I use machine learning algorithm in Malware analysis. When I input some features, I get strange training time. For example:
4 feature(A,B,C,D), model training time is 3 seconds.
3 Features(A,B,C), training time is 5 seconds.
2 features(A, B), training time is 8 seconds.
1 feature(A), training time is 4 seconds.
This kind of result happens on both MLP and Random Forest. In my opinion, the training time should be faster if I use less features, but the result is complete different.
In KNN, the result will be like these:
If I using 6,5,4,3 features(A,B,C,D,E,F), model testing time is about 1.1 seconds, almost the same.
2 features(A,B), model testing time is 3 seconds.
1 feature (A), model testing time is 5 seconds.
My dataset has 17K records and using 10-Fold cross-validation. The feature is sort by their entropy, feature A have highest entropy and feature F is lowest. Using Google Colab with sklearn for the testing. I tried several times in different date, and the trend is the same.
The feature of my dataset has total 79 features, the appearance only happens with few features.
Thanks for anyone who reply me, I have no idea about it.
It does seem at first glance that having fewer features will result in lower training times. However, depending on which algorithm is being used, this may not be the case. In training, an objective function (loss function) is being minimized by the algorithm. Taking the case of the MLP neural network, if you change the features (especially depending on whether they're informative or not), you're changing the feature space (or "error surface") over which the optimization occurs and possibly the minima of the function will be harder to find, resulting in more steps and longer training in order to satisfy the convergence criteria.
I am working on a time-series prediction problem using GradientBoostingRegressor, and I think I'm seeing significant overfitting, as evidenced by a significantly better RMSE for training than for prediction. In order to examine this, I'm trying to use sklearn.model_selection.cross_validate, but I'm having problems understanding the result.
First: I was calculating RMSE by fitting to all my training data, then "predicting" the training data outputs using the fitted model and comparing those with the training outputs (the same ones I used for fitting). The RMSE that I observe is the same order of magnitude the predicted values and, more important, it's in the same ballpark as the RMSE I get when I submit my predicted results to Kaggle (although the latter is lower, reflecting overfitting).
Second, I use the same training data, but apply sklearn.model_selection.cross_validate as follows:
cross_validate( predictor, features, targets, cv = 5, scoring = "neg_mean_squared_error" )
I figure the neg_mean_squared_error should be the square of my RMSE. Accounting for that, I still find that the error reported by cross_validate is one or two orders of magnitude smaller than the RMSE I was calculating as described above.
In addition, when I modify my GradientBoostingRegressor max_depth from 3 to 2, which I would expect reduces overfitting and thus should improve the CV error, I find that the opposite is the case.
I'm keenly interested to use Cross Validation so I don't have to validate my hyperparameter choices by using up Kaggle submissions, but given what I've observed, I'm not clear that the results will be understandable or useful.
Can someone explain how I should be using Cross Validation to get meaningful results?
I think there is a conceptual problem here.
If you want to compute the error of a prediction you should not use the training data. As the name says theese type of data are used only in training, for evaluating accuracy scores you ahve to use data that the model has never seen.
About cross-validation I can tell that it's an approach to find the best training/testing set. The process is as follows: you divide your data into n groups and you do various iterating changing the testing group you pick. If you have n groups you will do n iteration and each time the training and testing set will be different. It's more understamdable in the image below.
Basically what you should do it's kile this:
Train the model using months from 0 to 30 (for example)
See the predictions made with months from 31 to 35 as input.
If the input has to be the same lenght divide feature in half (should be 17 months).
I hope I understood correctly, othewise comment.
I am trying to use the TensorFlow object detection API to recognize a specific object (guitars) in pictures and videos.
As for the data, I downloaded the images from the OpenImage dataset, and derived the .tfrecord files. I am testing with different numbers, but for now let's say I have 200 images in the training set and 100 in the evaluation one.
I'm traininig the model using the "ssd_mobilenet_v1_coco" as a starting point, and the "model_main.py" script, so that I can have training and validation results.
When I visualize the training progress in TensorBoard, I get the following results for train:
and validation loss:
respectively.
I am generally new to computer vision and trying to learn, so I was trying to figure out the meaning of these plots.
The training loss goes as expected, decreasing over time.
In my (probably simplistic) view, I was expecting the validation loss to start at high values, decrease as training goes on, and then start increasing again if the training goes on for too long and the model starts overfitting.
But in my case, I don't see this behavior for the validation curve, which seems to be trending upwards basically all the time (excluding fluctuations).
Have I been training the model for too little time to see the behavior I'm expecting? Are my expectations wrong in the first place? Am I misinterpreting the curves?
Ok, I fixed it by decreasing the initial_learning_rate from 0.004 to 0.0001.
It was the obvious solution, considering the wild oscillations of the validation loss, but at first I thought it wouldn't work since there seems to be already a learning rate scheduler in the config file.
However, immediately below (in the config file) there's a num_steps option, and it's stated that
# Note: The below line limits the training process to 200K steps, which we
# empirically found to be sufficient enough to train the pets dataset. This
# effectively bypasses the learning rate schedule (the learning rate will
# never decay). Remove the below line to train indefinitely.
Honestly, I don't remember if I commented out the num_steps option...if I didn't, it seems my learning rate was kept to the initial value of 0.004, which turned out to be too high.
If I did comment it out (so that the learning scheduler was active), I guess that, instead of the decrease, it still started from too high of a value.
Anyway, it's working much better now, I hope this can be useful if anyone is experiencing the same problem.
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I have a problem where I am trying to create a neural network for Tic-Tac-Toe. However, for some reason, training the neural network causes it to produce nearly the same output for any given input.
I did take a look at Artificial neural networks benchmark, but my network implementation is built for neurons with the same activation function for each neuron, i.e. no constant neurons.
To make sure the problem wasn't just due to my choice of training set (1218 board states and moves generated by a genetic algorithm), I tried to train the network to reproduce XOR. The logistic activation function was used. Instead of using the derivative, I multiplied the error by output*(1-output) as some sources suggested that this was equivalent to using the derivative. I can put the Haskell source on HPaste, but it's a little embarrassing to look at. The network has 3 layers: the first layer has 2 inputs and 4 outputs, the second has 4 inputs and 1 output, and the third has 1 output. Increasing to 4 neurons in the second layer didn't help, and neither did increasing to 8 outputs in the first layer.
I then calculated errors, network output, bias updates, and the weight updates by hand based on http://hebb.mit.edu/courses/9.641/2002/lectures/lecture04.pdf to make sure there wasn't an error in those parts of the code (there wasn't, but I will probably do it again just to make sure). Because I am using batch training, I did not multiply by x in equation (4) there. I am adding the weight change, though http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-2.html suggests to subtract it instead.
The problem persisted, even in this simplified network. For example, these are the results after 500 epochs of batch training and of incremental training.
Input |Target|Output (Batch) |Output(Incremental)
[1.0,1.0]|[0.0] |[0.5003781562785173]|[0.5009731800870864]
[1.0,0.0]|[1.0] |[0.5003740346965251]|[0.5006347214672715]
[0.0,1.0]|[1.0] |[0.5003734471544522]|[0.500589332376345]
[0.0,0.0]|[0.0] |[0.5003674110937019]|[0.500095157458231]
Subtracting instead of adding produces the same problem, except everything is 0.99 something instead of 0.50 something. 5000 epochs produces the same result, except the batch-trained network returns exactly 0.5 for each case. (Heck, even 10,000 epochs didn't work for batch training.)
Is there anything in general that could produce this behavior?
Also, I looked at the intermediate errors for incremental training, and the although the inputs of the hidden/input layers varied, the error for the output neuron was always +/-0.12. For batch training, the errors were increasing, but extremely slowly and the errors were all extremely small (x10^-7). Different initial random weights and biases made no difference, either.
Note that this is a school project, so hints/guides would be more helpful. Although reinventing the wheel and making my own network (in a language I don't know well!) was a horrible idea, I felt it would be more appropriate for a school project (so I know what's going on...in theory, at least. There doesn't seem to be a computer science teacher at my school).
EDIT: Two layers, an input layer of 2 inputs to 8 outputs, and an output layer of 8 inputs to 1 output, produces much the same results: 0.5+/-0.2 (or so) for each training case. I'm also playing around with pyBrain, seeing if any network structure there will work.
Edit 2: I am using a learning rate of 0.1. Sorry for forgetting about that.
Edit 3: Pybrain's "trainUntilConvergence" doesn't get me a fully trained network, either, but 20000 epochs does, with 16 neurons in the hidden layer. 10000 epochs and 4 neurons, not so much, but close. So, in Haskell, with the input layer having 2 inputs & 2 outputs, hidden layer with 2 inputs and 8 outputs, and output layer with 8 inputs and 1 output...I get the same problem with 10000 epochs. And with 20000 epochs.
Edit 4: I ran the network by hand again based on the MIT PDF above, and the values match, so the code should be correct unless I am misunderstanding those equations.
Some of my source code is at http://hpaste.org/42453/neural_network__not_working; I'm working on cleaning my code somewhat and putting it in a Github (rather than a private Bitbucket) repository.
All of the relevant source code is now at https://github.com/l33tnerd/hsann.
I've had similar problems, but was able to solve by changing these:
Scale down the problem to manageable size. I first tried too many inputs, with too many hidden layer units. Once I scaled down the problem, I could see if the solution to the smaller problem was working. This also works because when it's scaled down, the times to compute the weights drop down significantly, so I can try many different things without waiting.
Make sure you have enough hidden units. This was a major problem for me. I had about 900 inputs connecting to ~10 units in the hidden layer. This was way too small to quickly converge. But also became very slow if I added additional units. Scaling down the number of inputs helped a lot.
Change the activation function and its parameters. I was using tanh at first. I tried other functions: sigmoid, normalized sigmoid, Gaussian, etc.. I also found that changing the function parameters to make the functions steeper or shallower affected how quickly the network converged.
Change learning algorithm parameters. Try different learning rates (0.01 to 0.9). Also try different momentum parameters, if your algo supports it (0.1 to 0.9).
Hope this helps those who find this thread on Google!
So I realise this is extremely late for the original post, but I came across this because I was having a similar problem and none of the reasons posted here cover what was wrong in my case.
I was working on a simple regression problem, but every time I trained the network it would converge to a point where it was giving me the same output (or sometimes a few different outputs) for each input. I played with the learning rate, the number of hidden layers/nodes, the optimization algorithm etc but it made no difference. Even when I looked at a ridiculously simple example, trying to predict the output (1d) of two different inputs (1d):
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class net(nn.Module):
def __init__(self, obs_size, hidden_size):
super(net, self).__init__()
self.fc = nn.Linear(obs_size, hidden_size)
self.out = nn.Linear(hidden_size, 1)
def forward(self, obs):
h = F.relu(self.fc(obs))
return self.out(h)
inputs = np.array([[0.5],[0.9]])
targets = torch.tensor([3.0, 2.0], dtype=torch.float32)
network = net(1,5)
optimizer = torch.optim.Adam(network.parameters(), lr=0.001)
for i in range(10000):
out = network(torch.tensor(inputs, dtype=torch.float32))
loss = F.mse_loss(out, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print("Loss: %f outputs: %f, %f"%(loss.data.numpy(), out.data.numpy()[0], out.data.numpy()[1]))
but STILL it was always outputting the average value of the outputs for both inputs. It turns out the reason is that the dimensions of my outputs and targets were not the same: the targets were Size[2], and the outputs were Size[2,1], and for some reason PyTorch was broadcasting the outputs to be Size[2,2] in the MSE loss, which completely messes everything up. Once I changed:
targets = torch.tensor([3.0, 2.0], dtype=torch.float32)
to
targets = torch.tensor([[3.0], [2.0]], dtype=torch.float32)
It worked as it should. This was obviously done with PyTorch, but I suspect maybe other libraries broadcast variables in the same way.
For me it was happening exactly like in your case, the output of neural network was always the same no matter the training & number of layers etc.
Turns out my back-propagation algorithm had a problem. At one place I was multiplying by -1 where it wasn't required.
There could be another problem like this. The question is how to debug it?
Steps to debug:
Step1 : Write the algorithm such that it can take variable number of input layers and variable number of input & output nodes.
Step2 : Reduce the hidden layers to 0. Reduce input to 2 nodes, output to 1 node.
Step3 : Now train for binary-OR-Operation.
Step4 : If it converges correctly, go to Step 8.
Step5 : If it doesn't converge, train it only for 1 training sample
Step6 : Print all the forward and prognostication variables (weights, node-outputs, deltas etc)
Step7 : Take pen&paper and calculate all the variables manually.
Step8 : Cross verify the values with algorithm.
Step9 : If you don't find any problem with 0 hidden layers. Increase hidden layer size to 1. Repeat step 5,6,7,8
It sounds like a lot of work, but it works very well IMHO.
I know, that for the original post this is far, too late but maybe I can help someone with this, as I faced the same problem.
For me the problem was, that my input data had missing values in important columns, where the training/test data were not missing. I replaced these values with zero values and voilà, suddenly the results were plausible. So maybe check your data, maybe it si misrepresented
It's hard to tell without seeing a code sample but it is possible occure for a net because its number of hidden neron.with incresing in number of neron and number of hiden layer it is not possible to train a net with small set of training data.until it is possible to make a net with smaller layer and nerons it is amiss to use a larger net.therefore perhaps your problem solved with attention to this matters.
I haven't tested it with the XOR problem in the question, but for my original dataset based on Tic-Tac-Toe, I believe that I have gotten the network to train somewhat (I only ran 1000 epochs, which wasn't enough): the quickpropagation network can win/tie over half of its games; backpropagation can get about 41%. The problems came down to implementation errors (small ones) and not understanding the difference between the error derivative (which is per-weight) and the error for each neuron, which I did not pick up on in my research. #darkcanuck's answer about training the bias similarly to a weight would probably have helped, though I didn't implement it. I also rewrote my code in Python so that I could more easily hack with it. Therefore, although I haven't gotten the network to match the minimax algorithm's efficiency, I believe that I have managed to solve the problem.
I faced a similar issue earlier when my data was not properly normalized. Once I normalized the data everything ran correctly.
Recently, I faced this issue again and after debugging, I found that there can be another reason for neural networks giving the same output. If you have a neural network that has a weight decay term such as that in the RSNNS package, make sure that your decay term is not so large that all weights go to essentially 0.
I was using the caret package for in R. Initially, I was using a decay hyperparameter = 0.01. When I looked at the diagnostics, I saw that the RMSE was being calculated for each fold (of cross validation), but the Rsquared was always NA. In this case all predictions were coming out to the same value.
Once I reduced the decay to a much lower value (1E-5 and lower), I got the expected results.
I hope this helps.
I was running into the same problem with my model when number of layers is large. I was using a learning rate of 0.0001. When I lower the learning rate to 0.0000001 the problem seems solved. I think algorithms stuck on local minumums when learning rate is too low
It's hard to tell without seeing a code sample, but a bias bug can have that effect (e.g. forgetting to add the bias to the input), so I would take a closer look at that part of the code.
Based on your comments, I'd agree with #finnw that you have a bias problem. You should treat the bias as a constant "1" (or -1 if you prefer) input to each neuron. Each neuron will also have its own weight for the bias, so a neuron's output should be the sum of the weighted inputs, plus the bias times its weight, passed through the activation function. Bias weights are updated during training just like the other weights.
Fausett's "Fundamentals of Neural Networks" (p.300) has an XOR example using binary inputs and a network with 2 inputs, 1 hidden layer of 4 neurons and one output neuron. Weights are randomly initialized between +0.5 and -0.5. With a learning rate of 0.02 the example network converges after about 3000 epochs. You should be able to get a result in the same ballpark if you get the bias problems (and any other bugs) ironed out.
Also note that you cannot solve the XOR problem without a hidden layer in your network.
I encountered a similar issue, I found out that it was a problem with how my weights were being generated.
I was using:
w = numpy.random.rand(layers[i], layers[i+1])
This generated a random weight between 0 and 1.
The problem was solved when I used randn() instead:
w = numpy.random.randn(layers[i], layers[i+1])
This generates negative weights, which helped my outputs become more varied.
I ran into this exact issue. I was predicting 6 rows of data with 1200+ columns using nnet.
Each column would return a different prediction but all of the rows in that column would be the same value.
I got around this by increasing the size parameter significantly. I increased it from 1-5 to 11+.
I have also heard that decreasing your decay rate can help.
I've had similar problems with machine learning algorithms and when I looked at the code I found random generators that were not really random. If you do not use a new random seed (such Unix time for example, see http://en.wikipedia.org/wiki/Unix_time) then it is possible to get the exact same results over and over again.