Each input is of size (64, 1, 28, 28) and the architecture is as follows: . Reduce learning rate when a metric has stopped improving. Thanks for contributing an answer to Stack Overflow! If the process is all right, you should get a overfitted model with 0 loss. Would be great if you can provide s. Deploying A MERN Application To Heroku(A Step-by-Step Guide), Acing the Social Component of Technical Interviews, Boost the flexibility of your Microservice architecture with Spring Cloud, How to Use on Conflict in INSERT Statement in PostgreSQL, RuntimeError: 1only batches of spatial targets supported (3D tensors) but got targets of size: : [64], transforms.Normalize(128, 1) # wrong normalization, transforms.Normalize(mean=0.1307, std=0.3081). I checked and found while I was using LSTM: I simplified the model - instead of 20 layers, I opted for 8 layers. Now knowing what we are looking for, we quickly find a mistake in the forward method. Then I tried to train hmdb51 without pretrained, the evaluation accuracy is as follows: I think the dataset is primary cause, and the data processing method(one clip or multiple clips sampled from one video) is second cause. ( see the below image ) The training and validation losses quickly decrease. When these functions are applied on the wrong dimensions or in the wrong order, we usually get a shape mismatch error, but this is not always the case! For demonstration, we will use a simple MNIST classifier example that has a couple of bugs: If you run this code, you will find that the loss does not decrease and after the first epoch, the test loop crashes. find out why your training loss does not decrease. If you shift your training loss curve a half epoch to the left, your losses will align a bit better. The model is overfitting right from epoch 10, the validation loss is increasing while the training loss is decreasing.. PyTorch Lightning has logging to TensorBoard built in. You signed in with another tab or window. Have a question about this project? If you've done the previous step of this tutorial, you've handled this already. Its been a while. Each input is of size (64, 1, 28, 28) and the architecture is as follows: self.conv1 = nn.Conv2d(1, 10, kernel_size=5), self.conv2 = nn.Conv2d(10, 20, kernel_size=5), self.fc2 = nn.Linear(50, 10) # (num_features, num_classes), x = F.relu(F.max_pool2d(self.conv1(x), 2)), x = F.relu(F.max_pool2d(self.dropout(self.conv2(x)), 2)). There are several similar questions, but nobody explained what was happening there. If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. Making statements based on opinion; back them up with references or personal experience. Dropout is used during testing, instead of only being used for training. To learn more, see our tips on writing great answers. This is identical to the code in the tutorial but I have to reshape the output so it fits. @sacmehta Hi, are you able to share your pretrained PyTorch ImageNet weights? Below is the implementation for n = 3: And here is the same in a Lightning Callback: Applying this test to the LitClassifer immediately reveals that it is mixing data. my dataset os imbalanced so i used weightedrandomsampler but didnt worked . So far I've found pytorch to be different but MUCH more intuitive. Have you made sure the logsoftmax is being performed along the correct axis? Any idea what might go wrong? Have a question about this project? I don't want to use fully connected (in pytorch linear) layers and I want to add Batch Normalization. Can I spend multiple charges of my Blood Fury Tattoo at once? In fact, I have already done it for you in this repository. neural-networks The fixed code now runs without errors, but if we look at the loss value in the progress bar (or the plots in TensorBoard) we find that it is stuck at a value 2.3. This is not a bug, its a feature! The model verification is a bit more sophisticated and also works with multiple in- and outputs. Now that we have that clear let's understand the training steps:- Move data to GPU (Optional) Clear the gradients using optimizer.zero_grad () Make a forward pass Calculate the loss Perform a backward pass using loss.backward () to calculate the gradients Take optimizer step using optimizer.step () to update the weights Non-anthropic, universal units of time for active SETI, How to constrain regression coefficients to be proportional. What is left is the actual research code: the model, the optimization and the data loading. But the validation loss started increasing while the validation accuracy is not improved. Pytorch tutorial loss is not decreasing as expected, Making location easier for developers with new data primitives, Stop requiring only one assertion per unit test: Multiple assertions are fine, Mobile app infrastructure being decommissioned. 2018-12-01 12:40:18,564 - root - INFO - Epoch: 0, Validation Loss: inf, Validation Regression Loss inf, Validation Classification Loss: 10.0192. How do I simplify/combine these two methods for finding the smallest and largest int in an array? Hi, I am taking the output from my final convolutional transpose layer into a softmax layer and then trying to measure the mse loss with my target. U-Net pytorch model outputting nan for MSE but not L1? Train the model on the training data. But wait! Oh, I see. What is the function of in ? @nguyenquibk1996 Hi did you solve the problem? There could be many reasons for this: wrong optimizer, poorly chosen learning rate or learning rate schedule, bug in the loss function, problem with the data etc. I first feed that in an char-based Embedding, then padding using pack_padded_sequence, feeding in LSTM , and finally unpacking with pad_packed_sequence. After some time, validation loss started to increase, whereas validation accuracy is also increasing. https://pytorch.org/docs/stable/nn.html#torch.nn.SmoothL1Loss . Loss is not decreasing. Define a neural network. class. In this blog post, we implemented two callbacks that help us 1) monitor the data that goes into the model; and 2) verify that the layers in our network do not mix data across the batch dimension. When I train I am getting a constant loss value and no change. If you look at the documentation of CrossEntropyLoss, there is an advice: The input is expected to contain raw, unnormalized scores for each class. I'm using an SGD optimizer, learning rate of 0.01 and NLL Loss as my loss function. Using learning rate scheduler, we can gradually decrease the learning rate value dynamically while training. Sign in Error in training PyTorch classifier from the 60 minute blitz in GPU, Output of the model depends on the shape of the weights tensor. @sacmehta thanks a lot. It can be every epoch or if this is too costly because the dataset is huge it can be each N epoch. Hi, I am new to deeplearning and pytorch, I write a very simple demo, but the loss can't decreasing when training. Viewed 616 times 1 $\begingroup$ So I am currently trying to . How to distinguish it-cleft and extraposition? Hi @sacmehta , have you tried a smaller learning rate? 2018-12-01 12:38:51,741 - root - INFO - Epoch: 0, Step: 300, Average Loss: 7.1205, Average Regression Loss 2.2209, Average Classification Loss: 4.8996 Edit: It may also be possible that my issue lies outside the model architecture. Are Githyanki under Nondetection all the time? On average, the training loss is measured 1/2 an epoch earlier. Hi, So I am trying to sanity-check my binary image classification model. The convolution layers don't reduce the resolution size of the feature maps because of the padding. In that case I have added my training loop here: for batch_idx, (image, label) in enumerate(train_loader): image, label = image.to(device), label.to(device), loss = F.nll_loss(output, label).to(device), (batch_idx*64) + ((epoch-1)*len(train_loader.dataset))), torch.save(model.state_dict(), 'results/model.pth'), torch.save(optimizer.state_dict(), 'results/optimizer.pth'). You can optionally divide by its length in order to normalize the loss, so the scale will be the same if you increase the validation set one day. Also, try a small subset of the training data to verify the process is right. One of my nets is a good old fashioned autoencoder I use for anomaly detection . What is the difference between the following two t-statistics? Ask Question Asked 2 years ago. Extending TorchVisions Transforms to Object Detection Getting Started with Facial Keypoint Detection using Deep is it possible to use several different pytorch models on Press J to jump to the feed. Sign in Best way to get consistent results when baking a purposely underbaked mud cake. Your learning rate and momentum combination is too large for such a small batch size, try something like these: Update: I just realized another problem is you are using a relu activation at the end of the network. This is not a good solution, because it pollutes the code unnecessarily, fills the terminal and overall takes too much time to repeat it later on should we need to. I've managed to get the model to train but my loss is not decreasing over time. What you did seems correct, you compute the loss of the whole validation set. From a practical point of view, a Deep Learning project starts with the code. In this example, neither the training loss nor the validation loss decrease. At this moment, I have a Variable of BATCH_SIZE*PAD_LENGTH*EMBEDDING_LEN and another Variable of the real length of each. P.S. Plz also reference the implementation is PyTorch. I don't think it can converges from the first epoch with many datasets. I have solved the problem, because my training data has very small boxes, so the smoothed l1 loss(log(0)=-inf) become -Inf. When the validation loss is not decreasing, that means the model might be overfitting to the training data. Try training your network by removing last relu from conv5 and keeping lr=0.01 and momentum=0.9. Here is the rest of the code. I have tried different learning rate regimes, but didn't have any luck. I want to use one hot to represent group and resource, there are 2 group and 4 resouces in training data: group1 (1, 0) can access resource 1 (1, 0, 0, 0) and resource2 (0, 1, 0, 0) group2 (0 . There are many ways to do this. You can try to plug-in your model in my codebase and see if that helps. loss: 2.270 loss: 2.260 loss: 2.253 loss: 2.250 loss: 2.232 while in the tutorial the loss decreases way faster. These nasty bugs are hard to track down. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. A fast learning rate means you descend down quickly because you likely are far away from any minimum. Already on GitHub? (output in the tutorial was (4,10) and mine is 4,1,1,10). Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Before we debug this code, we will organize it into the Lightning format. Did Dick Cheney run a death squad that killed Benazir Bhutto? Define a loss function. Pytorch LSTM not training. implement automatic model verification and anomaly detection. If your loss is composed of several smaller loss functions, make sure their magnitude relative to each is correct. Create notebooks and keep track of their status here. Maybe a log of the training. loss_fn = torch.nn.crossentropyloss() # nb: loss functions expect data in batches, so we're creating batches of 4 # represents the model's confidence in each of the 10 classes for a given input dummy_outputs = torch.rand(4, 10) # represents the correct class among the 10 being tested dummy_labels = torch.tensor( [1, 5, 3, 7]) print(dummy_outputs) The concept of a callback is a very elegant way of adding arbitrary logic to an existing algorithm. Why is the loss function not decreasing in PyTorch? 2018-12-01 12:38:16,778 - root - INFO - Epoch: 0, Step: 100, Average Loss: 12.1986, Average Regression Loss 2.7535, Average Classification Loss: 9.4451 A reliable way to implement this test is to compute the gradient on the n-th output with respect to all inputs.
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