Questions tagged [adam]
An adaptive algorithm for gradient-based optimization of stochastic objective functions, often used to train deep neural networks.
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Pathological curvature
How common is the issue of pathological curvature? I have been reading a post on the internet about it, and I would like to know how common this happens with deep learning models.
Could you also ...
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2answers
695 views
Training a neural network on chess data
I have been writing a chess engine with a friend and the engine itself is really good already (2700+ CCRL). We had the idea to use a neural network to have a better evaluation of positions.
Input to ...
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24 views
Momentum vs Polyak averaging
I'm going through this deck but don't quite get the difference between momentum and Polyak averaging, and what role Polyak averaging plays in modern optimizers.
For example, is it correct to say that ...
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0answers
17 views
ADAM First and Second Moments
I'm trying to understand why ADAM uses the gradient and the gradient squared respectively as estimators the first and second moments. I was assuming that the random variable we were estimating was the ...
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1answer
50 views
What is a stochastic objective function?
In the paper "ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION" they write in the introduction "Often, objective functions are stochastic."
I know what a stochastic gradient descent is, but what does it ...
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0answers
20 views
Adam converges while SGD does not improve at all
I am trying to build a model based movie recommendation system with a neural network.
The architecture looks as follows:
...
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1answer
25 views
Do we have guarantees about Adam's convergence when we reach an region with gradient $0$?
Recall the Adam update rule:
$$m_t = \beta_1 m_{t-1} + (1 - \beta_1) g_t$$
$$v_t = \beta_2 v_{t-1} + (1 - \beta_2) g_t^2$$
$$\hat{m}_t = \dfrac{m_t}{1 - \beta^t_1}$$
$$\hat{v}_t = \dfrac{v_t}{1 - \...
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1answer
37 views
step size in the first epochs of adam are too large [closed]
When I train models in keras with keras.optimizers.Adam(learning_rate=0.001), I typically get a history of the training error over the training time in epochs like in the plot below.
This looks like ...
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0answers
224 views
understanding bias correction in ADAM algorithm [duplicate]
why is possible to approximate E[gi^2] with E[gt^2]? by the time we go to the t timestamp, we've already made weight updates, which mean gradients should be different as they are taken from different ...
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1answer
70 views
Different optimization behaviours on delta vs non-delta targets
I built a simple classification model that is required to predict a probability distribution for a set of two available classes.
The target distributions are not necessarily delta distributions. i.e ...
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0answers
19 views
SGD versus Adamax on XOR operator
I am trying to resolve the xor operator using neural networks, and to accomplish that this is my code:
...
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1answer
941 views
Combining Random forest with Adam (or an other gradient method)
There is no "gradient" in the standard Random Forest formulation, but can I combine random Forests with an optimisation method like Gradient Descent or SGD?
Can I use Adam (Adaptive moment estimation)...
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2answers
763 views
How well should I expect Adam to work?
I've been coding up a neural network package for my own amusement, and it seems to work. I've been reading about Adam and from what I've seen it's very difficult to beat.
Well, when I implement the ...
5
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1answer
3k views
What does decay_steps mean in Tensorflow tf.train.exponential_decay?
I am trying to implement an exponential learning rate decay with the Adam optimizer for a LSTM. I do not want the 'staircase = true' version. The decay_steps for me feels like the number of steps that ...
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2answers
1k views
Why we call ADAM an a adaptive learning rate algorithm if the step size is a constant
In the book "Deep Learning" by Goodfellow et.al, the ADAM algorithm is described in sub-chapter 8.5 "Algorithm with Adaptive Learning Rate".
To my understanding an adaptive learning rate should ...
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0answers
511 views
Adam optimizer's escape from local minima [duplicate]
I noticed some interesting behaviour in my loss history while training my model.
Please note the sudden change in test loss at around epoch 106.
A similar drop will appear around epoch ~1000.
It ...
0
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1answer
744 views
How to select parameters for ADAM gradient descent
I am using ADAM for performing gradient descent. I am having difficulty in setting the learning rate, $\beta_1$ and $\beta_2$. Along with gradient descent, I am projecting the paramters on $L_1$ ball ...
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327 views
what is the mistake of convergence proof in Adam
Sashank J. Reddi et. al in their paper "On the convergence of Adam and beyond" say that, Adam's proof of convergence as stated in original paper is wrong. More than that, they point out that the value ...
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1answer
690 views
Understanding a derivation of bias correction for the Adam optimizer
I'm reading paper about the Adam optimizer and went up until the bias-correction section; in the paper they estimate the bias of the moving average of the squared gradient. These are the equations ...
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0answers
66 views
Adaptive Moment Estimate - What is meant by parameters of Adam optimizer are biased towards zero initially? [duplicate]
Let us consider the Adam optimizer with the equation given below:
$w_{t + 1} = w_{t} - \frac{\eta \times \bar{m_t}} {\sqrt{\bar{v_t} + \epsilon}}$
Here $w$ denotes weight (in time $t$ and $t + 1$) ...
5
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1answer
986 views
What does Diagonal Rescaling of the gradients mean in ADAM paper?
I was reading the original paper on ADAM (Adam: A Method for Stochastic Optimization), which mentions:
[...] invariant to diagonal rescaling of the gradients, [...]
What does it mean?
Also, ...
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1answer
455 views
Two large decreses in loss function with ADAM optimizer
I am training an RNN with tensorflow using the ADAM optimizer. The loss with respect to iteration has a sort of "two stage" decrease. As shown in the image below.
The task itself if just trying to ...
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0answers
43 views
Decreasing accuracy of neural network after long training
I trained a MLP network in Tensorflow which reached at some point 98.66 % accuracy. I used the MNIST data set and five hidden layers of the following size: 4096, 2048, 4096, 2048, 4096. In Tensorflow ...
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2answers
1k views
Etymology of “Adam” algorithm for gradient descent
What is the history behind the choice of the name "Adam" as used in Adam: A Method for Stochastic Optimization?
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0answers
219 views
Do we need to normalize inputs when using adaptive learning rate optimizers?
As stated in this thread Why do we need to normalize the images before we put them into CNN?, normalization of the inputs to a deep network to have zero mean and unit-deviation will make SGD more ...
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314 views
Difference between Stochastic Approximation (SA) and Stochastic Gradient Descent (SGD)
I understand the intended use cases for both stochastic approximation algorithms like SPSA or FDSA, and for SGD algorithms like Adam.
SPSA is intended for noisy objective functions, and Adam for ...
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1answer
854 views
What should we do when changing SGD optimizer to Adam optimizer?
Adam is one popular method of the optimization policies with adaptive learning rate. I'm focusing on a image segmentation project using fully convolutional networks. All weights were initialized by ...
5
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1answer
947 views
Does RMSProp/Adam solve vanishing gradient problem?
RMSProp and Adam both scale the effectively learning rate by dividing the moving average of past gradients (root mean squared). So if the first layer has gradient much smaller than the last layer, ...
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1answer
2k views
XGBoost Optimization Function
Which optimization technique does XGBoost use? I have trained my XGBoost classifier using the 'binary:logistic' objective. Does it use SGD / Adam?
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1answer
31k views
RMSProp and Adam vs SGD
I am performing experiments on the EMNIST validation set using networks with RMSProp, Adam and SGD. I am achieving 87% accuracy with SGD(learning rate of 0.1) and dropout (0.1 dropout prob) as well ...
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2answers
5k views
The reason of superiority of Limited-memory BFGS over ADAM solver
I am using Multilayer Perceptron MLPClassifier for training a classification model for my problem. I noticed that using the solver lbfgs (I guess it implies Limited-...
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1answer
5k views
No change in accuracy using Adam Optimizer when SGD works fine
I have been training a Spatial Transformer network with DNN on GTRSB dataset. I initially used SGF with momentum and was able to achieve good accuracy.
For further improvements and testing, I ...
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2answers
14k views
How does batch size affect Adam Optimizer?
What impact does mini-batch size have on Adam Optimizer? Is there a recommended mini-batch size when training a (covolutional) neural network with Adam Optimizer?
From what I understood (I might be ...
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564 views
Loss update with mini batches and after epoch
If I understand correctly, when using deep learning with mini batches, we have a forward and backward pass in every mini batch (with the corresponding optimizer). But does something different happen ...
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2answers
17k views
Explanation of Spikes in training loss vs. iterations with Adam Optimizer
I am training a neural network using i) SGD and ii) Adam Optimizer. When using normal SGD, I get a smooth training loss vs. iteration curve as seen below (the red one). However, when I used the Adam ...
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0answers
2k views
Spikes in loss function of deep neural network training [duplicate]
I'm training a simple feed-forward Deep Network with standard relu/sigmoid activation functions, using Tensorflow. I tried several optimizers and I noticed that those belonging to the Adam family show ...
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2answers
2k views
Why update all parameters at each step of the Adam optimiser even when we have sparse observations?
Adam is a method for stochastic optimisation. The algorithm is given below.
Consider our parameters that we wish to optimise
$$\boldsymbol{\theta} = [\theta_1, \theta_2]$$
observations
$$\...
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0answers
1k views
Better Performance With Gradient Descent than Adam on word2vec
I was implementing word2vec in TensorFlow and found that Gradient Descent worked much better and faster than the AdamOptimizer. I was under the impression that Adam was the "smarter" option that ...
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1answer
2k views
Is manually tuning learning rate during training redundant with optimization methods like Adam?
I have seen some high-profile deep learning papers where an optimization method like Adam was used, yet the learning rate was manually changed at specific iterations.
What is the relationship between ...
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1answer
835 views
Convergence proof of ADAM optimizer
I've read ADAM paper (https://arxiv.org/abs/1412.6980) and found out a suspicious part.
In Lemma 10.3 of Appendix,
\begin{align*}
\sqrt{\|g_{1:T,i}\|_2^2 - g_{T,i}^2} & \le \|g_{1:T,i}\|_2 - \...
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1answer
10k views
How to choose between SGD with Nesterov momentum and Adam?
I'm currently implementing a neural network architecture on Keras. I would like to optimize the training time, and I'm considering using alternative optimizers such as SGD with Nesterov Momentum and ...
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1answer
3k views
Training accuracy increase abruptly at first epoch to 99%. is it normal?
I expected my training process is like this:
But my training accuracy increased to 99% at only 1 epoch, not steadily increase.
I was suspicious about high learning rate, so i used various learning ...
4
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1answer
477 views
Should you use optimization algorithms like Adagrad and ADAM for neural network online training?
Optimization algorithms like Adagrad and ADAM decay your learning rate over time. To me this sounds like a bad idea for online training since you're always getting new data as opposed to retraining on ...
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1answer
11k views
Why is it important to include a bias correction term for the Adam optimizer for Deep Learning?
I was reading about the Adam optimizer for Deep Learning and came across the following sentence in the new book Deep Learning by Begnio, Goodfellow and Courtville:
Adam includes bias corrections to ...
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3answers
14k views
What is the reason that the Adam Optimizer is considered robust to the value of its hyper parameters?
I was reading about the Adam optimizer for Deep Learning and came across the following sentence in the new book Deep Learning by Bengio, Goodfellow and Courville:
Adam is generally regarded as ...
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1answer
31k views
How does the Adam method of stochastic gradient descent work?
I'm familiar with basic gradient descent algorithms for training neural networks. I've read the paper proposing Adam: ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION.
While I've definitely got some ...
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6answers
77k views
Adam optimizer with exponential decay
In most Tensorflow code I have seen Adam Optimizer is used with a constant Learning Rate of 1e-4 (i.e. 0.0001). The code usually looks the following:
...
