Metadata-Version: 2.4
Name: nn3d
Version: 0.1.0
Summary: Sinir aglarini tarayicida canli 3D olarak gorsellestir - Keras/TensorFlow icin
Project-URL: Homepage, https://github.com/gamedeveloperxyz/neuralnetwork3d
Author-email: gamedeveloperxyz <gamedeveloperxyz@gmail.com>
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License-File: LICENSE
Keywords: 3d,keras,neural-network,tensorflow,visualization,webgl
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.9
Provides-Extra: dev
Requires-Dist: pytest>=7; extra == 'dev'
Provides-Extra: keras
Requires-Dist: keras>=3.0; extra == 'keras'
Requires-Dist: numpy>=1.22; extra == 'keras'
Description-Content-Type: text/markdown

# nn3d

Sinir aglarini tarayicida **canli 3D** olarak izle. Egitim sirasinda her katmanin
aktivasyonu ve her baglantinin agirligi gercek zamanli akar.

```python
import nn3d

nn3d.show(model)                                       # tarayici acilir
model.fit(X, y, callbacks=[nn3d.Monitor(X[:1])])       # egitim canli akar
```

Notebook'ta calisiyorsan baska hicbir sey gerekmez. Duz bir `.py` betiginde
betik bitince surec de biter; sonuna `nn3d.wait()` ekle.

## Kurulum

```bash
pip install -e .
```

Kullanmak icin Node.js **gerekmez** — 3D cizici derlenmis halde paketin icinde
gelir. Node yalnizca ciziciyi degistirmek istersen lazim (asagi bak).

## Ne gorursun

- **Katman kartlari** — ad, noron/kanal sayisi, tensor sekli, `[ReLU]` gibi aktivasyon
- **Noronlar** — parlaklik ve buyukluk o anki aktivasyonu gosterir
- **Baglantilar** — turkuaz pozitif, kizil negatif agirlik; parlaklik `|agirlik|` × kaynak aktivasyonu
- **Giris/cikis etiketleri** — ozellik adlarin ("Hunger Level", "Fox Distance") aktivasyona gore renklenir
- **Fare ile uzerine gel** — o noronun butun baglantilari altin sariya doner, gerisi soner
- **Metrikler** — `loss`, `accuracy`, `val_loss`… sag ustte canli

## API

### `nn3d.show(model, ...)`

Modeli cizer ve tarayiciyi acar. `View` dondurur.

| parametre | ne ise yarar |
|---|---|
| `sample` | verilirse ilk kare hemen gonderilir, ekran bos kalmaz |
| `input_labels` | giris noronlarinin adlari (ozellik sutunlarin) |
| `output_labels` | cikis noronlarinin adlari (sinif adlarin) |
| `max_neurons` | katman basina cizilecek nokta sayisi (varsayilan 16) |
| `port` | varsayilan 8092; doluysa otomatik bos porta gecer |
| `open_browser` | `False` yaparsan sadece URL basar |

### `nn3d.Monitor(sample, every=20, ...)`

Keras callback'i. `model.fit(callbacks=[...])` icine koy.

`every` her N batch'te bir kare gonderir. Her batch'te ekstra ileri yayilim
yapmak egitimi olculebilir sekilde yavaslatir; 20 akici gorunur ve maliyeti
ihmal edilebilir.

### `View.update(x, metrics=..., epoch=...)`

Kendi dongunu yaziyorsan (RL, ozel egitim adimi) kareyi elle gonder.

## Sanallastirma — ekranda gordugun gercek mi?

Evet, ama **hepsi degil**. `Dense(3072)` katmanini 3072 nokta cizmek hem okunmaz
hem de onceki katmanla arasinda 2.3 milyon kenar demektir; tarayici kilitlenir.

Bu yuzden her katmandan esit araliklarla `max_neurons` kadar **temsilci** noron
secilir; aktivasyonlar ve agirliklar ayni indislere gore kirpilir. Yani ekrandaki
her nokta ve her cizgi gercek bir noron/agirliktir — uydurma yok, sadece bir alt
kume. Bir noronun uzerine geldiginde ipucu kutusu **gercek noron numarasini**
yazar (`noron 1847 / 3072`), boylece hangi alt kumeye baktigini bilirsin.

## Desteklenen katmanlar (Keras 3)

`Dense` · `Conv1D/2D/3D` · `Conv2DTranspose` · `SeparableConv2D` · `LSTM` · `GRU` ·
`SimpleRNN` · `Flatten` · `Reshape` · `Dropout` · `BatchNormalization` ·
`LayerNormalization` · `MaxPooling*` · `AveragePooling*` · `Activation` ·
`LeakyReLU` · `ReLU` · `Softmax` · `Embedding`

Agirliklar gorsellestirme icin `(giris_birimi, cikis_birimi)` matrisine indirgenir:
konvolusyon cekirdekleri uzamsal eksenler uzerinde toplanir, LSTM/GRU kapilari
ortalanir. Amac sayisal dogruluk degil, **baglantinin isareti ve siddeti**.

## Mimari

```
Keras modeli ──build_graph()──▶ graph.json ──┐
                                              ├─▶ stdlib HTTP + SSE ──▶ Three.js cizici
ActivationTap.read(x) ──────▶ kare (base64 f32) ┘         (:8092)          (tarayici)
```

- **Sifir bagimlilik.** Sunucu tamamen Python standart kutuphanesi. Akis icin
  WebSocket yerine SSE kullaniliyor: akis tek yonlu (Python → tarayici), SSE tam
  bunun icin var, `http.server` ile calisir ve baglanti koptugunda kendi kendine
  yeniden baglanir. WebSocket ya harici bir paket ya da el yazmasi handshake
  isterdi; ikisi de "`pip install nn3d` yeter" hedefini bozardi.
- **Cizici modeli bilmez**, sadece `src/nn3d/schema.py`'deki semayi bilir. Bu
  yuzden ayni kod hem 10 noronluk bir oyun ajanini hem 12 blokluk bir
  transformer'i cizer. Baska bir cerceve (PyTorch, Unity, JS oyun) eklemek icin
  yeni bir cizici degil, sadece yeni bir adapter yazmak yeterli.
- **Tek geometri.** Butun kenarlar tek bir `LineSegments`, butun noronlar tek bir
  `Points`. Her karede yalnizca renk tamponu guncellenir; geometri hic degismez.

## Ciziciyi degistirmek

```bash
cd viewer
npm install
npm run dev      # canli gelistirme (once Python sunucusunu ayri calistir)
npm run build    # src/nn3d/static/index.html icine derler
```

Derlenmis cikti pakete dahildir, bu yuzden `npm run build` ciktisini da
commit'lemek gerekir.

## Ornekler

| dosya | ne |
|---|---|
| `examples/nn3d_baslangic.ipynb` | Bastan sona rehber: statik gorunum, canli egitim, elle kare gonderme, sanallastirma |
| `examples/churn_canli.py` | Duz betik ornegi (`nn3d.wait()` kullanimi) |
| `examples/kurs/` | FNN / CNN / RNN / GAN kurs notebook'lari, nn3d eklenmis hali |

```bash
PYTHONPATH=src python examples/churn_canli.py
```
