Questions tagged [convergence]
The convergence tag has no usage guidance.
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How big is the threshold that is usually used in determining the convergence of loss values in deep learning?
In deep learning, one way to determine whether the training has converged is to observe the movement of the loss values over iterations or epochs. One can choose any $\epsilon$ threshold and any ...
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What is the definition of convergence in the context of deep neural networks?
Suppose I have a feed forward neural network which approximates a value, say $Y_0$. The analytical value of $Y_0$ is given. The plot of the network approximation of $Y_0$ each step is given as follows....
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Pytorch Neural Network that tries to approximate $z_i = x_i^2 + y_i^2$ not converging to solution
Background
I am teaching myself Pytorch, as a Mechanical engineering technology (MET) faculty. My end goal is to replace many data-driven heat transfer and Fluid dynamics models with Neural network ...
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How to calculate the KL divergence for two multivariate pandas dataframes
I am training a Gaussian-Process model iteratively. In each iteration, a new sample is added to the training dataset (Pandas DataFrame), and the model is re-trained and evaluated. Each row of the ...
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Is batch size of 1 a valid choice for a very deep neural network with high memory requirement?
I am training a very deep neural network (Panoptic-DeepLab) with a ResNet34 backbone on Google Colab on CityScapes dataset for Panoptic Segmentation, and noticed that, with a big crop size, the batch ...
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What exactly is convergence rate referring to in machine learning?
My understanding of the term "Convergence Rate" is as follows:
Rate at which maximum/Minimum of a function is reached, so in logistic
regression rate at which gradient decent reaches global ...
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What is going on with this kind of validation loss graph?
I am using stock prices and a whole bunch of indicators values to try to get a tensorflow model to predict to buy,sell, or hold. I think im going about this right but when i train the model, first i ...
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Rate of convergence - comparison of supervised ML methods
I am working on a project with sparse labelled datasets, and am looking for references regarding the rate of convergence of different supervised ML techniques with respect to dataset size.
I know that ...
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Does convergence equal learning in Deep Q-learning?
In my current research project I'm using the Deep Q-learning algorithm. The setup is as follows: I'm training the model (using Deep Q-learning) on a static dataset made up of experiences extracted ...
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Do smaller neural nets always converge faster than larger ones?
In your experience, do smaller CNN models (fewer params) converge faster than larger models?
I would think yes, naturally, because there are fewer parameters to optimize. However, I am training a a ...
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Force Matching in Coarse Grained Molecular Dynamics with Jax - Forces do not match when neglecting energy loss
I am currently exploring force matching approaches for molecular dynamic simulations.
As I am still in an exploration state, I'd tried investigated
Force Matching Neural Network Colab Notebook
...
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ElasticNet Convergence odd behavior
I am optimizing a model using ElasticNet, but am getting some odd behavior. When I set the tolerance hyperparameter with a small value, I get
...
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Uniform convergence garantee on sample complexity
I can't understand why the Uniform Convergence guarantees an upper bound and not a lower bound on sample complexity as stated on [1]
Corollary 4.4.
If a class $H$ has the uniform convergence property ...
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Logistic regression does cannot converge without poor model performance
I have a multi-class classification logistic regression model. Using a very basic sklearn pipeline I am taking in cleansed text descriptions of an object and classifying said object into a category.
<...
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How do i iterate functions until convergence in R?
I am looking to iterate until convergence but I am not sure how i should code it.
A simple similar example is something like
$X_0=5 , Y_0=3$
for $n=1,2..$
$P_n=X_{n-1}-Y_{n-1}$
$Y_n=3P_n$
$X_n=P_n+...
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How to assess that a cross entropy based model has converged
I have a question regarding cross entropy convergence using Stochastic Gradient Descent. I am a little bit confused about how the convergence should be assessed. Should we treat the model as converged ...
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Why GA convergence curves continue as two parallel lines?
I'm working on a optimization problem and using GA algorithm (in MATLAB, ga function).
As you know MATLAB plots GA result with two curves, one for the best values and other to show the mean values ...
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Is there a universal convergence rate when stacking models/experts?
It's fairly common to see people stacking different models when chasing marginal gains in contexts such as Kaggle competitions or the Netflix challenge. I would like to know about the mathematics ...
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1
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How can we conclude that an optimization algorithm is better than another one
When we test a new optimization algorithm, what the process that we need to do?For example, do we need to run the algorithm several times, and pick a best performance,i.e., in terms of accuracy, f1 ...
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Help with MLP convergence
I posted this question on AI SE and got advised to ask here for guidance. I've been stuck for a couple of days trying to figure it out how the standard MLP works and why my code doesn't converge at ...
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Python lifelines - ConvergenceWarning: Newton-Raphson failed to converge sufficiently in Cox prop hazard
When calling CoxPHFitter() on my full dataset I'm getting the following error:
...
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Huge cost not converging well with TensorFlow logistic regression
I try to use Logistic Regression for a dataset which contains 15 numeric features and 4238 rows of examples. The calculated cost started at 415.91, and converged when the cost was reduced to 220.119 ...
user77241
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How does permutation of training data improve convergence time when training a perceptron or neural network model? [duplicate]
I'm currently studying some basic concepts regarding Deep Learning and Neural Networks with this material.
When discussing the training algorithm for a perceptron, the author states that looping ...
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Can one use non converged results from Logistic Regression?
I'm running Logistic Regression on a dataset for a classification problem.
I used the model on the dataset when it was normalized and I had no problem with it converging.
Now, I wanted to see the ...
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sequence convergence - python
Let $\{p_i : i \in \mathbb{Z}\}$ be an i.i.d sequence with $p_i \in (0, 1)$.
Fix this random environment, then consider the random walk
$$P[ X_{n+1} = i + 1 | X_n = i] = 1 − P[ X_{n+1} = i − 1 | X_n =...
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LSTM not converging
I am sorry if this questions is basic but I am quite new to NN in general. I am trying to build an LSTM to predict certain properties of a light curve (the output is 0 or 1). I build it in pytorch. ...
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DQN fails to find optimal policy
Based on DeepMind publication, I've recreated the environment and I am trying to make the DQN find and converge to an optimal policy. The task of an agent is to learn how to sustainably collect apples ...
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Weights not converging while cost function has converged in neural networks
My cost/loss function drops drastically and approaches 0, which looks a sign of convergence. But the weights are still changing in a visible way, a lot faster than the cost function. Should I ensure ...
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Recurrent Neural Network (LSTM) not converging during optimization
I am trying to train a RNN with text from wikipedia but I having having trouble getting the RNN to converge. I have tried increasing the batch size but it doesn't seem to be helping. All data is one ...
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Very slow convergence with CNN [closed]
I am new to deep learning. I am working on training an SSD model on a set of small objects. I am using Adam gradient descent for optimization and a large input (800x800), but I seem to only get an ...
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Why this model does not converge in keras?
This case has an underlying story but I have essentially boiled it down to the simplest possible re-producible example I could.
Essentially let us think that I have up to 1000 nodes and each node ...
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Validation loss keeps fluctuating about training loss
I am training a Keras model for multi-target regression by using a custom loss function with the goal of getting predictions accurate to below 0.01 with respect to ...
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Convergence of vanilla or natural policy gradients (e.g. REINFORCE)
I am reading in a lot of places that policy gradients, especially vanilla and natural, are at least guaranteed to converge to a local optimum (see, e.g., pg. 2 of Policy Gradient Methods for Robotics ...
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Normalizing the final weights vector in the upper bound on the Perceptron's convergence
The convergence of the "simple" perceptron says that:
$$k\leqslant \left ( \frac{R\left \| \bar{\theta} \right \|}{\gamma } \right )^{2}$$
where $k$ is the number of iterations (in which the weights ...
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need explanation on how an equation is being converted to cvxopt logic in solver.lq
This is the equation that is given in the example:
and the code to replicate it in python is
...
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Is the percepetron algorithm's convergence dependent on the linearity of the data?
Does the fact that I have linearly separable data or not impact the convergence of the perceptron algorithm?
Is it always gonna converge if the data is linearly separable and not if it is not ? Is ...
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local minima vs saddle points in deep learning
I heard Andrew Ng (in a video I unfortunately can't find anymore) talk about how the understanding of local minima in deep learning problems has changed in the sense that they are now regarded as less ...
oW_♦
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Number of epochs in Gensim Word2Vec implementation
There's an iter parameter in the gensim Word2Vec implementation
...
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