02-20-2020, 12:59 PM
Batch size, that's another one I mess with early on. You pick a small batch, say 32, and the gradients get noisy, which can help escape local minima sometimes. I like that for smaller datasets, gives the model a bit of randomness to explore. But go too big, like 512 or more, and it smooths everything out, faster per epoch but might settle into okay-ish results quicker. On my GPU, I balance it with memory limits, you have to, or it crashes mid-run. Hmmm, and don't forget, larger batches often need a lower learning rate to match.
Number of epochs, oh man, that's tricky because you overdo it and overfitting creeps in. I aim for 50 to 100 at first, watching the curves closely. You stop early if validation loss plateaus, saves time and compute. But sometimes, with regularization, you push to 200 or so, lets the deeper patterns emerge. I use callbacks for that, automatically halts when no improvement shows for ten epochs straight.
Layers and neurons, those architectural hyperparameters shape your whole net. For a simple feedforward, I stack three hidden layers, starting with 128 neurons, then halving each time down to 64, 32. You adjust based on input complexity, more for images, fewer for tabular data. CNNs need filter sizes too, like 3x3 kernels in the beginning layers to catch edges. I experiment with depth, adding skip connections if it gets too deep, prevents vanishing gradients from messing things up.
Activation functions, I stick with ReLU most days, fast and avoids vanishing issues better than sigmoid. But for outputs, softmax if you're classifying multi-way. You might swap to Leaky ReLU if dead neurons pop up, adds a tiny slope to keep things flowing. Or GELU in transformers, smoother gradients there. I test a couple per project, see what boosts accuracy without slowing training.
Dropout rate, crucial for regularization. I apply 0.2 to 0.5 on dense layers, randomly zeros out neurons during training. You don't want it too high, or the model underfits, learns nothing useful. But it forces robustness, less reliance on single paths. In conv layers, I use 0.25, keeps spatial features intact mostly.
Optimizer choice, Adam usually wins for me, adaptive rates make it forgiving. You tune betas if needed, but defaults work fine 80% of the time. SGD with momentum if you want purer gradients, slower but sometimes generalizes better. I switch to RMSprop for RNNs, handles varying scales well. Or try AdamW for weight decay, separates regularization nicely.
Weight initialization, Xavier or He method, depending on activation. I forget it once, weights explode, NaNs everywhere. You set it right, training stabilizes from epoch one. For LSTMs, orthogonal init helps recurrent weights, preserves info over sequences.
L2 regularization, lambda around 0.01, penalizes big weights to smooth the function. You combine with dropout, double whammy against overfitting. But too strong, and it shrinks everything, hurts capacity. I monitor during tuning, adjust if loss doesn't drop enough.
Early stopping patience, say 10 epochs, tied to validation metric. You implement it, avoids wasting cycles on stalled runs. Or learning rate schedulers like reduce on plateau, halves it when stuck. I layer those in, makes hyperparameter search less painful.
For sequence models, sequence length matters, truncate to 100 tokens usually. You pad shorter ones, batch them evenly. Embedding size, 300 dims for words, captures semantics without bloating. Bidirectional flags, set to true for better context, doubles params though.
In GANs, noise dimension, 100 for generators, matches output complexity. You balance discriminator updates, maybe 5:1 ratio to stabilize. Leaky ReLU with 0.2 slope there, prevents dying ReLUs in critics.
Hyperparameter optimization, I use grid search for quick ones like batch size, random search for more, covers space better. Or Bayesian methods if time allows, like Optuna, smart sampling. You parallelize on clusters, speeds it up huge.
Temperature in softmax, 1.0 default, but soften to 0.5 for sharper probs in distillation. You anneal it during training, starts high for exploration, tightens later.
Gradient clipping, norm of 1.0 for RNNs, clips wild updates. I add it when losses spike, keeps things sane. Momentum 0.9 in SGD, carries speed through flats.
Kernel initializer in CNNs, glorot uniform, variances match layers. You vary stride, 2 for downsampling, keeps receptive fields growing.
Batch norm, momentum 0.99, epsilon 1e-5, stabilizes activations. I place after convs, before activations, huge for deep nets. But test without on small data, sometimes overfits.
For VAEs, beta in loss, 1.0 balances recon and KL. You ramp it up gradually, avoids posterior collapse. Latent dim, 20-50, enough for meaningful reps.
In reinforcement learning nets, discount factor gamma 0.99, values future rewards right. Exploration epsilon, decays from 1.0 to 0.01 over episodes. You tune network size smaller here, faster policy updates.
Policy gradient clip, 0.2 in PPO, bounds ratios for stability. Entropy coeff 0.01, encourages exploration without greed.
I could go on, but you get the idea, each hyperparam interacts, so tune iteratively. Start with defaults, perturb one by one, track with logs. Tools like TensorBoard help visualize, see where it breaks. You build intuition over projects, what works for vision differs from NLP.
And for transformers, heads in attention, 8 or 12, parallel subspaces. You scale dim per head, sqrt(d_model) ish. Dropout on attention 0.1, keeps it from memorizing. Layer norm epsilon 1e-6, numerical stability.
Position encoding, sinusoidal fixed, or learned if data varies. Max sequence 512 often, trades off context for speed. Warmup steps 4000 for Adam, ramps learning rate gently.
In diffusion models, noise schedule, linear beta from 1e-4 to 0.02. You sample timesteps uniform, or cosine for better quality. Guidance scale 7.5, amplifies classifier free direction.
Unusual ones, like label smoothing 0.1, softens targets to reduce overconfidence. You use it in classification, improves calibration. Mixup alpha 0.2, blends inputs labels, data aug on fly.
CutMix, patches from images, beta 1.0, augments spatially. I apply during training, boosts gen without extra data.
For meta-learning, inner loop steps 5, outer lr 0.001. You adapt fast, few shots per task.
In federated, local epochs 5, communication rounds 100. Client fraction 0.1, privacy via diff priv noise.
I think that's a solid rundown, covers the commons you hit in most models. Tune smart, validate always, and your nets perform way better.
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