantStep 5test

Tool ResultStep 5

[Test #2] (1 test remaining; call submit(n=N) to choose which test result to submit as final)

### resnet20-cifar10 (scripts/resnet20_cifar10.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=0.1292 train_acc=95.64 test_loss=0.3142 test_acc=90.33 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=0.0847 train_acc=97.26 test_loss=0.3085 test_acc=90.50 lr=0.005450
TRAIN_METRICS: epoch=180 train_loss=0.0482 train_acc=98.65 test_loss=0.2777 test_acc=91.79 lr=0.002447
TRAIN_METRICS: epoch=190 train_loss=0.0308 train_acc=99.27 test_loss=0.2762 test_acc=91.91 lr=0.000616
TRAIN_METRICS: epoch=200 train_loss=0.0268 train_acc=99.46 test_loss=0.2732 test_acc=92.10 lr=0.000000
Final metrics (resnet20-cifar10): test_acc_resnet20-cifar10=92.19

### vgg16bn-cifar100 (scripts/vgg16bn_cifar100.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=0.1674 train_acc=94.82 test_loss=1.5800 test_acc=69.13 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=0.0714 train_acc

AssistantStep 6undo

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 6

Undo complete:
Restored: pytorch-vision/custom_activation.py

AssistantStep 7edit

Tool Input

1{
2  "op": "replace",
3  "filename": "pytorch-vision/custom_activation.py",
4  "start_line": 32,
5  "end_line": 49,
6  "content": "class CustomActivation(nn.Module):\n    \"\"\"Smooth Leaky ReLU (SLeakyReLU) with learnable mixture.\n\n    SLeakyReLU(x) = x * [β + (1-β) * sigmoid(α*x)]\n    where α > 0 (via softplus) and β ∈ [0,1] (via sigmoid) are learnable.\n    This smoothly interpolates between linear (β=1) and Swish (β=0).\n    The parameter α controls the sharpness of transition.\n    \"\"\"\n\n    def __init__(self):\n        super().__init__()\n        # Learnable parameters\n        self.theta = nn.Parameter(torch.tensor(0.0))  # unconstrained for α\n        self.phi = nn.Parameter(torch.tensor(-float('inf')))  # initialized to β=0 (Swish)\n\n    def forward(self, x):\n        alpha = F.softplus(self.theta)\n        beta = torch.sigmoid(self.phi)  # β ∈ [0,1]\n        return x * (beta + (1 - beta) * torch.sigmoid(alpha * x))"
7}

Tool ResultStep 7

OK: Replaced lines 32..49 with 19 line(s) in pytorch-vision/custom_activation.py. Editable range: 32–50.

[Current file: pytorch-vision/custom_activation.py | editable: 32–50 | total: 430 lines]
    29| # ============================================================================
    30| 
    31| # -- EDITABLE REGION START (lines 32-49) --------------------------------------
    32  class CustomActivation(nn.Module):
    33      """Smooth Leaky ReLU (SLeakyReLU) with learnable mixture.
    34  
    35      SLeakyReLU(x) = x * [β + (1-β) * sigmoid(α*x)]
    36      where α > 0 (via softplus) and β ∈ [0,1] (via sigmoid) are learnable.
    37      This smoothly interpolates between linear (β=1) and Swish (β=0).
    38      The parameter α controls the sharpness of transition.
    39      """
    40  
    41      def __init__(self):
    42          super().__init__()
    43          # Learnable parameters
    44          self.theta = nn.Parameter(torch.tensor(0.0))  # unconstrained for α

AssistantStep 8test

Tool ResultStep 8

[Test #3] (0 tests remaining; call submit(n=N) to choose which test result to submit as final)

[NOTE] This was your last test. You MUST now call submit(n=X) to choose which test result to submit as your final answer.

### resnet20-cifar10 (scripts/resnet20_cifar10.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=nan train_acc=10.00 test_loss=nan test_acc=10.00 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=nan train_acc=10.00 test_loss=nan test_acc=10.00 lr=0.005450
TRAIN_METRICS: epoch=180 train_loss=nan train_acc=10.00 test_loss=nan test_acc=10.00 lr=0.002447
TRAIN_METRICS: epoch=190 train_loss=nan train_acc=10.00 test_loss=nan test_acc=10.00 lr=0.000616
TRAIN_METRICS: epoch=200 train_loss=nan train_acc=10.00 test_loss=nan test_acc=10.00 lr=0.000000
Final metrics (resnet20-cifar10): test_acc_resnet20-cifar10=10.00

### vgg16bn-cifar100 (scripts/vgg16bn_cifar100.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=nan train_acc=1.00 test_l

AssistantStep 9submit

Tool Input

1{
2  "n": 1
3}

Tool ResultStep 9

[submit] Submitting result from test #1 as final.

### resnet20-cifar10 (scripts/resnet20_cifar10.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=0.0804 train_acc=97.31 test_loss=0.3131 test_acc=90.52 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=0.0446 train_acc=98.64 test_loss=0.3103 test_acc=91.32 lr=0.005450
TRAIN_METRICS: epoch=180 train_loss=0.0213 train_acc=99.53 test_loss=0.2910 test_acc=92.43 lr=0.002447
TRAIN_METRICS: epoch=190 train_loss=0.0128 train_acc=99.78 test_loss=0.2793 test_acc=92.79 lr=0.000616
TRAIN_METRICS: epoch=200 train_loss=0.0115 train_acc=99.84 test_loss=0.2779 test_acc=92.87 lr=0.000000
Final metrics (resnet20-cifar10): test_acc_resnet20-cifar10=93.01

### vgg16bn-cifar100 (scripts/vgg16bn_cifar100.sh)
Training progress (last epochs):
TRAIN_METRICS: epoch=160 train_loss=0.1574 train_acc=95.29 test_loss=1.5851 test_acc=69.12 lr=0.009549
TRAIN_METRICS: epoch=170 train_loss=0.0551 train_acc=98.41 test_loss=1.5952 test_acc=71.38 lr=0.