How do you choose the right layer type (Linear, Conv, Attention) for a given input modality?

Each layer type encodes different structural assumptions (inductive biases) about the data. Using a layer whose assumptions match the data's structure allows the model to learn faster and with less data than a generic alternative.

import torch
import torch.nn as nn

# Tabular data: simple MLP
mlp = nn.Sequential(
    nn.Linear(30, 128), nn.ReLU(), nn.Dropout(0.2),
    nn.Linear(128, 64), nn.ReLU(),
    nn.Linear(64, 1)
)

# Image: CNN
cnn = nn.Sequential(
    nn.Conv2d(3, 32, 3, padding=1), nn.BatchNorm2d(32), nn.ReLU(),
    nn.MaxPool2d(2),
    nn.Conv2d(32, 64, 3, padding=1), nn.BatchNorm2d(64), nn.ReLU(),
    nn.AdaptiveAvgPool2d(1),   # global average pooling -> (B, 64, 1, 1)
    nn.Flatten(),
    nn.Linear(64, 10)
)

# Text: embedding + transformer encoder
encoder_layer = nn.TransformerEncoderLayer(
    d_model=256, nhead=4, dim_feedforward=512,
    dropout=0.1, batch_first=True
)
text_model = nn.Sequential(
    nn.Embedding(10000, 256),
    nn.TransformerEncoder(encoder_layer, num_layers=4)
)

# Time series: Conv1d (local patterns) or LSTM (sequential patterns)
ts_cnn = nn.Conv1d(in_channels=1, out_channels=32, kernel_size=5, padding=2)
ts_rnn = nn.LSTM(input_size=1, hidden_size=64, batch_first=True)