diff --git a/shap/explainers/deep/deep_pytorch.py b/shap/explainers/deep/deep_pytorch.py
index ef276c216..885e78f83 100644
--- a/shap/explainers/deep/deep_pytorch.py
+++ b/shap/explainers/deep/deep_pytorch.py
@@ -351,10 +351,16 @@ def nonlinear_1d(module, grad_input, grad_output):
 op_handler['Conv1d'] = linear_1d
 op_handler['Conv2d'] = linear_1d
 op_handler['Conv3d'] = linear_1d
+op_handler['ConvTranspose1d'] = linear_1d
+op_handler['ConvTranspose2d'] = linear_1d
+op_handler['ConvTranspose3d'] = linear_1d
 op_handler['Linear'] = linear_1d
 op_handler['AvgPool1d'] = linear_1d
 op_handler['AvgPool2d'] = linear_1d
 op_handler['AvgPool3d'] = linear_1d
+op_handler['AdaptiveAvgPool1d'] = linear_1d
+op_handler['AdaptiveAvgPool2d'] = linear_1d
+op_handler['AdaptiveAvgPool3d'] = linear_1d
 op_handler['BatchNorm1d'] = linear_1d
 op_handler['BatchNorm2d'] = linear_1d
 op_handler['BatchNorm3d'] = linear_1d
diff --git a/tests/explainers/test_deep.py b/tests/explainers/test_deep.py
index 2a9bf242f..5468ea4c7 100644
--- a/tests/explainers/test_deep.py
+++ b/tests/explainers/test_deep.py
@@ -258,7 +258,8 @@ def __init__(self):
                     nn.MaxPool2d(2),
                     nn.Tanh(),
                     nn.Conv2d(10, 20, kernel_size=5),
-                    nn.MaxPool2d(2),
+                    nn.ConvTranspose2d(20, 20, 1),
+                    nn.AdaptiveAvgPool2d(output_size=(4, 4)),
                     nn.Softplus(),
                 )
                 self.fc_layers = nn.Sequential(
@@ -369,12 +370,13 @@ def __init__(self, num_features):
             super(Net, self).__init__()
             self.linear = nn.Linear(num_features // 2, 2)
             self.conv1d = nn.Conv1d(1, 1, 1)
+            self.convt1d = nn.ConvTranspose1d(1, 1, 1)
             self.leaky_relu = nn.LeakyReLU()
-            self.maxpool1 = nn.MaxPool1d(kernel_size=2)
+            self.aapool1d = nn.AdaptiveAvgPool1d(output_size=6)
             self.maxpool2 = nn.MaxPool1d(kernel_size=2)
 
         def forward(self, X):
-            x = self.maxpool1(self.conv1d(X.unsqueeze(1))).squeeze(1)
+            x = self.aapool1d(self.convt1d(self.conv1d(X.unsqueeze(1)))).squeeze(1)
             return self.maxpool2(self.linear(self.leaky_relu(x)).unsqueeze(1)).squeeze(1)
     model = Net(num_features)
     optimizer = torch.optim.Adam(model.parameters())