# Visualize feature map

**URL:** <https://discuss.pytorch.org/t/visualize-feature-map/29597>\
**Category:** vision\
**Created:** [November 14, 2018, 4:33pm UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597 "2018-11-14T16:33:18Z")\
**Posts on this page:** 20\
**Page:** 2

<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [December 4, 2019, 1:23am UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/22 "2019-12-04T01:23:21Z")

</div>

Is your model predicting image-like outputs?  
It seems `preds` might be just a scalar tensor.

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<div class="post-metadata">

**Author:** ![raj\_patel](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/raj_patel/32/18397_2.png) [@raj\_patel](https://discuss.pytorch.org/u/raj_patel)\
**Post date:** [December 4, 2019, 1:52am UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/23 "2019-12-04T01:52:28Z")

</div>

Yes. Here is my code.

```auto
class Net(nn.Module):
    def __init__ (self):
        super(Net, self). __init__ ()
        self.conv1 = nn.Conv2d(3, 6, 7, 1, 3)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 7, 1, 3)
        self.fc1 = nn.Linear(16 * 8 * 8, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 32)
        self.fc4 = nn.Linear(32, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 8 * 8)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = self.fc4(x)
        return x

net = Net()

#training loop
for epoch in range(1,num_epoch+1):
    for i in range(0,len(X_train),batch_size):
        X = X_train_torch[i:i+batch_size]
        y = y_train_torch[i:i+batch_size]
        optimizer.zero_grad()
        y_pred = net(X)
        l = loss(y_pred,y)
        
       
        l.backward()
        optimizer.step()
    print("Epoch %d final minibatch had loss %.4f" % (epoch, l.item()))

#testing loop
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
    for i in range(0,len(X_test),batch_size):
        xt = X_test_torch[i:i+batch_size]
        yt = y_test_torch[i:i+batch_size]
        outputs = net(xt)
        _, predicted = torch.max(outputs.data, 1)
        c = (predicted == yt).squeeze()
        for i in range(4):
            label = yt[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1

for i in range(10):
    print('Accuracy of %5s : %2d %%' % (
        classes[i], 100 * class_correct[i] / class_total[i]))

# normalizing the output
def normalize_output(img):
    img = img - img.min()
    img = img / img.max()
    return img

# Plot some images
idx = torch.randint(0, outputs.size(0), ())  
pred = normalize_output(outputs[idx, 0])
img = X_train_torch[idx, 0]
print(pred.shape)
fig, axarr = plt.subplots(1, 2)
axarr[0].imshow(img.detach().numpy())
axarr[1].imshow(pred.detach().numpy())
# Visualize feature maps
activation = {}
def get_activation(name):
    def hook(model, input, output):
        activation[name] = output.detach()
    return hook

net.conv1.register_forward_hook(get_activation('conv1'))
data = X_train_torch[0]
data.unsqueeze_(0)
output = net(data)

act = activation['conv1'].squeeze()

# fig, axarr = plt.subplots(act.size(0))
j = 0
for i in range(2):
  f,(ax1,ax2,ax3,ax4) = plt.subplots(1,4,sharex=True)
  ax1.imshow(act[j])
  ax2.imshow(act[j+1])
  ax3.imshow(act[j+2])
  ax4.imshow(act[j+3])

  j = j+4

```

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<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [December 4, 2019, 1:53am UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/24 "2019-12-04T01:53:21Z")

</div>

In that case `imshow` won’t work, as it visualizes images.  
What is your use case and what would you like to visualize or plot?

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<div class="post-metadata">

**Author:** ![raj\_patel](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/raj_patel/32/18397_2.png) [@raj\_patel](https://discuss.pytorch.org/u/raj_patel)\
**Post date:** [December 4, 2019, 2:06am UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/25 "2019-12-04T02:06:02Z")

</div>

Got it. I’m trying to plot the activations.

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<div class="post-metadata">

**Author:** ![max\_jiang](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/max_jiang/32/15171_2.png) [@max\_jiang](https://discuss.pytorch.org/u/max_jiang)\
**Post date:** [February 24, 2020, 1:46pm UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/26 "2020-02-24T13:46:48Z")

</div>

sorry to bother you, I wonder how you solved this problem  
print(pred.shape)

> > > torch.Size()

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<div class="post-metadata">

**Author:** ![Tino](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/t/9dc877/32.png) [@Tino](https://discuss.pytorch.org/u/Tino)\
**Post date:** [February 25, 2020, 9:39pm UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/27 "2020-02-25T21:39:13Z")

</div>

@ptrblck Yes, are there any solutions for this error. My img works but my pred variable returns empty and causes the invalid shape error. I am using the ImageNet dataset and it does output images.

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<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [February 25, 2020, 9:48pm UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/28 "2020-02-25T21:48:21Z")

</div>

If your model outputs a scalar tensor, you won’t be able to plot it as an image using `plt.imshow`.  
How would you like to plot this scalar value?

CC @max_jiang

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<div class="post-metadata">

**Author:** ![Tino](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/t/9dc877/32.png) [@Tino](https://discuss.pytorch.org/u/Tino)\
**Post date:** [February 25, 2020, 9:52pm UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/29 "2020-02-25T21:52:38Z")

</div>

![featuremap](https://discuss.pytorch.org/uploads/default/original/3X/7/b/7b17d04af9465b9e150aa5974aef706576051c9d.png)

The img variable is plotted on the left, I would like the pred variable to be also plotted in the same way in its respective box. Is this possible?

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<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [February 25, 2020, 10:20pm UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/30 "2020-02-25T22:20:32Z")

</div>

That should be possible, if your model outputs e.g. a segmentation map (logits corresponding to each class for all pixels).  
However, since the shape of the output seems to be empty, it seems your model currently outputs a scalar.

Which model are you currently using?

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<div class="post-metadata">

**Author:** ![Tino](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/t/9dc877/32.png) [@Tino](https://discuss.pytorch.org/u/Tino)\
**Post date:** [February 25, 2020, 10:31pm UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/31 "2020-02-25T22:31:52Z")

</div>

This is my model here:

> > class ConvNet(nn.Module):
> > 
> > ```
> > def __init__ (self, num_classes=10):
> > super(ConvNet, self). __init__ ()
> > 
> > # Layer 1
> > self.conv1 = nn.Conv2d(3, 16, 3)
> > self.pool = nn.MaxPool2d(2, 2)
> > 
> > # Layer 2
> > self.conv2 = nn.Sequential(
> > nn.Conv2d(16, 24, 4),
> > nn.MaxPool2d(2, 2),
> > nn.ReLU(),
> > nn.Dropout(0.3)
> > )
> > 
> > # Layer 3
> > self.conv3 = nn.Sequential(
> > nn.Conv2d(24, 32, 4),
> > nn.MaxPool2d(2, 2),
> > nn.ReLU(),
> > nn.Dropout(0.3)
> > 
> > )
> > 
> > # Fully Connected Layer 1
> > self.fc1 = nn.Sequential(
> > nn.Linear(32 * 29 * 29, 512)
> > )
> > 
> > self.fc2 = nn.Sequential(
> > nn.Linear(512, num_classes),
> > )
> > 
> > def forward(self, x):
> > 
> > x = self.pool(torch.nn.functional.relu(self.conv1(x)))
> > x = torch.nn.functional.dropout(x, p= 0.3)
> > 
> > #x = self.conv1(x)
> > x = self.conv2(x)
> > x = self.conv3(x)
> > 
> > x = x.reshape(x.size(0), -1) 
> > x = self.fc1(x)
> > x = self.fc2(x)
> > 
> > return x
> > 
> > ```

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<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [February 25, 2020, 10:35pm UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/32 "2020-02-25T22:35:43Z")

</div>

Your model seems to be a classification model, which should yield an output of `[batch_size, nb_classes]`. This also means that you won’t get a prediction for each pixel in your input image.  
I’m currently not sure, how you would like to visualize the current output.

---

<div class="post-metadata">

**Author:** ![Tino](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/t/9dc877/32.png) [@Tino](https://discuss.pytorch.org/u/Tino)\
**Post date:** [February 25, 2020, 10:39pm UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/33 "2020-02-25T22:39:13Z")

</div>

Oh! Thanks for your time, that has cleared up a few of my misunderstandings.

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<div class="post-metadata">

**Author:** ![Tino](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/t/9dc877/32.png) [@Tino](https://discuss.pytorch.org/u/Tino)\
**Post date:** [February 26, 2020, 10:31am UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/34 "2020-02-26T10:31:33Z")

</div>

Regarding the feature map section, I have 16 outputs and the:

> axarr = plt.subplots(act.size(0))

Line won’t let me change the number of rows/columns to plot them in a way that is appropriate. It currently looks like this:

 ![featuremap](https://discuss.pytorch.org/uploads/default/original/3X/f/5/f5ab1af056be81de69b686e92aa255e4b09db688.png)

I tried to make a variable to replace act.size(0) but it seems that this line won’t accept anything else, I am not sure why.

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<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [February 26, 2020, 4:27pm UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/35 "2020-02-26T16:27:53Z")

</div>

`put.subplots` accepts the the number of rows as the first and number of columns as the second argument.  
Which values have you passed to it that were not working?

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<div class="post-metadata">

**Author:** ![Tino](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/t/9dc877/32.png) [@Tino](https://discuss.pytorch.org/u/Tino)\
**Post date:** [February 26, 2020, 6:06pm UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/36 "2020-02-26T18:06:12Z")

</div>

I changed it to (4,4) and received this error:

![featuremap](https://discuss.pytorch.org/uploads/default/original/3X/d/2/d220396ef4d83c57a38cedad70db15d843d1c119.png)

it seems to be with line 17 not the line I mentioned before apologies.

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<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [February 26, 2020, 7:36pm UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/37 "2020-02-26T19:36:46Z")

</div>

`axarr` will be a numpy array with the shape `[4, 4]`, so you would need to index it as `axarr[0, 0]` or flatten it before using a linear index.

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<div class="post-metadata">

**Author:** ![Tino](https://discuss.pytorch.org/letter_avatar_proxy/v4/letter/t/9dc877/32.png) [@Tino](https://discuss.pytorch.org/u/Tino)\
**Post date:** [February 26, 2020, 8:26pm UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/38 "2020-02-26T20:26:33Z")

</div>

Thanks for your replies. After I index `axarr` as [0,0] I receive the error `missing a required argument: 'X'`

Update: I “fixed” the issue. However now only one image is plotted out of the 16 outputs:

![featuremap](https://discuss.pytorch.org/uploads/default/original/3X/7/4/749b3f53232e96aed225c251ac7b304f8ed41fad.png)

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<div class="post-metadata">

**Author:** ![saba](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/saba/32/19439_2.png) [@saba](https://discuss.pytorch.org/u/saba)\
**Post date:** [March 12, 2020, 12:11am UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/39 "2020-03-12T00:11:05Z")

</div>

Hello Ptrblck,

Sorry I want to see my feature maps I ues the above command but the error was yor model has not extractor. Indeed my model is 🙂

```auto
class ConvNetRedo1(nn.Module):
    def __init__ (self,numf1,numf2,fz1,fz2,nn2,nn3): 
        super(ConvNetRedo1, self). __init__ ()
        self.numf1=numf1
        self.numf2=numf2
        self.fz1=fz1
        self.fz2=fz2
        self.nn2=nn2
        self.nn3=nn3
        self.layer1 = nn.Sequential(nn.Conv3d(1, self.numf1, kernel_size=self.fz1, stride=1, padding=1),nn.ReLU())
        self.layer2 = nn.Sequential(nn.Conv3d(self.numf1,self.numf2,kernel_size=self.fz2,stride=1,padding=0),nn.ReLU(),nn.MaxPool3d(kernel_size=2, stride=2))
       
       
        self.fc1 = nn.Linear(2048, self.nn2) 
        self.drop_out1 = nn.Dropout(0.3)
        self.relu1 = nn.ReLU() 

        self.fc2 = nn.Linear( self.nn2, self.nn3) 
        self.drop_out2 = nn.Dropout(0.3)
        self.relu2 = nn.ReLU()                     
        self.fc3 = nn.Linear( self.nn3, 1) 

        
    def forward(self, x):

        x=x.unsqueeze(1).float()
        out = self.layer1(x)
        out = self.layer2(out)
        out = out.view(out.size(0), -1)
        out = self.fc1(out)
        out=self.drop_out1(out)
        out=self.relu1(out)

        out = self.fc2(out)
        out=self.drop_out2(out)
        out=self.relu2(out)

        
        out = self.fc3(out)

```

Would you please tell me how I can see the feature maps or kernels?

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<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [March 12, 2020, 4:34am UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/40 "2020-03-12T04:34:43Z")

</div>

Which feature map would you like to visualize?  
[My code snippet](https://discuss.pytorch.org/t/visualize-feature-map/29597/2) should also work for you, if you use the corresponding layer to add the forward hook. 😉

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<div class="post-metadata">

**Author:** ![saba](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/saba/32/19439_2.png) [@saba](https://discuss.pytorch.org/u/saba)\
**Post date:** [March 12, 2020, 4:55am UTC](https://discuss.pytorch.org/t/visualize-feature-map/29597/41 "2020-03-12T04:55:56Z")

</div>

sorry, the way that I define the CNN layers is different, there is no self.Conv2 and like that , should I change the way of defining the layers to have sth like you?

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