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Questions tagged [convergence]

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13 views

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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41 views

Cross entropy loss for sigmoid function

Suppose instead of squared error loss I take cross entropy loss: $$H(y) = - \sum y' \log(y) $$ ( where y' is the actual distribution) . I read somewhere that this loss function converges faster ...
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1answer
24 views

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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0answers
21 views

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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2answers
119 views

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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0answers
34 views

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 ...
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0answers
28 views

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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0answers
10 views

Initialisation of weights for input sensitivity

Iam trying to architect a neural network. I have 3 inputs and I require the output to be as sensitive as possible to changes in the input. The outputs are calculated using 10 hidden ReLU layers and a ...
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1answer
43 views

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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0answers
39 views

Training loss and accuracy is oscillating and failed to converge

I was using AlexNet to do dog&cat classification tasks practice: https://github.com/stephen-v/tensorflow_alexnet_classify While I run the training, the loss oscillated while decreasing, which ...
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0answers
64 views

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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4 views

What are some common sources of variance in a convolutional neural network DICE scores?

I know it's basically impossible to pinpoint the exact answer to this question without data, code, architecture, etc, but I'm curious as to if anyone has any general ideas on why this might be ...
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0answers
310 views

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. ...
2
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1answer
324 views

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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4answers
127 views

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 ...
1
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1answer
951 views

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 ...
2
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1answer
372 views

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 ...
2
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1answer
2k views

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 ...
2
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0answers
467 views

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 ...
1
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1answer
928 views

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 ...
3
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1answer
108 views

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 ...
1
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1answer
4k views

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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0answers
793 views

Neural Network: how to interpret this loss graph?

I have built a deep CNN with TensorFlow that does not classify on one-hot encoded vectors but probability distributions, i.e. given some input $X$ I feed a normal distribution $\mathcal{N}(\mu, \sigma^...
17
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2answers
4k views

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 ...
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4answers
12k views

Number of epochs in Gensim Word2Vec implementation

There's an iter parameter in the gensim Word2Vec implementation class gensim.models.word2vec.Word2Vec(sentences=None, size=...