⬡ JSTCalc
Beranda
Kalkulator CNN
◈ Konvolusi 2D ◉ Gambar → Matriks ⊡ Pooling Layer μ Normalisasi ∿ Konvolusi 1D
Kalkulator RNN
⟳ Forward Pass RNN ⊞ Forward Pass LSTM
Kalkulator MLP
∇ Backpropagation MLP σ Fungsi Aktivasi ⇗ Optimizer ⊗ Softmax & Cross-Entropy ⊛ Attention Mechanism ⊘ Dropout & Regularisasi ⊕ Inisialisasi Bobot
Konsep JST
Beranda CNN ◈ Konvolusi 2D ◉ Gambar → Matriks ⊡ Pooling Layer μ Normalisasi ∿ Konvolusi 1D RNN ⟳ Forward Pass RNN ⊞ Forward Pass LSTM MLP ∇ Backpropagation σ Fungsi Aktivasi ⇗ Optimizer ⊗ Softmax & Cross-Entropy ⊛ Attention Mechanism ⊘ Dropout & Regularisasi ⊕ Inisialisasi Bobot Konsep JST
μ Norm

Kalkulator Normalisasi

Hitung BatchNorm, LayerNorm, GroupNorm, dan Instance Norm step-by-step.
x̂ = (x − μ) / √(σ² + ε)  ·  γ + β

⊕ Konfigurasi
Jenis Normalisasi
Dimensi Data
Batch (N)
3
Channel (C)
4
Groups (G)
2
Parameter
ε (epsilon)
γ (scale)
β (shift)
Input Data (N×C)
γ / β per-Channel
⊕ Konfigurasi
Data (N=2, C baris baru)
ε
∇ Backprop BatchNorm
Data (satu feature, N nilai)
Upstream ∂L/∂y (N nilai)
γ
ε
Masukkan data lalu klik ▶ Hitung Gradien.
⊞ Inference Mode
Running Statistics (dari training)
μ running
σ² running
γ
β
ε
Input x (satu sampel)
Klik ▶ Hitung untuk melihat hasil inference mode.
⬡ JSTCalc Kalkulator Jaringan Saraf Tiruan — PHP MVC
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