5 files changed, 12 insertions, 6 deletions
diff --git a/src/text_recognizer/networks/ctc.py b/src/text_recognizer/networks/ctc.py
index 72f18b8..2493d5c 100644
--- a/src/text_recognizer/networks/ctc.py
+++ b/src/text_recognizer/networks/ctc.py
@@ -24,7 +24,7 @@ def greedy_decoder(
         target_lengths (Optional[Tensor]): Length of each target tensor. Defaults to None.
         character_mapper (Optional[Callable]): A emnist/character mapper for mapping integers to characters.  Defaults
             to None.
-        blank_label (int): The blank character to be ignored. Defaults to 79.
+        blank_label (int): The blank character to be ignored. Defaults to 80.
         collapse_repeated (bool): Collapase consecutive predictions of the same character. Defaults to True.
 
     Returns:
diff --git a/src/text_recognizer/networks/line_lstm_ctc.py b/src/text_recognizer/networks/line_lstm_ctc.py
index 988b615..5c57479 100644
--- a/src/text_recognizer/networks/line_lstm_ctc.py
+++ b/src/text_recognizer/networks/line_lstm_ctc.py
@@ -33,8 +33,9 @@ class LineRecurrentNetwork(nn.Module):
         self.hidden_size = hidden_size
         self.encoder = self._configure_encoder(encoder)
         self.flatten = flatten
+        self.fc = nn.Linear(in_features=self.input_size, out_features=self.hidden_size)
         self.rnn = nn.LSTM(
-            input_size=self.input_size,
+            input_size=self.hidden_size,
             hidden_size=self.hidden_size,
             num_layers=num_layers,
         )
@@ -73,6 +74,9 @@ class LineRecurrentNetwork(nn.Module):
         # Avgerage pooling.
         x = reduce(x, "(b t) c h w -> t b c", "mean", b=b, t=t) if self.flatten else x
 
+        # Linear layer between CNN and RNN
+        x = self.fc(x)
+
         # Sequence predictions.
         x, _ = self.rnn(x)
 
diff --git a/src/text_recognizer/networks/misc.py b/src/text_recognizer/networks/misc.py
index cac9e78..1f853e9 100644
--- a/src/text_recognizer/networks/misc.py
+++ b/src/text_recognizer/networks/misc.py
@@ -34,6 +34,7 @@ def activation_function(activation: str) -> Type[nn.Module]:
     """Returns the callable activation function."""
     activation_fns = nn.ModuleDict(
         [
+            ["elu", nn.ELU(inplace=True)],
             ["gelu", nn.GELU()],
             ["leaky_relu", nn.LeakyReLU(negative_slope=1.0e-2, inplace=True)],
             ["none", nn.Identity()],
diff --git a/src/text_recognizer/networks/transformer.py b/src/text_recognizer/networks/transformer.py
new file mode 100644
index 0000000..868d739
--- /dev/null
+++ b/src/text_recognizer/networks/transformer.py
@@ -0,0 +1 @@
+"""TBC."""
diff --git a/src/text_recognizer/networks/wide_resnet.py b/src/text_recognizer/networks/wide_resnet.py
index d1c8f9a..618f414 100644
--- a/src/text_recognizer/networks/wide_resnet.py
+++ b/src/text_recognizer/networks/wide_resnet.py
@@ -28,10 +28,10 @@ def conv_init(module: Type[nn.Module]) -> None:
     classname = module.__class__.__name__
     if classname.find("Conv") != -1:
         nn.init.xavier_uniform_(module.weight, gain=np.sqrt(2))
-        nn.init.constant(module.bias, 0)
+        nn.init.constant_(module.bias, 0)
     elif classname.find("BatchNorm") != -1:
-        nn.init.constant(module.weight, 1)
-        nn.init.constant(module.bias, 0)
+        nn.init.constant_(module.weight, 1)
+        nn.init.constant_(module.bias, 0)
 
 
 class WideBlock(nn.Module):
@@ -183,7 +183,7 @@ class WideResidualNetwork(nn.Module):
             else None
         )
 
-        self.apply(conv_init)
+        # self.apply(conv_init)
 
     def _configure_wide_layer(
         self, in_planes: int, out_planes: int, stride: int, activation: str