From 889b7c28fb4f20bada8539b140dd2a11847ef33a Mon Sep 17 00:00:00 2001
From: jrzaurin <jrzaurin@gmail.com>
Date: Fri, 10 Jul 2020 18:30:21 +0100
Subject: [PATCH] Adapted the examples to the small code changes. Replace add_
 with add in WideDeep to avoid annoying warnings. Replace output_dim with
 pred_dim in Wide for consistentcy

---
 pytorch_widedeep/models/wide.py      | 12 ++++++------
 pytorch_widedeep/models/wide_deep.py |  8 ++++----
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/pytorch_widedeep/models/wide.py b/pytorch_widedeep/models/wide.py
index 91b8793..10cc790 100644
--- a/pytorch_widedeep/models/wide.py
+++ b/pytorch_widedeep/models/wide.py
@@ -5,14 +5,14 @@ from ..wdtypes import *
 
 class Wide(nn.Module):
     r"""Simple linear layer that will receive the one-hot encoded `'wide'`
-    input and connect it to the output neuron.
+    input and connect it to the output neuron(s).
 
     Parameters
     -----------
     wide_dim: int
         size of the input tensor
-    output_dim: int
-        size of the ouput tensor
+    pred_dim: int
+        size of the ouput tensor containing the predictions
 
     Attributes
     -----------
@@ -24,7 +24,7 @@ class Wide(nn.Module):
     >>> import torch
     >>> from pytorch_widedeep.models import Wide
     >>> X = torch.empty(4, 4).random_(2)
-    >>> wide = Wide(wide_dim=X.size(0), output_dim=1)
+    >>> wide = Wide(wide_dim=X.size(0), pred_dim=1)
     >>> wide(X)
     tensor([[-0.8841],
             [-0.8633],
@@ -32,9 +32,9 @@ class Wide(nn.Module):
             [-0.4762]], grad_fn=<AddmmBackward>)
     """
 
-    def __init__(self, wide_dim: int, output_dim: int = 1):
+    def __init__(self, wide_dim: int, pred_dim: int = 1):
         super(Wide, self).__init__()
-        self.wide_linear = nn.Linear(wide_dim, output_dim)
+        self.wide_linear = nn.Linear(wide_dim, pred_dim)
 
     def forward(self, X: Tensor) -> Tensor:  # type: ignore
         r"""Forward pass. Simply connecting the one-hot encoded input with the
diff --git a/pytorch_widedeep/models/wide_deep.py b/pytorch_widedeep/models/wide_deep.py
index bc2cab6..8956381 100644
--- a/pytorch_widedeep/models/wide_deep.py
+++ b/pytorch_widedeep/models/wide_deep.py
@@ -201,13 +201,13 @@ class WideDeep(nn.Module):
             if self.deepimage is not None:
                 deepside = torch.cat([deepside, self.deepimage(X["deepimage"])], axis=1)  # type: ignore
             deepside_out = self.deephead(deepside)
-            return out.add_(deepside_out)
+            return out.add(deepside_out)
         else:
-            out.add_(self.deepdense(X["deepdense"]))
+            out.add(self.deepdense(X["deepdense"]))
             if self.deeptext is not None:
-                out.add_(self.deeptext(X["deeptext"]))
+                out.add(self.deeptext(X["deeptext"]))
             if self.deepimage is not None:
-                out.add_(self.deepimage(X["deepimage"]))
+                out.add(self.deepimage(X["deepimage"]))
             return out
 
     def compile(
-- 
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