2 files changed, 25 insertions, 3 deletions
diff --git a/src/text_recognizer/networks/transformer/__init__.py b/src/text_recognizer/networks/transformer/__init__.py
index 020a917..9febc88 100644
--- a/src/text_recognizer/networks/transformer/__init__.py
+++ b/src/text_recognizer/networks/transformer/__init__.py
@@ -1,3 +1,3 @@
 """Transformer modules."""
 from .positional_encoding import PositionalEncoding
-from .transformer import Decoder, Encoder, Transformer
+from .transformer import Decoder, Encoder, EncoderLayer, Transformer
diff --git a/src/text_recognizer/networks/transformer/transformer.py b/src/text_recognizer/networks/transformer/transformer.py
index c6e943e..dd180c4 100644
--- a/src/text_recognizer/networks/transformer/transformer.py
+++ b/src/text_recognizer/networks/transformer/transformer.py
@@ -6,11 +6,25 @@ import numpy as np
 import torch
 from torch import nn
 from torch import Tensor
+import torch.nn.functional as F
 
 from text_recognizer.networks.transformer.attention import MultiHeadAttention
 from text_recognizer.networks.util import activation_function
 
 
+class GEGLU(nn.Module):
+    """GLU activation for improving feedforward activations."""
+
+    def __init__(self, dim_in: int, dim_out: int) -> None:
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Forward propagation."""
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
 def _get_clones(module: Type[nn.Module], num_layers: int) -> nn.ModuleList:
     return nn.ModuleList([copy.deepcopy(module) for _ in range(num_layers)])
 
@@ -36,9 +50,17 @@ class _ConvolutionalLayer(nn.Module):
         activation: str = "relu",
     ) -> None:
         super().__init__()
+
+        in_projection = (
+            nn.Sequential(
+                nn.Linear(hidden_dim, expansion_dim), activation_function(activation)
+            )
+            if activation != "glu"
+            else GEGLU(hidden_dim, expansion_dim)
+        )
+
         self.layer = nn.Sequential(
-            nn.Linear(in_features=hidden_dim, out_features=expansion_dim),
-            activation_function(activation),
+            in_projection,
             nn.Dropout(p=dropout_rate),
             nn.Linear(in_features=expansion_dim, out_features=hidden_dim),
         )