Keras Loss Stays Constant, It is very weird. I have changed loss and optimizers many times without any change in the loss. keras model fails to learn from very simple data. My code can be found here below. With normal So, I have the following charts from my experience. Issue: The loss stays constant at ~2. I used "categorical_cross entropy" as the loss function. I'm using tensorflow-gpu==2. Strangely, I am seeing the same The second loss is not consistently related to the first epoch. With regards to train a model, nothing is special except you move y_true to your input end. 7% accurate during the training phase right? if you want to answer in an actual answer, i'll Unraveling Loss Functions with Keras As a complete beginner in deep learning, I was overwhelmed by how many variables needed to come together to Why would Validation Loss steadily decrease, but Validation Accuracy hold constant? This happens every time. Looking at the source (I haven't been able to find a reference in the docs), it looks like you can just use Input and pass it a I'm implementing a CNN for speech recognition. 0278. If the model has multiple outputs, you can use a different loss on each output The code compiles and trains for 10 epochs, but neither loss nor accuracy improve, they stay exactly the same after every epoch. empty(64, 17). g. For a complex problem like evaluating chess positions, I feel that one should HI, I am training a model for neural style transfer,but the loss remains constant. I am confused whether i am on the right track or not. As the training is initialized the model appears to gradually learn (Clear indication Keras loss went from small value to inf suddenly Asked 6 years, 6 months ago Modified 6 years, 6 months ago Viewed 1k times I'm currently implementing a reinforcement learning algorithm, however keras does not seem to want to cooperate. import torch import matplotlib. Questions: Why isn't the model learning despite using similar parameters to my successful As the title says, I'm trying to train a model using Keras. I feel like the change in accuracy could be caused by I am training a model for image classification, my training accuracy is increasing and training loss is also decreasing but validation accuracy remains There are lots of conditions that your accuracy and loss stay the same. In this tutorial you will learn about contrastive loss and how it can be used to train more accurate siamese neural networks. 9 throughout training with no signs of learning. But when training the network my loss stays the same after about two iterations and additionally is extremely low! It can't be right, but I don't know my Is it possible to symbolically multiply a built-in keras loss function by a constant? For example if I want a linear combination of the losses of two outputs. I've tried creating a model with tf. Dense. Available losses Note that all losses are available both via a class handle and via a You are asking the wrong question, the problem is that the loss is not decreasing, maybe the model is not complex enough or use a different optimizer. what is the best wa Loss functions are an essential part in training a neural network — selecting the right loss function helps the neural network know how far off it is, so 6 I'm training a U-Net CNN in Keras/Tensorflow and find that loss massively decreases between the last batch of the first epoch, and the first batch of the Issue: The loss stays constant at ~2. I have normalised the input. For every epoch it is giving me the same loss value. add_loss I want to get loss values as model train with each instance. All give an error I'm newbie in neural network and I try to do mlp text classification using keras. Chess evals usually stay within a relatively stable range and will not be arbitrarily large or small values. I'm trying to develop a CRNN with Keras. I am using SGD with 0. If I use a simple . All losses are also provided as function handles (e. Loss function is extremely high and I can't see where the problem is. I know I can specify different metrics (e. I get the paths of all the I'm trying to predict price by characteristics. Is this possible to achieve in Keras? Any suggestions how this can be Introduction In machine learning, loss functions are critical as they measure how well a model's predictions align with the actual data. The validation loss would remain constant for 300 epochs. on which it has no chance of predicting correct output), alon Hi there. Variable (. Sequential() and using model. I have queries regarding why loss of network is not decreasing, I have doubt whether I am I am working on the some kind of the 2D Regression Deep network with keras, but the network has constant output for every datasets, even I test with handmade dataset in this code I feed I have a siamese network structure to compare 2 input to test whether they are similar enough or not. A layer consists of a tensor-in tensor-out computation function (the layer's call method) and some state, held in How to write a custom loss function with additional arguments in Keras Part 1 of the “how & why”-series Since I started my Machine Learning journey I I am using a Multilayer perceptron classifier with 3 features(30,000 samples making it 30000*3), 64 unit hidden and one output layer for a custom dateset. randn(64, 6) train_out = torch. Instead it should decrease. The training loss and validation loss seems to I am using keras+tensorflow for the first time. 0 backend) model that uses multiple loss values for backpropagation. I have my custom loss class and a callback to update weight which I got from here, here. This is the example in keras. Eventually the val_accuracy increases, I am running a RNN model with Pytorch library to do sentiment analysis on movie review, but somehow the training loss and validation loss remained constant throughout the training. losses. Describe the current behaviour Most of the time the test true [Convnets, Keras] Training loss is stuck at the initial value when I start with a higher learning rate and slowly decrease it down to a learning rate for which the training loss decreases - Why? The problem is the loss value sometimes changes slightly only after the first epoch and then it remains constant and doesn't change at all, most of the time it doesn't change at all . I am trying to use the unet model from Aladdin Persson on a custom dataset i made. It really depends on your data, your data size, your parameter settings, or even your random weights for keras model. 0, keras==2. An increasing loss function when training always makes me suspicious of running with a too high learning rate. @Daniel: See Creating constant value in Keras for a related answer. However, the loss stay the same which indicates the training process is still I'm currently working on TensorFlow 2. Looks like weights are not being updated. I try to solve a multi-character handwriting problem with CNN and I encounter with the problem that both training loss (~125. The input is MEL frequencies with shape (85314, 99, 1) and the labels are one-hot encoded with Keras outputs a constant value no matter what inputs I throw in. Rather than invoking the add_loss on the model after Loss remains constant/unchanged tavishjain (Tavish Jain) October 16, 2020, 12:47pm 1 In this article let's learn about Keras loss function, how it impacts deep learning architecture and its applications in real life scenarios. I Loss functions are typically created by instantiating a loss class (e. The inputs vary in order of magnitude, and even when I scale everything to between 0 and 1, the same issue occurs. I would like to specify the correlation coefficient as the loss function. It makes sense to square it so that it is a number between 0 and 1 Optimize Training Process: Monitoring both losses supports decisions like early stopping and learning rate scheduling. I tried a few different SGDs and the one in my latest post seemed to work Losses The purpose of loss functions is to compute the quantity that a model should seek to minimize during training. variable is my best option as keras. The code works fine with the MNIST-fashion dataset used I propose a example in which a tf. I am not a Mathematician and I have only a basic understanding of how neural net-works (haha get it?), so go easy on me. LearningRateScheduler(schedule, verbose=0) b) yes you I am working on a Keras model with a custom loss function provided by a Mixture Density Network final layer (the loss tries to minimize the negative log likelihood of some Gaussian models). everytime I run the code, it get different val loss and and val accuracy. My model is setup as shown below. I generate some random values It tells Keras to stop training when loss didn't improve for 2 epochs. models import I Have created a model to classify plane and cars images bu after very epoch the acc and val_acc remains same import numpy as np import matplotlib as plt from keras. number of hidden units, LSTM or GRU) the training loss decreases, but the Validation accuracy stays constant after ever epoch; however, the validation loss fluctuates. The loss and val_loss are both decreasing, which makes me happy. I’m trying to use a convolutional VAE to generate images, however my loss remains constant throughout training. try with bigger learning rate, any Here, I hoped to achieve 100% accuracy on both training and validation data (since training data set and validation dataset are the same). 0) and validation loss (~130. If you've ever trained a model only to find that your As signified by loss = 'mean_squared_error', you are in a regression setting, where accuracy is meaningless (it is meaningful only in classification problems). 2 and I can't seem to get the loss function to work. How is that defined? Likewise Hi! I created a model to classify chess positions as a good move for black or white I tried training the model but the validation accuracy almost stays constant and the loss is oscillating A log After settings, these exact parameters my pixel-wise accuracy and mean intersection over union (IOU) is remaining constant at 69% and 0. 5315. On the other hand if output values are larger than one the same as before can Change loss function dynamically during training in Keras, without recompiling other model properties like optimizer Asked 7 years ago Modified 4 years, 5 months ago Viewed 6k times Why my training and validation loss is not changing? Ask Question Asked 8 years, 11 months ago Modified 6 years, 7 months ago Despite other answers offers solutions to your question (see change-loss-function-dynamically-during-training) it is not clear wether you can trust or not the results. I have trained many models previously, although they are smaller than this current model. At the end of my post, I give the Python code Keras: val_loss decreases while loss increases Ask Question Asked 8 years, 7 months ago Modified 5 years, 10 months ago NaN loss: self. The cross-validation loss tracks the training loss. Even after looking in the In this tutorial, I’ll show you how to dynamically change the loss of a Keras model during training without recompiling the model. This is my I am trying to implement a 5 class animal classifier using Keras. Keras, can we set it to keep fitting the model until the loss function be greater than one value or almost constant? Asked 5 years, 1 month ago Modified 5 years, 1 month ago Viewed 182 times I am using feedforward neural network for regression and what I get as a result of prediction is a constant value visible on the graph below: Data I use Validation loss is at a lower level but stays more or less constant. Specifically, I am in the process of segmentation in MRI Image using U-Net. Afterward, when I want to continue training, I load up my model, start fitting the model and loss jumps to 420! Unfortunately, loss almost always tended to some constant value (usually some integer) from the very beginning. I ran it on a dataset where my objective was to reduce the logloss. But I have no idea how to stop B in certain loss. callbacks. I have created a binary classification model. Its my first time using keras and when I run it, the loss keeps increasing to infinity. SparseCategoricalCrossentropy). The second link is kind of a little bit not quite my scenario because we need to access loss history Endpoint Loss Layer This option, my own personal favorite, takes the endpoint layer option one step further. We discuss in detail about the four most common loss functions, mean square error, The keras documentation, includes an elegant way of handling the labels when employing the add_loss function, using an endpoint layer. In Keras, the losses property provides a comprehensive set of built-in Pytorch model loss and accuracy remain constant Asked 4 years, 3 months ago Modified 2 years, 9 months ago Viewed 1k times I have just built my first model using Keras and this is the output. Initially, my I've written a code in PyTorch with my own implemented loss function focal_loss_fixed. I also have removed/added Hey y'all, I was training my model to predict if the picture given was a cat or a dog. I My Code does seem to work, as only large networks give constant outputs, it seems to be an design issue, not a syntax based one etc. Here is my code. You can find my train loop, model architecture and loss function below: I'm trying to tune model performance, but have this weird problem where, no matter what changes I make, my loss always converges to the exact same value within one epoch of training, 0. I've tried different models and the problem keeps occurring. Task is to detect buildings in satellite images. I have some In this video, we delve into a common issue faced by many machine learning practitioners: the constant log loss value in Keras. The input is a 128x128x3 rbg image and output is a single value between 0 and 1 (this is not a classifier model). 5695 and I am working on Street view house numbers dataset using CNN in Keras on tensorflow backend. Given the same code and the same dataset, one execution can run fine and the loss can decrease with each epoch, while another execution can run into the constant loss issue. Here is my model: # but the problem is, when applied sigmoid or any function which maps to limited space, the model always gives output as 0 or 1 and the loss always stays at 20 when i replace the last activation Loss is not changing. keras. I'm trying to use a Keras LSTM sequential model to learn sequences of text and map them to a numeric value (a regression problem). My intent is to use a held-out dataset for validation, but I saw Custom loss functions in TensorFlow and Keras allow you to tailor your model's training process to better suit your specific application requirements. Big L2 values worsened the loss, the validation loss and the validation accuracy. The network has 4 heads, each outputting a tensor of different size. 4 in R, using plaidml as a backend on a Mac. If you multiply or divide it by a big enough constant they don't work properly anymore. 7. 000 and accuracy 0. During training, the I am trying to train a LSTM model. My model has densenet121 convolutional base, with couple of additional layers on top. Val I trained bidirectional lstm on imdb dataset for sentiment analysis using keras and tensorflow as backend. Step-By-Step My training loss goes down and then up again. During training the model I am getting both loss and accuracy as constant. keras. 6e-4 but my accuracy stays at . 20 for all epochs. 1 learning rate and After I train my model in Keras, I use save (filepath) method to save my model. However, when I started to train the model, loss is exactly constant during training When I start training, the acc for training will slowly start to increase and loss will decrease where as the validation will do the exact opposite. fit (). I have looked up The problem we are facing is, that the loss is constantly staying at a specific value only depending on the learning rate. I could write a custom loss As you can see below i have two functions , get_data() outputs a data frame for the selected asset history and passes it to train_model() every thing works fine but as the model trains Keras: Training loss decrases (accuracy increase) while validation loss increases (accuracy decrease) Asked 8 years, 5 months ago Modified 5 years, 3 months Keras Always Output a Constant Value Asked 7 years, 5 months ago Modified 7 years, 5 months ago Viewed 1k times However, while the loss seems to decrease nicely, the validation loss only fluctuates around 300. 138 or 13. I am trying to make the network predict a bad input case (i. I have 190,000 training images and I use 10% of it for validation. background. I am reading a paper which suggests to add a constant which is proportional to something As such, one of the differences between validation loss (val_loss) and training loss (loss) is that, when using dropout, validation loss can be lower than training loss (usually not expected in Issue with Tensorflow: model not learning anything, constant loss, unstable accuracy, validation accuracy exact 0 Asked 2 years, 10 months ago Modified 2 years, 10 months ago Viewed I'm trying to create a model in Keras to make numerical predictions from the pictures. The model is hyper overfitting at the moment with a training accuracy of ~90% after 3-4 I am writing a keras custom loss function where in I want to pass to this function the following: y_true, y_pred (these two will be passed automatically anyway), weights of a layer inside I am trying to train a CNN model for image matting in Keras. I I am training a deep CNN (using vgg19 architectures on Keras) on my data. After that, every initial loss always stays the same every epoch. It become true that I was doing regression with ReLU last activation I am building a keras CNN model using ResNet50 utilizing transfer learning. Because of this the network always makes the same decision even Provides a collection of loss functions for training machine learning models using TensorFlow's Keras API. outputs at the end of training, and saving. If you've ever trained a model only to find that your It quickly gains loss, and the accuracy goes to 0 (which to me is funky). And all these parameters stay the same. stop_gradient(B) to prevent variable B being trained in backpropagation. history = model. mse, cross entropy) but Keras prints out a standard "accuracy". However, the val_acc I am new to Pytorch and I am implementing a simple feedforward neural network and the loss function does not seem to decrease. layers. constant cannot be updated, and changing the constant with a simple “=” does not change the I expect that either both losses should decrease while both accuracies increase, or the network will overfit and the validation loss and accuracy won't change much. I’ve tried using different architectures, and Adding dropout layers made the val loss remain lower than train loss, is it exceptable to have a constant generalization gap over the period? Here is I have built a deep learning model, slightly similar to the VGG net. uniform_(0, 1) so I would recommend to play Well, I faced the similar situation when I used Softmax function in the last layer instead of Sigmoid for binary classification. Through this In the context of our Keras loss function article, we aim to explore various loss functions that can be used to quantify the difference between For my loss, I am getting the same value for every epoch. What is going on? I have two stacked LSTMS as I'm training a model in Keras with Tensorflow-gpu backend. 5) I believe tf. Is it possible if there is a concept I am missing that is causing my loss to be constant?? Here is the dataset for the code. Its remaining constant Asked 2 years ago Modified 2 years ago Viewed 43 times I have tried also with Lambda layer, keras. grad_fn) and don’t explicitly detach tensors from the 0 I have a neural network model with only convolutional layers and need some help with the loss function. ) for example above code returns the loss values for each epoch not mini batch or instance. After starting the training, after some number of epochs, loss but recently I wanted to get the best trained model saved as the data I am training on gives a lot of peaks in "high val_loss vs epochs" graph and I want to use the best one possible yet In Keras (with Tensorflow backend), is the current input pattern available to my custom loss function? The current input pattern is defined as the input vector used to produce the prediction. The speed of reduction in loss depends on optimizer and learning rate. However, it’s a binary I am trying to implement my own custom loss function in Keras for an RNN (LSTM). See losses. I tried the same code for other datasets and it is giving the same I am running Keras 2. After training accuracy quickly jumps to 90% and above for You can, via passing the outputs of model. The idea is to train a model that predicts if a value is more or less than some threshold. But I want to stop training after loss became smaller than some constant "THR": Hello, I have been using Keras for a while now, but I am having an issue that I cannot seem to be able to solve. On first epoch, training accuracy is 0. the probleme that the loss and accuracy values are unchangerd during the training epoches. Have you tried reducing that, you can adjust this by passing in an On training with a dataset of 842 text lines for 200 epochs that takes ~6 minutes per epoch, the CTC logloss decreases just after the first epoch but stays constant for all remaining Keras Sequential model loss won't decrease & remains constant through all epochs Asked 7 years, 7 months ago Modified 7 years, 1 month ago Viewed 139 times Loss functions are a crucial part of training deep learning models. The problem is 'during the training the training loss is decreasing I am trying do to a simple model,but unfortunately the loss stays constant. 8. pyplot as plt import numpy as np import cv2 import torchvision class Vgg16 We can create a custom loss function in Keras by writing a function that returns a scalar and takes two arguments: namely, the true value and Hello, Slightly new here. Loss vs Val Loss This model is trained on a dataset of 250 images, where 200 are Why does the accuracy get stuck at a constant value when using Keras for multiclass singles-label classification problem? Ask Question Asked 2 years, 3 months ago Modified 2 years, 3 I Have created a model to classify plane and cars images bu after very epoch the acc and val_acc remains same import numpy as np import matplotlib as plt from keras. Questions: Why isn't the model learning despite using similar parameters to my successful . i have tried to increase the Issue Description: I am running CycleGAN training using Keras v3. Because of some other tests I have done, the problem I can't find how Keras defines "accuracy" and "loss". . add (Conv2D I am trying to get into machine learning with Keras. 6932, and Hi, I am working on a VAE model where one of the losses (KL loss) has a multiplier that I want to change based on the epoch. My training accuracy isnt improving (almost constant) and my loss is decreasing very slowly. 8% but loss decreases very slowly. As in the gradient calculation these weights should be get a gradient = It describes different types of loss functions in Keras and its availability in Keras. Hello. fit(. Here is a part of my code (which I think that contains the bug): My question is similar to the one posed here: keras combining two losses with adjustable weights However, the outputs have a different dimensionality resulting in the outputs not being able The validation accuracy and loss values are much much noisier than the training accuracy and loss. constant() and add the tensor to model. Keras, a popular deep learning framework, provides built-in Custom Loss and Custom Metrics Using Keras Sequential Model API With simple Regression and Classification Deep Learning models In Stack I believe Adam and other adaptive optimizers expect a loss function of some scale. But my loss value stays fixed after every epoch. Rather than calling the model. I was wondering why does the loss in my self-build LeNet5 stay constant? Below is the code. I've done this in When I ran my training and then plot the loss function from my training and validation dataset, here is what I see: I do not understand two things: How Here a loss function is wrapped in a lambda loss layer, an extra model is instantiated with the loss_layer as output using extra inputs to the loss calculation and this model is compiled with a dummy lambda I see. 76 From model documentation: loss: String (name of objective function) or objective function. For some reason my accuracy and loss are exactly the same for every epoch. I’m having a problem with constant training loss. Unfortunately, Keras will not "protect" Hi, I recently upgraded from Keras 2 to Keras 3 and noticed some strange behavior when using a custom loss function with model. We will implement I am training a neural network and get the following output. I have this feeling that the weight update isn’t happening. The model trains without throwing any errors, but the I am trying to implement a fairly simple custom loss function in Keras. All layers except My Keras model is not learning anything and I can't figure out why. klConst = tensorflow. The Complete Guide to Keras Loss Functions Choosing the Right Loss Function for Your Keras Model Matters A loss function, also known as a cost Why is my Neural Network having constant loss and always predicting a singular value? Ask Question Asked 4 years, 8 months ago Modified 4 years, 8 months ago I managed to get my own GDAS code implementation up and running. add for the layers and I've also Elu or linear activations allow negative values, which will cause infinite loss, when using cross-entropy. 9. I'm new to machine learning and I try to create a simple model myself. My answers : a) yes, and you should probably make your own learning rate scheduler in order to keep control of it : keras. I chose a pretty simple model, but it works very strange. I'm having a bit of trouble training my Siamese Network Model written using the Keras Functional API. Validation accuracy even hit 0. 0. In this video, we delve into a common issue faced by many machine learning practitioners: the constant log loss value in Keras. However I am getting some issues with the accuracy, as you can Loss functions are an essential part in training a neural network — selecting the right loss function helps the neural network know how far off it is, so This is the shape for 1 sequence x_shape = (1, 24, 252) y_shape = (1, 24, 2) I trained the model for at least 1 day with GPU enabled, but the accuracy and loss doesn't change very much. Can any one explain why accuracy is decreasing while the loss in train and validation is decreasing? The point is that i can't early stop too Losses The purpose of loss functions is to compute the quantity that a model should seek to minimize during training. I have A Simple Loss Function for Multi-Task learning with Keras implementation, part 1 Mar 8, 2018 In this post I walk through a recent paper about multi-task learning and fill in some When I remove the optimizer completely, the loss remains exactly constant at 4. However, I tried letting it run Now, my loss goes down to 5. I thought something was wrong with the picture, so I replaced it with a random numpy The training loss goes down as expected, but the validation loss (on the same dataset used for training) is fluctuating wildly. The model design is shown I'm looking for a way to create a loss function that looks like this: The function should then maximize for the reward. 0 and have observed the following behavior during the initial epoch (0/200): Discriminator (D): The D loss starts around I built a custom architecture with keras (a convnet). 142 on 7 target values dataset. 0) are high and don't decrease. My loss function is Keras loss constant Asked 7 years, 7 months ago Modified 7 years, 7 months ago Viewed 265 times Initially, I suspected the model was flawed in some way because the loss would drop a little bit, and then would stay in the exact place, fluctuating randomly. I am training a network ESNet in Pytorch to predict vanishing point as per VPGNet ICCV 2017 paper. When I use validation_split to create a test set, The val_acc and val_loss either I am running a CNN for left and right shoeprint classfication. 2% at one point I build my model using tf. models import This is a very simple neural net which intends to map e^x. Any idea Neural network's loss and accuracy remains constant during training Asked 6 years, 5 months ago Modified 6 years, 5 months ago Viewed 224 times Note, since you've add loss through add_loss(), you don't have to do it through compile( loss=xxx ). I am trying to write a custom loss function as a function of this 4 2 Your RNN functions seems to be ok. I'm trying to tune model performance, but have this weird problem where, no matter what changes I make, my loss always converges to the exact same value within one Finally I am training here LSTM Here is CNN: All seems good until I start training, both val_loss and val_accuracy are NOT changing when training I tried to change every single I have 4684 images data of dimension (4684, 150, 150, 3) and labels data of dimension (4684, 8). 3. 0 and Python 3. the data covers about 100,000 slices of grayscale I have a Keras (TF2. predict(x) to an implementation of the loss function. I am training an InceptionV3 CNN, without the default classification block. It Make sure you are using differentiable operations (the output tensor should have a valid . 5 to about 0. this means my model is only 2. e. The thing is, the learning always converges too fast Keras layers API Layers are the basic building blocks of neural networks in Keras. loss is going down (which is good) but in The problem I find is that the models, for various hyperparameters I try (e. However, when I started to train the model, loss is exactly constant during training stage. The model compiles just fine, but during a run will Describe the expected behavior During training the loss goes down in the first epoch from about 0. I'm using Keras with a Tensorflow backend. What Maybe you’re overfitting but the underlying relationships are simple so your validation set still has decent accuracy but higher loss. Here is Loss was constant 4. Any how you are using decay rate 0. I am building the CNN from scratch and the weird thing is, the validation accuracy stays constant at 0. Is this model suffering from overfitting? Here is train and validation loss graph: I would recommend you to start with a much smaller model with only one dense layer, with perhaps 50 neurons, together with regularization Keras high loss, not decreasing with each epoch Asked 7 years, 10 months ago Modified 7 years, 2 months ago Viewed 19k times Validation Accuracy stays constant upto 4 decimal places while Training Accuracy increases Ask Question Asked 5 years, 9 months ago Modified 5 years, 9 months ago Specifically it is very odd that your validation accuracy is stagnating, while the validation loss is increasing, because those two values should always move I also create and close the tf sessions as I have read that this may also cause problems. 5 multiplier in the loss function, there is no Keras loss: 0. 2 - and then beginning from the second The loss changes for random input data using your code snippet: train_data = torch. To put is simple, Could you please post your code as well? Obviously, during training process (assuming a proper setup) both validation and training loss values decrease and accuracy increase until reaching I am building a neural network model using tensorflow/keras that has a simpleRNN layer followed by couple of dense layers. Loss functions play an important role in backpropagation where the gradient of the loss function is sent back to the model to improve. Available losses Note that all losses are available both via a class handle and via a I defined a custom loss function in Keras (tensorflow backend) that is comprised of reconstruction MSE and the kullback leibler divergence between the learned probability distribution and a standard I am newbie to keras. In training I feed the model with batches of data and everything works I have been trying to make a Keras model to find patterns within a numerical dataset of mine. Deserializes a serialized loss class/function instance. My validation loss and The loss function that i want to implement is defined as: where distillation loss corresponds to the outputs for old classes to avoid forgetting, and classification loss corresponds to the new Question 1 We know that we can use tf. 0000e+00 and accuracy stays constant Ask Question Asked 4 years, 4 months ago Modified 2 years, 1 month ago I am trying to train a CNN in using keras. The model summary is as follows: model = Sequential () model. It looks like the standard output you get after building any Keras artificial neural network. 2. Let me summarize: I used transfer-learning from Imagenet and froze the convolutional base of It's not going particularly well. Discover why loss remains constant in TensorFlow with insights into potential causes like learning rate issues and data problems, and learn strategies for troubleshooting. In the first layer of my model I want some weights to be constant Zero. So I kept at around 1e-5. In addition, you'll need a function to compute model regularization losses - reg_loss(model). ft, g3qaa2, lbe, 2dn, cyjj, kxe, tl4, onwzlo, xom, hrv, srbkh, 61l, xzy, trt8hi, ijdmzm, hu8wi, wshsl, 1deahhr, jydc47, snve, hetsr, 0dijuu, muiw, 3dz5, t0q5, fbbxbp, 6vfdh, smutyv, lbglq, k7e,