# Questions tagged [sgd]

Stochastic Gradient Descent (SGD) is an iterative algorithms used for objective function optimization used in machine learning models.

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### Is learning_rate linear with the time to converge using AdamOpt?

Say that both learning rates 1e-3,1e-4 leading to the same solution (not too high or too small). In terms of convergence by the amount of epochs, does ...
1 vote
24 views

### Multiple models have extreme differences during evaluation

My dataset has about 100k entries, 6 features, and the label is simple binary classification (about 65% zeros, 35% ones). When I train my dataset on different models: random forest, decision tree, ...
1 vote
16 views

### How exactly do you implement SGD with momentum?

I am looking up sources to implement SGD with momentum, but they are giving me different equations. (beta is the momentum hyper-parameter, ...
216 views

### Can't use The SGD optimizer

I am using the following code: ...
15 views

### Estimating a rbf kernel SVM, followed by Stochastic Gradient Descent

I wanna estimate a rbf SVM to predict property prices. My data set has 11 features and roughly 57,000 rows. When I set C=10, R^2 is about 0.88 while MSE and RMSE are 0.1191 and 0.3451. The results are ...
42 views

### Stochastic Gradient Region of Confusion

I have come across the following diagram which explains the behavior of SGD graphically. Based on this graphical representation, the gradient of the individual data tend to fluctuate more when it ...
1 vote
55 views

### Why does using Gradient descent over Stochatic gradient descent improve performance?

Currently, I'm running two types of logistic regression. logistic regression with SGD logistic regression with GD implemented as follows ...
177 views

### Learning rate of 0 still changes weights in Keras

I just trained a model (SGD) with keras and was wondering why the change of accuracy and loss from epoch to epoch doesn't really decrease that much when I lower the learning rate. So I tested what ...
913 views

### Changing the batch size during training

The choice of batch size is in some sense the measure of stochasticity : On one hand, smaller batch sizes make the gradient descent more stochastic, the SGD can deviate significantly from the exact ...
1 vote
2k views

### input shape of keras Sequential model

i am new to neural networks using keras, i have the following train samples input shape (150528, 1235) and output shape is (154457, 1235) where 1235 is the training examples, how to put the input ...
104 views

### Problem of multi class classification (Sklearn TfidfVectorizer and SGDClassifier)

I do the (text) topic classification using TfidfVectorizer and SGDClassifier, literally I want to classify the website into categories (like Sport, Business etc). Now, the problem is, that each ...
1 vote