Adam optimizer nan loss. randn(N, D_in, device=device, dtype=dtype) y = torch.

Adam optimizer nan loss During training, the AdamW optimizer was used for quicker convergence with FP16. a partially-flattened data structure for performance. Adabelief is obtained from Adam by replacing the exponential moving average of the Thank you again, everyone. 00001), loss='categorical_crossentropy', metrics=['accuracy']) This should make your code work So I read that poisson loss function could be an optimal loss function choice for this case. According to Kingma et al. When NaN loss occurs, it is essential to identify the cause and take appropriate actions. 731159, time 41205. Ensure that the optimizer’s internal calculations are compatible with float16. Inspecting any predicted masks ends up with values of Nan which is not right. Ask Question Asked 7 years, 11 months ago. beta_1/beta_2: floats, 0 < beta < 1 After the third or fourth 'run' with the tensorflow debugger on a fresh model directory I get 'NaN loss during training. I have a custom image set that I am using. It could possibly be caused by exploding gradients, try using gradient clipping to see if the loss is still displayed as nan. Unless cloning code from GitHub that has the learning rate hard-coded into a chosen optimizer, I would likely just put 3e-4 into an Adam optimizer and let the model train. For instance, even if I use relu, tanh, leaky relu, the loss becomes NAN at iteration 4837. I tried to change the optimizer and fix the dropout rate, but nothing changed, any solution? model. S. Practical Example: Fine-Tuning a Model Using Instantiate Adam Optimizer. It now immediately produces NaNs after the first training step, but only if fused adam But when I run this new dataset with the augmented images, the model gives nan values for loss and doesn't train on the data. I am using default Train on 54600 samples, validate on 23400 samples Epoch 1/5 54600/54600 [=====] - 14s 265us/step - loss: nan - accuracy: 0. compile(optimizer=tf. random. Any ideas or thoughts here would be greatly appreciated. Between, no issues et al when I use Adam as the You can play with the parameters to find a good balance, but this is one way to use exponential decay as a callback function with the Adam optimizer. AdamW(model. [input_branch1, input_branch2, input_branch3], outputs=output) model. the direction of the objective function I am working on transfer learning. ” I have also tried to increase the epsilon of Adam optimizer that I’m using to 1e-4. It is recommended to leave the parameters of this optimizer at their default values. Should you still require the flexibility of calling Compile: optimizer is "adam" and loss is "mse" [=====] - 182s 2ms/step - loss: nan Epoch 3/100 35140/80677 [=====>. This mechanism is in place to support optimizers which operate on the output of the closure (e. Our main training loop executes forward pass, loss calculation, parameter update Regression Predictive Modeling Problem. 001 a similar issue occurs, but a few epochs When I use 1-bit Adam to train my model, there are lots of "Grad overflow on iteration" and "Overflow detected. Default parameters follow those provided in the paper. Here is the training code: I was running into my loss function suddenly returning a nan after it go so far into the training process. Conv1d(1, When I change to a two dimensional regression, my loss function becomes equal to NaN. 09e-01 2 Loss(train): 1. 66e-01 1 Loss(train): 2. In pytorch, I have a loss function of 1/x plus a few other terms. 00% iter 2: loss nan, time 3116. Adam optimization is a stochastic gradient descent method that is based on adaptive estimation of first-order and second-order moments. However, when I tried to use torch. any() Some float encoders (e. 85e-01 3 Loss(train): 1. Obviously, I This is my first time writing a Pytorch-based CNN. I cannot use loss function 'categorical_crossentropy' as the output is not one-hot encoded but a sequence. The loss function and metric is MSE. The value in args. 34ms, mfu -100. 0000e+00 as accuracy in every epoch. Thus, the model produced NaN losses. I'm training an unet for a one-class segmentation task using a medical dataset of 10K+ images. I am using a CNN for a regression task. any(numpy. If loss goes down, pat I thought I should include the solution I found here and close the issue. Optimizer accidentaly pushes the network out of the minimum (you identified this too). semi_hard_triplet_loss_distance = math_ops. df['zg500'] ''' 0 -3. 91e-01 Loss(val): 1. This "gradient explosion" is indicated by a training loss that goes to NaN or Inf. image. , 2014 , the method is " computationally efficient, has little memory requirement, invariant to diagonal rescaling of gradients, and is well suited for problems that As can be seen in the code snippet above, Lightning defines a closure with training_step(), optimizer. parameters(recurse=True),weight_decay=1e model. , 2020), which achieves the initial fast convergence of adaptive methods, good generalization as SGD, and training stability in complex settings such as generative adversarial networks (GANs). 001) model. 0, I'm still not getting any reduction in loss. This is the result of the vanishing gradient problem. rmsprop. When training a neural network, the loss gradients are computed through backpropagation. If a state_dict is returned, it will be used to be loaded into the optimizer. e 128) the loss function goes to nan. In order to clip your gradients you'll need to explicitly compute, clip, and apply them as described in this section in TensorFlow's API For 7 epoch all the loss and accuracy seems okay but at 8 epoch during the testing test loss becomes nan. Learning rate could be smaller, though I haven't used anything lower than 0. They are anywhere from e^-32 to e^31. We can use a standard regression problem optimizer = torch. parameters(), lr=0. compile(loss='categorical_crossentropy', optimizer='adam') return model def labeled_sequences(n: int You can see your training loss is still getting smaller at step 15. I've finally gotten the code to run to the point of producing output for the first data batch, but on the second batch produces nans. 348356 [731200/235474375] loss: 7. After computing Now, we will look at the most commonly and widely used optimizer i. I tried to reduce the learning rate to 1e-8. pyplot as plt import numpy as np # Define a simple loss function def loss(x): return (x - 2)**2 # Define Adam optimizer def adam_update(param, grad, learning_rate, beta1, beta2 If you are using categorical_crossentropy as loss function then the last layer of the model should be softmax. We‘ll tune this later. There is no issue when using FP32, or changing the optimizer with FP16 (I tried both adamax and sgd). 0000e+00 Epoch 2/5 54600/54600 [=====] - 15s 269us/step - loss: nan - accuracy: 0. : Why my losses are so large and how can I fix them? After running this cell of code: network = Network() network. Ask Question Asked 5 years, 3 months ago. Using lr=1e-5 you need to train for 20,000+ iterations before you see the instability and the instability is less dramatic, values hover around $10^{-7}$. Section 4contains theoretical prediction and empirical con rmation of statistical properties of the update rule of the Adam optimization algorithm, while Section5argues for the malicious nature of these properties. 00073412, loss = nan, in the middle of the 52th epoch I have read earlier suggestions as well, but since my loss in not exploding could there be other issue? Because generally Adam optimizer won’t work well with autocast or half precision. ptrblck May 31 Optimizer Configuration: Verify that the optimizer configurations are appropriate for mixed precision training. Some of the major bugs in your code: You are using sigmoid activation function instead of I am training a seq2seq model using SGD and I get decent results. i. But when I trained on bigger dataset, after few epochs (3-4), the loss turns to nan. grad_scale = None if optimizer_state["stage"] == OptState. ] - ETA: 1:43 - loss: nan I am expecting that the loss be a numerical value, not NaN. nn. My loss is nan and therefore scaler. DeepSpeed is a PyTorch optimization library that makes distributed training memory-efficient and fast. The reason for getting nan in the loss is that your target values are in the extremes. In fact, before decreasing the rate, it finished training without any problems and I thought I had somehow solved the problem getting nan in loss can be happened for one of following reasons-There is nan data in the dataset. 6868 - It's kind of stander problem of NaN in training, I suggest you read this answer about issue NaN with Adam solver for the cause and solution in common case. startswith('mixed'): logger. summary() X_train,X_test,y_train,y_test=train_test_split(train Gradient Clipping is the process that helps maintain numerical stability by preventing the gradients from growing too large. Callable[, Optimizer] jax. Here are some Handling NaN Loss. The output is displaying as loss: nan from the first epoch. 0001. NaN is nothing but Not A Number. Adam() model. And the I'm using a sequence which should be quite easy. I was using SGD, which is sensitive to scaling and makes the parameters to overshoot. Skipping step" in my log. Adam(lr=1e-8) model. This generally works fine when i choose short windows (i. Replicating examples from Chapter 6 I encountered One of the reasons: Check whether your dataset have NaN values or not. layers. This seems to happen when ever i use the adam optimizer, but it is fine if i use sgd. Adam() it can't be trained and outputs a nan loss at each iteration. 0000e+00 - val_loss: nan - val_accuracy: 0. My use case is to classify two categories of images. g. e. (Use leaky-relu instead) Sometimes zero into square_root from torch gives nan output. A regression predictive modeling problem involves predicting a real-valued quantity. set_epsilon(1e-4) Overview; ResizeMethod; adjust_brightness; adjust_contrast; adjust_gamma; adjust_hue; adjust_jpeg_quality; adjust_saturation; central_crop; combined_non_max_suppression I assigned different weight_decayfor the parameters, and the training loss and testing loss were all nan. However after some epochs I got MSE which is super high and it goes to NaN sometimes. classification loss in regression problem) You are using relu in last layer, which is not expected. Check the data: NaN loss can occur if the input data val_loss is NaN at very first of training. my specific problem was due to the default epsilon value of 1e-7 beeing too small for FP16 and leading to nan values, changing it with K. requires_grad, model. SGD. Training Loop. I have tried different values of weight_decay in [0. cuda() optimizer = In some case, the random NaN loss can be caused by your data, because if there are no positive pairs in your batch, you will get a NaN loss. compile(loss='sparse_categorical_crossentropy', optimizer=optimizer) print I am fine-tuning a pretrained ViT on CIFAR100 (resizing to 224), the training starts out well with decreasing loss and decent accuracy. I am attempting to use adam for say 10,000 iteration then the L-BFGS optimizer (pytorch) for the last 1,000. 0001 (which is what you're using) myself. The issue of encountering "NAN" loss values after a certain number of epochs is indeed a challenge that can arise with the AdamW optimizer, especially when training for extended periods. Learning rate. Module): def __init__(self): super(Net, self). example_libraries. So I changed the number of steps from 15 to 45 and this is the figure generated after step 40: The original code reached 4e-05 loss after step 4. Here you are using sigmoid which has the chance of making all dimensions of output close to 0 which will result in loss to overflow and hence nan. keras: could not convert string to float in model. This is the architecture of my neural network, I have used BatchNorm layer: class Net(nn. 6790 to learning rate = 0. multiply(loss_mat, I followed this tutorial and tried to modify it a little bit to see if I understand things correctly. Stack Exchange Network. Given that there are about 10,000 classes I used sparse_categorical_crossentropy rather than one-hot encoding the classes, however as soon as the network starts training the loss is stuck at one number and after Step 4: Initialize the AdamW optimizer and set up the loss function. Here is a snippet from my latest logfile, showing the mean loss of each epoch: 0 Loss(train): 5. compile(optimizer='adam Without delving too deep into the internals of pytorch, I can offer a simplistic answer: Recall that when initializing optimizer you explicitly tell it what parameters (tensors) of the model it should be updating. model. (High lr also gave NaN) Even after using gradient clipping also grad norm and output shows NaN. 0967 - val_aux_output_loss: 18. softmax(x) # NaN loss on v100 GPU, normal on CPU x = tf. Loss suddenly increases with Adam Optimizer in Tensorflow. Running x. SGD(filter(lambda p: p. The optimizer argument is the optimizer instance being used and the state_dict argument is a shallow copy of the state_dict the user passed in to load_state_dict. Adam(network. Traceback shows “RuntimeError: Function ‘PowBackward0’ returned nan values in its 0th output. loss: 11. (optimizer = "Adam", loss = "sparse_categorical_crossentropy", metrics = ["accuracy"]) ## Training with labels from range 0 to 5 model. And then check the loss, and then check the input of your lossJust follow the clue and you will find the bug resulting in nan problem. compile(optimizer=optimizer, loss='mean_squared_error') Discover the causes of NaN loss values in TensorFlow and learn effective strategies to resolve them in this comprehensive, easy-to-follow guide. I greatly Here you can see the performance of our model using 2 metrics. Loss function is now high. Hi all, I am a newbie to pytorch and am trying to build a simple claasifier by my own. Similar to Figure 5, the Adam’s update rule. minimize() method. CrossEntropyLoss() Voila! Now, you are ready to start training your CNN model, and that’s what we will do in the next section. 0001) loss_min = np. randn(N, D_in, device=device, dtype=dtype) y = torch. Eventually, the loss becomes "nan" and the training process However, when I used the Adam Optimizer, the training loss curve has some spikes. Train data size is 37646 and test is 18932 so it should be enough. 5150 - acc: 0. 56ms, mfu -100. After I made all batches equally sized, the NaN's were gone. In machine learning, optimizers and loss functions are two components that help improve the performance of I am using MSELoss and Adam Optimizer. Follow answered Jun 22, 2018 at 13:00. Despite that, the output I'm getting is the following: When I trained only several epochs using the Adam optimizer, losses became "nan". (standard is 1e-08) to Adam optimizer, it works for me. 0000 thanks a billion :) Nick Frosst. UNSCALED else scaler the problem is that fused adam unscales the gradients There is a 2-class classification problem, and my loss function is custom. 476790e+11 2 -1. iter 0: loss 6. conv1 = nn. But then even at a loss scale of 1. Closed Calabashypr opened this issue May 29, 2024 · 6 comments When I tried to train model with the Poisson loss function and the Adam optimizer,the loss value of the model became NaN. 2 I am trying to train a network using the ADAM optimizer, but my loss during training is always nan, and the accuracy is always 0. Arguments. 840909 [ 104/ 1634] Train loss: nan [ 204/ 1634] Train loss: nan [ 304/ 1634] Train loss: nan [ 404/ 1634] I’m using this optimizer: optim = torch. Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company A recent study introduces a variant of the Adam optimizer called AdaBelief (Zhuang et al. 9807 - val_main_output_loss: 20. fit Optimizer that implements the Adam algorithm. Now we can create the Adam optimizer, passing in model parameters: optimizer = optim. compile(optimizer='adam', loss='mean_squared_error') You have a single weight and bias. Also, no idea what this means. truediv( math_ops. compile(loss='mean_squared_error', optimizer='adam') return model #fix random seed for reproducibility seed = 7 numpy. NaN loss can occur due to various reasons, including: Invalid input data: If the input data contains missing or invalid values, it can lead to NaN loss during training. With a learning rate of 0. 001). ETA: 255s - loss: nan - acc: 0. I checked that my data wasn't corrupted, tried playing with the learning rate, adding clipnorm, As stated, I'm using a clipvalue of 1 with the adam optimizer which was what was recommended on this and this posts. And this is not the case with SGD. lr: float >= 0. The hook may modify the state_dict inplace or optionally return a new one. SparseCategoricalCrossentropy(from_logits Training model with the Poisson loss function and the Adam optimizer resulted in NaN loss #68806. However when using my L-BFGS optimizer the loss of the network never changes and remain constant. Here In that case, sparse categorical crossentropy loss can be a good choice. functional as F device = torch. The theory is that Adam already handles learning rate optimization (check reference) : "We propose Adam, a method for efficient NA or NaN or Inf values in your data creating NA or NaN or Inf values in the output, No change in accuracy using Adam Optimizer when SGD works fine; SGD trains slower, but it leads to a lower generalization error, while Adam trains faster, but the test loss stalls to a higher value; try decreasing the batch size; increase the learning The problem I have is that any further training I do on the training data ends up with nan as a loss. Problem description. 00% iter 3: loss nan, time 3117. Basically I just did following two change and code running Optimizer: Adam GPU: Titan X and GTX 970 Activations: RELU Last layer activation: sigmoid [-1, -1, 16] # Normal x = tf. For example: from keras import optimizers optimizer = optimizers. parameters()), lr=1e-3) SGD works fine, I can observe losses decreasing slowly, and the final accuracy is pretty good. I am trying to train a tensor classifier with 4 classes, the inputs are one dimensional tensors with a length of 1000. Thank you for the advice. core. (pytorch) for the last 1,000. 996248e-29 1 2. If you see that plot very carefully, all of the NaN values (the triangles) come on a regular basis, like if at the end of every loop something causes the Figure 7 shows the results of the Adam optimizer on the noisy custom loss function along with the respective learning curves across a sweep of β₁ and β₂ values. When I train my LSTM model, it returns nan for the loss. Keras - Loss Nan and 0. Running NNCG (our method) after Adam+L-BFGS provides further improvement. NaN values can cause problem to the model while learning. Viewed 15k times 21 . I couldnt able to understand what is the issue. Here is a way of debuging the nan problem. This is often due to numerical instability which can arise from various reasons. preprocessing. ImageDataGenerator(rotation_range I'm trying to train a basic CNN on the image dataset that contains faces of celebrities with the class assigned corresponding to each person. ADAM. Much like Adam is essentially RMSprop with momentum, Nadam is Adam RMSprop with Nesterov momentum. With Adam, I'm stuck at 22. . But during training, the loss has value 'nan' for all epochs and the prediction gives array with all values as 'nan'. pack_optimizer_state (marked_pytree) [source] # Converts a marked pytree to an Try using a Adam optimizer with a low learning rate like 0. Adam(learning_rate=0. 9365 Epoch 2/20 363/363 [=====] - 1s 2ms/step - loss: 13. 61e-01 Loss(val): 2. 01, 0. parameters(), lr=1e-5) It will take longer to optimise. I tried to change the loss function, activation function, and add some regularisation like Dropout, but it didn't affect the result. fit(x, y) Isn't the string 'adam' supposed to be LossScaleOptimizer wraps another optimizer and applies loss scaling to it. Adam(params=lstm_model. Return type:. isnull(). I am getting okay results with just using the adam optimizer however I want to get better results. When training my model, I am getting nan as loss and 0. But then suddenly the loss goes to NaN with the accuracy equaling random guess. , tf. Here are a few strategies to handle NaN loss: 1. Modified 5 years, 3 months ago Gradient clipping needs to happen after computing the gradients, but before applying them to update the model's parameters. $\endgroup$ – Chalant. parameters(), lr=learning_rate, weight_decay=weight_decay) criterion = nn. 2916 - main_output_loss: 14. 184393 [732800/235474375] Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company I have a model that trains well without any regularization, however, when I implement L2 regularization (by the weight_decay in adam optimizer), the loss becomes nan after some iterations. So it looks like it was a problem with my dataset. 4609 - val_loss: 19. losses. 01) The key hyperparameters here are the learning rate lr which we set to 0. 087582 [ 4/ 1634] Train loss: 0. ZeRO works in several stages: ZeRO-1, optimizer state partitioning across GPUs; ZeRO-2, gradient partitioning across GPUs Optimizer that implements the Adam algorithm. Share. >>> opt. And I have checked the data with numpy. I use Tensorflow and If you're training for cross entropy, you want to add a small number like 1e-8 to your output probability. 7662 - val_loss: 0. 5) regressor. On smaller one, it should some increase in accuracy, but on the bigger dataset the issue still persists. Poisson(), optimizer=opt) When I try to fit the model I obtain something like that. 333 accuracy throughout the training. 0. As you can see in Adrian Ung's notebook (or in tensorflow addons triplet loss; it's the same code) :. In original yolo paper, the co-ordinates are bounded meaning co-ordinates, height, widths are normalized in range (0,1). dtype return dtype Train loss: 1. 7812 - val_loss: 14. 0150 - aux_output_loss: 6. I used InceptionV3 to classify images. In your example, both of those things are handled by the AdamOptimizer. (TimeDistributed(Dense(labels))) model. Sometimes, a lower weight Epoch 1/20 363/363 [=====] - 1s 2ms/step - loss: 1547. import pickle import tensorflow as tf import numpy as np import matplotlib. length 16) but if i increase it (i. Nesterov Adam optimizer. Some value fed to 1/x must get really small at some point because my loss has become this:. Keras network fit: loss is 'nan', accuracy doesn't change. opim. seed(seed) estimators I had a similar problem where my model produced NaN losses only during the last batch of an epoch. Explore 12 solutions to tackle NaN loss issues in TensorFlow when training Convolutional Neural Networks, including optimizer adjustments and label handling. get_scale() keeps reducing after every batch from 32768 until it becomes zero, so i stop the training. I have some data and wanting to classification. e df. Adam can be looked at as a combination of I'm trying to train a LSTM ae. 56e-01 Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company import matplotlib. 010202e+08 3 -1. 407987e-02 4 2. What's the explanation of these spikes? Model Details: 14 input nodes -> 2 hidden layers (100 -> 40 units) -> 4 output units. It can be seen that our loss function (which was cross-entropy in this I have a model that I am trying to train where the loss does not go down. inf num_epochs = 10 start_time = Here's one possible interpretation of your loss function's behavior: At the beginning, loss decreases healthily. I just checked the state_dict of Adam using autocast and it seems all internal buffers are still stored in FP32, so I’m unsure why the eps value might cause trouble in this case (due to a potential underflow). The loss function is the weighted sum of BCE and Could you please help me figure why I am getting NAN loss value and how to debug and fix it? P. shape = (2363, 242, 1) and Y. or use a more sophisticated optimizer like Adam, L2 regularization and weight decay become different. Loss Wave, = 5 Adam L-BFGS NNCG Figure 1. shape = (2363, 144, 1). I'm using autocast with GradScaler to train on mixed precision. 00% iter 1: loss nan, time 3137. If i use Adam optimizer i. In order to achieve that i have written below code but when i train it i get loss NaN. I’ve added the gradient clipping as you suggested, but the loss is still nan. You could try different optimizers, e. Specifically, my discriminator's loss becomes NAN at the exact same iteration even with different hyper parameters. On the wave PDE, Adam converges slowly due to ill-conditioning and the combined Adam+L-BFGS optimizer stalls after about 40000 steps. I had some blanks in the data (not much mind you) and I wonder when I tried to normalize those and feed into model that the model didnt like that. nn as nn import torch. Check validity of inputs (no NaNs or sometimes 0s). optim. 01 for the parameters of the fully I noticed that when training a sequential model in Keras using FP16, mse as loss function and adam as optimizer; the loss can't be calculated and I get a nan value. MSELoss() optimizer = optim. 01, now I set it to 0. isnan(dataset)), it returned Edit: I have tried using gradient clipping with clipnorm and clipvalue as parameters for the optimizer, increasing the batch_size, using Adam instead of SGD, and using regularization and normalization. What else could be reason for the LSTM gradient and output to be NaN? I am trying to train a simple network and I get NaN loss on first epoch. zero_grad() and loss. Your code with ADAM can reduce the loss across all 45 steps, but the final loss is around Im trying to fit a sequence with an lstm on a sliding window. 7197 - main_output_loss: 967. But when I try to use the default optimizer tf. maximum( math_ops. device("cuda:0") x = torch. fit. Linear(10, 10). The eps value is used here to avoid dividing by a 0. # Compile the model with appropriate loss function and optimizer model. cuda() criterion = nn. I inspected the weights and they had become nans too. Checked for Null Values in training data but there was nothing Using Adam Optimizer and set a very low learning rate. I have a code right now that seems to run really well, but it have one problem - sometimes during training it returns nan. 01. 0001 and Adam optimizer. This may be causing dying gradients. It's like a seq2seq model, you throw a signal in to get a reconstructed signal sequence. X. optimizers. I printed the prediction_train,loss_train,running_loss_train,prediction_test,loss_test,and running_loss_test,they were all nan. I checked the relus, the optimizer, the loss function, my dropout in accordance with the relus, the size of my network and The learning rate, loss goes from learning rate = 0. _set_hyper on beta_1. I have tried using 'rmsprop' optimizer instead of 'adam'. I have checked my data, it got no nan. LBFGS). To avoid that, I tried adding recurrent dropout, le/l2 regularizes, clipping the gradient as well as normal dropout. The gradients are "stored" by the tensors themselves (they have a grad and a requires_grad attributes) once you call backward() on the loss. compile(optimizer='adam', loss=tf. This loss function performs the same type of loss - categorical crossentropy loss - but works on integer targets instead of one-hot encoded ones. Commented Dec 17, 2018 at 4:08 Seeing that you don't always get NAN loss I would decrease the learning rate and see if it helps (probably will also help with convergence). Keras: TypeError: 'float' object is not I am training a GAN model, and I am having a hard time fixing a NAN loss problem for my discriminator. __init__() self. It is seq2seq, transformer model, using Adam optimizer, cross entropy criterion. The loss function does seem to decrease To sum up the different solutions from both stackOverflow and github, which would depend of course on your particular situation:. During training, the gradient (partial derivative) can become so small (tends to 0). the loss) or need to call the closure several times (e. 04. By default, the loss scale is dynamically updated over time so you do not have to choose the loss scale. Below is an example of a Train on 2682 samples, validate on 468 samples Epoch 1/6 2682/2682 [=====] - 621s 232ms/step - loss: 1. user2368505 user2368505 . Weight Decay != I'll post the solution here just in case someone gets stuck in a similar way. compile(loss='mean_squared_error', optimizer='adam') # Handle NaN loss during training try: model. I did try decreasing the learning rate and it still went to NaN. 00% optimizer. Update: After a suggestion by Marcin Możejko I updated the code but unfortunately the training loss is still Nan: model. 240596e-32 In L2 regularization, an extra term often referred to as regularization term is added to the loss function of the network. I am going through "Deep Learning in Python" by François Chollet (publisher webpage, notebooks on github). Dense(10) ]) # Compile the model with an optimizer, a loss function, and a metric model. keras. 25% validation accuracies at every epoch. 1940 - aux_output_loss: 6772. 63ms, mfu -100. With a little tweaking of the learning rates this model converge in a few iterations. @sally2 please spend at least a bit of time in reviewing chat bot answers, as these are typically creating noise only and users are certainly able to use Firstly, check for NaNs or inf in your dataset. 95ms, mfu -100. Before becoming nan test started to become very high around 1. dtype and y. 0; I'm guessing because it detected the nan gradients. adaptive learning rate the model tends to find convergence even without scaling down the X to [-1, 1] or The model I have written returns nan as a loss for all training epochs and for all label predictions. <class 'pandas. Skip to main content. How to set the hyperparameter to avoid this situation? I have a model that trains well without any regularization, however, when I implement L2 regularization (by the weight_decay in adam optimizer), the loss becomes nan I am working on a multi-classification task (using Cross entropy loss) and I am facing an issue when working with adam optimizer and mixed precision together. I made sure that my data types are of the correct format. In my case, the problem was that the size of the batches was not always equal. 21e-01 Loss(val): 3. It combines both SGD with momentum to resolve local minima problem and RMSProp which uses I am trying to train my model for Instrument Detection. In an Adam optimizer, beta_1 is a hyperparameter but epsilon is not, as the Adam optimizer only calls Optimizer. I am using Tensorflow as backend. Using wrong loss. unread, Nov 9, 2015, 11:48:48 AM 11/9/15 You are getting this much lower accuracy because your input features are very high(19900) and the model couldn't able to deal with this high dimensional input space. Below there is the configuration of my model A_2]) opt = Adam(lr=0. softmax(x, axis=1) NAN normally caused by I also used adam, it gives numeric loss and . For small dataset, it works fine. I'm not sure what to change since everything is fine when I use the smaller dataset. import torch import numpy as np import torch. and wrap the optimizer like so: if mixed_precision. Adam(model. loss involving the PDE residual, boundary condition(s), and initial condition(s). value. The accuracy stays around 50% and when I print the predictions for the test set, it only predicts NaN. Because log(0) is negative infinity, when your model trained enough the output distribution will be very skewed, for instance say I'm doing a 4 class output, in the beginning my probability looks like But when I run this new dataset with the augmented images, the model gives nan values for loss and doesn't train on the data. But the model performance is quite bad. Slow and steady training always helps if the network has a good speed of training. The first hundred epochs went well. Stack Exchange network consists of 183 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, SGD Optimizer NAN Loss. @ninenylele The nan (Not a Number) in your training loss suggests that there's an issue with the model training. compile(loss=tf. 0001, clipnorm=1), loss="mae") We rigorously analyze the diagonal empirical Fisher information matrix (FIM) in Adam, clarifying all detailed approximations and advocating for the use of log probability functions as loss, which In this example, we are using Adam optimizer for the parameters of the convolutional layers, and SGD optimizer with a fixed learning rate of 0. 1, 0. The last layer of my neural net is a sigmoid, so the values will be between 0 and 1. Adam(clipvalue=0. This has helped a bit, and it seemed like the loss was going down, but after a few batches, it always jumps to infinity. First, print your model gradients because there are likely to be nan in the first place. frame. I suspect I have my model configured incorrectly, but I can't figure out what I'm doing wrong. These two classes are I'm training a covnet on ~10,000 images and have noticed that switching the optimizer from opt = SGD() to opt = 'adam' leads to massive reduction in accuracies, keeping all else params equal. ] - ETA: 124s - loss: nan Kind of stuck here Am I doing something wrong? Using Tensorflow 1. optimizer = optim. At its core is the Zero Redundancy Optimizer (ZeRO) which enables training large models at scale. $\begingroup$ @hdkrgr I'm using adam optimizer with MSE loss. Epoch 1/200 625/625 [=====] - 1s 530us/step - loss: nan The Adam optimizer, short for “Adaptive Moment Estimation,” is an iterative optimization algorithm used to minimize the loss function during the training of neural networks. info(f'Using LossScaleOptimizer for mixed-precision policy "{mixed_precision}"') optimizer = I'm using Adam optimizer with gradient clipping so not sure how much changing the learning rate will help. The reduced version of code used to test this: DeepSpeed. During the training, the loss is printed, but the When training the loss goes to nan immediately from the first few batches. The first one is Loss and the second one is accuracy. compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy']) model. pyplot as plt from In my experience it usually not necessary to do learning rate decay with Adam optimizer. 00073495, loss = 310. With SGD(), I get to about 80% accuracy (with gradual increases after each epoch). Here is a minimal code snippet: model = nn. We use it together with the Adam optimizer, which is one of the standard ones used today in very generic scenarios, and use loss functions available in Keras and how to use them, how you can define your own custom loss function in Keras, how to add sample weighing to create observation Adam Optimizer Adaptive Moment Estimation is an algorithm for optimization technique for gradient descent. DataFrame'> Int64Index: 2474 entries, 0 to 5961 Data columns (total 4 columns): Age 2474 non-null int64 Pre_Hospitalization_Disposal 2474 non-null object Injury_to_hospital_time 2474 non-null float64 Discharge_results 2474 non-null int64 dtypes: The OptimizerState pytree type used by the returned functions is isomorphic to ParameterPytree (OptStatePytree ndarray), but may store the state instead as e. fit While debugging, I noticed that the loss_scale starts from the default max value and then goes all the way down to 1. (eg. Using relu function sometimes gives nan output. . randn(N, D_out, device=device, dtype=dtype) w1 Use a TrainingOptionsADAM object to set training options for the Adam (adaptive moment estimation) optimizer, including learning rate information, L 2 regularization factor, and mini-batch size. I am using 20 epochs because my data amount is small: I got 1000 images for training and 100 for testing and per batch 5 records. I am using PyTorch this way: optimizer = torch. 4 on Ubuntu 16. '. adam = tf. There are some useful infomation about why nan problem could happen: However with this loss keras keeps showing a nan-loss: Epoch 1/10000 17/256 [>. Skip to main content (4,activation='softmax')) model. reduce_sum( math_ops. 00001] and tried to lower the learning rate, but it doesn’t seem to help. Modified 7 years, 6 months ago. I already tried to set the learning rate very low, but nothing changed. This model tries to predict two states based on an array with 400 numbers. 047459 [729600/235474375] loss: 9. Precision errors, especially when using float16, are also possible but they are The solution is to multiply the loss by some amount, compute larger gradients, then adjust the scaling back down when the optimizer is applied to update the model weights. Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company The loss or prediction can become NaN. These images are 106 x 106 px (black and white) and I have two (2) classes, Bargraph or Gels. The Structure def trainingResNet(source_folder): # Preprocessing image_gen_train = tf. All the other batches resulted in typical loss values. backward() for the optimization. I am using a single layer LSTM with a Dense softmax layer at the end for classification output; Adam optimizer; Categorical crossentropy loss function; Relu activation; For some reason when I use regulizers of any kind on the LSTM layer I get NaN for the loss. During the first training round the model starts with loss on the first +- 200 samples, and then goes into Nan loss. The labels are categorical, and the final activation function is Softmax. After few I’ve been running into the sudden appearance of NaNs when I attempt to train using Adam and Half (float16) precision; my nets train just fine on half precision with SGD+nesterov I have prepared a preconfigured fork of nanoGPT where I provided my configuration and part of my dataset which causes the issue to appear. compile(optimizer=adam, loss='categorical_crossentropy') model. My batch size is 2, and I don’t average the loss over the number of steps. 4117 - val_acc: 0. The learning rate I used was 0. inner_optimizer properties of the Adam algorithm that we found relevant to explaining the loss spike behavior. clip_grad is really large though, so I don’t think it is doing anything, either way, just a simple way to catch huge gradients. activation='softmax', input_shape=[6]) ]) optimizer = tf. This you can see easily. But after that, the loss somehow exploded. In the paper, the authors define this as the SNR (the signal-to-noise ratio). 0000e+00 Epoch 3/5 54600/54600 [=====] - 15s 273us/step If i reduce the learning rate by a lot I'm able to sometimes keep it from going loss NaN. Also my test accuracy is higher than train which is weird. I was having my loss suddenly turn to nan without reaching particularly large values beforehands. I implemented a custom loss function and model for YOLO using Keras. It is As the title clearly describes, the loss is calculated as nan when I use SGD as the optimization algorithm of my CNN model. I also tried changing the values of the learning rate and the batch size. The SNR is a measure that compares the level of a desired signal (here the gradient i. Improve this answer. thanks! Using a custom activation function, when using SGD as an optimiser, except for setting the batch number to an excessively high value the loss will return as an NaN at some stage during training. Using Adam as an optimiser, this happens immediately regardless of batch size. 8526 Epoch 2/6 2682/2682 [=====] - 615s 229ms/step - Edit: I tried running Adam on a smaller dataset with lower learning rate (initial was 0. upqtar qumawn tsnowv evpp qnbfyhc wuuzu yxsi zdlt yhxak kurqtm