흔한 Layer 와 Building Block

실전 NN 에서 자주 쓰는 layer — 대부분 NNX / Equinox 가 제공. 외워둘 만한 것:

Linear / Dense

# Flax NNX
linear = nnx.Linear(in_features=784, out_features=128, rngs=nnx.Rngs(0))

# Equinox
linear = eqx.nn.Linear(784, 128, key=k)

Conv2d

# NNX
conv = nnx.Conv(
    in_features=3,
    out_features=64,
    kernel_size=(3, 3),
    strides=(1, 1),
    padding="SAME",
    rngs=nnx.Rngs(0),
)

# Equinox
conv = eqx.nn.Conv2d(3, 64, kernel_size=3, padding=1, key=k)

LayerNorm

# NNX
ln = nnx.LayerNorm(num_features=128, rngs=nnx.Rngs(0))

# Equinox
ln = eqx.nn.LayerNorm((128,))

Dropout

# NNX — training flag
class MyModel(nnx.Module):
    def __init__(self, *, rngs):
        self.dense = nnx.Linear(128, 64, rngs=rngs)
        self.dropout = nnx.Dropout(rate=0.5, rngs=rngs)

    def __call__(self, x, *, training=True):
        x = self.dense(x)
        x = self.dropout(x, deterministic=not training)
        return jax.nn.relu(x)

# Equinox — key 로 randomness
class MyModel(eqx.Module):
    dense: eqx.nn.Linear
    dropout: eqx.nn.Dropout

    def __init__(self, key):
        k1, k2 = jax.random.split(key)
        self.dense = eqx.nn.Linear(128, 64, key=k1)
        self.dropout = eqx.nn.Dropout(p=0.5)

    def __call__(self, x, key):
        x = self.dense(x)
        x = self.dropout(x, key=key)
        return jax.nn.relu(x)

Multi-Head Attention

# NNX
attn = nnx.MultiHeadAttention(
    num_heads=8,
    in_features=512,
    rngs=nnx.Rngs(0),
)
out = attn(query=x, key=x, value=x)   # self-attention

# Equinox
attn = eqx.nn.MultiheadAttention(
    num_heads=8,
    query_size=512,
    key=k,
)
out = attn(x, x, x)

Embedding

# NNX
emb = nnx.Embed(num_embeddings=10000, features=512, rngs=nnx.Rngs(0))
y = emb(token_ids)   # token_ids: (B, L) → y: (B, L, 512)

# Equinox
emb = eqx.nn.Embedding(num_embeddings=10000, embedding_size=512, key=k)

Activation 은 함수

jax.nn.relu(x)
jax.nn.gelu(x)
jax.nn.silu(x)   # SwiGLU 의 일부
jax.nn.softmax(x, axis=-1)
jax.nn.log_softmax(x, axis=-1)
jax.nn.tanh(x)
jax.nn.sigmoid(x)

activation 은 — class 가 아니고 그냥 함수. JAX 의 functional 정신.

Residual Connection

class ResBlock(nnx.Module):
    def __init__(self, dim, *, rngs):
        self.ln = nnx.LayerNorm(dim, rngs=rngs)
        self.dense1 = nnx.Linear(dim, 4*dim, rngs=rngs)
        self.dense2 = nnx.Linear(4*dim, dim, rngs=rngs)

    def __call__(self, x):
        residual = x
        x = self.ln(x)
        x = self.dense1(x)
        x = jax.nn.gelu(x)
        x = self.dense2(x)
        return residual + x   # skip connection

이 quest 의 다음 두 lesson 에서 — CNN (10-5) 과 Transformer block (10-6) 을 합성. 이 layer 들이 building block.