Can a LSTM problem be expressed as FFNN one?
LSTM neural networks simply look in the past. But I can also take some (or many) past values and use them as input features of a FFNN.
In this way, could FFNN replace LSTM Networks? Why should I prefer LSTM over FFNN if I can take past values and use them as input features?
LSTM is also a feed forward neural network with Memory Cell and recurrent connection. LSTM is an optimized NN algorithm since it can handle the problem of vanishing and exploring gradients and it can handle the long term dependencies. Obviously, you can use a FFNN by customizing the input layer information with a valid Neural Network architecture, this is not a replacement of LSTM.
When you have an input that you want to make a prediction on, does the input have to be run through the entire neural net?
Yes, that is how current neural networks work. The only exception are some state of the art networks such as CNNs with Adaptive Inference Graphs.
Yes. In most Neural Nets, each layer except the final one will generally produce outputs that do not correspond to output categories. Instead, these intermediate layers extract features and find patterns in the output from the previous layer. Only the final layer looks at all these high-level features (produced by the combination of all previous layers) to decide how the input should be categorised (example).
I learned that neural networks are only good at answering Yes or No questions, such as "is this a person, is this a car, is this an apple" etc.
But I see examples of ANNs finding matches to faces of people in a crowded place and being used for traditional machine vision applications, such as sub-pixel template matching.
Is this just a product of a combination of ANN and traditional matching techniques. Such as recognizing which features are matching to a known template using an ANN, and then figuring out where those keypoints are in the image using good old image processing? Or is it possible to get something other than a Yes or a No response from a network?
Yes it is possible to get a range of answers from an Artificial Neural Network. It depends on how you set up your Neurons.
Artificial Neural Networks make decisions by being trained using examples with known solutions, usually thousands of cases where the inputs and expected outputs are known.
They get "trained" by recursively adjusting each Neurons weight by comparing its output to the expected output.
Your first layer of Neurons is your inputs. Your last layer is your outputs. If your last layer has 2 Neurons, then you will get one of two outputs.
There is no limit to how many inputs and outputs an Artificial Neural Network can have. Check out these diagrams:
Here is a repository for an Artificial Neural Network I created that predicts the output of an XOR gate. Hope this helps!
Here is a truth table for an XOR gate for clarity.
[UPDATE]
To explicitly answer your question about Image Classification, I believe Artificial Neural Networks are a good approach.
Here is an article I found helpful in understanding the implementation of an Image Classifier. You can also experiment with Tenserflow with is a GUI Neural Network application which is an intuitive approach to understating how Neural Networks work.
I understand all the computational steps of training a neural network with gradient descent using forwardprop and backprop, but I'm trying to wrap my head around why they work so much better than logistic regression.
For now all I can think of is:
A) the neural network can learn it's own parameters
B) there are many more weights than simple logistic regression thus allowing for more complex hypotheses
Can someone explain why a neural network works so well in general? I am a relative beginner.
Neural Networks can have a large number of free parameters (the weights and biases between interconnected units) and this gives them the flexibility to fit highly complex data (when trained correctly) that other models are too simple to fit. This model complexity brings with it the problems of training such a complex network and ensuring the resultant model generalises to the examples it’s trained on (typically neural networks require large volumes of training data, that other models don't).
Classically logistic regression has been limited to binary classification using a linear classifier (although multi-class classification can easily be achieved with one-vs-all, one-vs-one approaches etc. and there are kernalised variants of logistic regression that allow for non-linear classification tasks). In general therefore, logistic regression is typically applied to more simple, linearly-separable classification tasks, where small amounts of training data are available.
Models such as logistic regression and linear regression can be thought of as simple multi-layer perceptrons (check out this site for one explanation of how).
To conclude, it’s the model complexity that allows neural nets to solve more complex classification tasks, and to have a broader application (particularly when applied to raw data such as image pixel intensities etc.), but their complexity means that large volumes of training data are required and training them can be a difficult task.
Recently Dr. Naftali Tishby's idea of Information Bottleneck to explain the effectiveness of deep neural networks is making the rounds in the academic circles.
His video explaining the idea (link below) can be rather dense so I'll try to give the distilled/general form of the core idea to help build intuition
https://www.youtube.com/watch?v=XL07WEc2TRI
To ground your thinking, vizualize the MNIST task of classifying the digit in the image. For this, I am only talking about simple fully-connected neural networks (not Convolutional NN as is typically used for MNIST)
The input to a NN contains information about the output hidden inside of it. Some function is needed to transform the input to the output form. Pretty obvious.
The key difference in thinking needed to build better intuition is to think of the input as a signal with "information" in it (I won't go into information theory here). Some of this information is relevant for the task at hand (predicting the output). Think of the output as also a signal with a certain amount of "information". The neural network tries to "successively refine" and compress the input signal's information to match the desired output signal. Think of each layer as cutting away at the unneccessary parts of the input information, and
keeping and/or transforming the output information along the way through the network.
The fully-connected neural network will transform the input information into a form in the final hidden layer, such that it is linearly separable by the output layer.
This is a very high-level and fundamental interpretation of the NN, and I hope it will help you see it clearer. If there are parts you'd like me to clarify, let me know.
There are other essential pieces in Dr.Tishby's work, such as how minibatch noise helps training, and how the weights of a neural network layer can be seen as doing a random walk within the constraints of the problem.
These parts are a little more detailed, and I'd recommend first toying with neural networks and taking a course on Information Theory to help build your understanding.
Consider you have a large dataset and you want to build a binary classification model for that, Now you have two options that you have pointed out
Logistic Regression
Neural Networks ( Consider FFN for now )
Each node in a neural network will be associated with an activation function for example let's choose Sigmoid since Logistic regression also uses sigmoid internally to make decision.
Let's see how the decision of logistic regression looks when applied on the data
See some of the green spots present in the red boundary?
Now let's see the decision boundary of neural network (Forgive me for using a different color)
Why this happens? Why does the decision boundary of neural network is so flexible which gives more accurate results than Logistic regression?
or the question you asked is "Why neural networks works so well ?" is because of it's hidden units or hidden layers and their representation power.
Let me put it this way.
You have a logistic regression model and a Neural network which has say 100 neurons each of Sigmoid activation. Now each neuron will be equivalent to one logistic regression.
Now assume a hundred logistic units trained together to solve one problem versus one logistic regression model. Because of these hidden layers the decision boundary expands and yields better results.
While you are experimenting you can add more number of neurons and see how the decision boundary is changing. A logistic regression is same as a neural network with single neuron.
The above given is just an example. Neural networks can be trained to get very complex decision boundaries
Neural networks allow the person training them to algorithmically discover features, as you pointed out. However, they also allow for very general nonlinearity. If you wish, you can use polynomial terms in logistic regression to achieve some degree of nonlinearity, however, you must decide which terms you will use. That is you must decide a priori which model will work. Neural networks can discover the nonlinear model that is needed.
'Work so well' depends on the concrete scenario. Both of them do essentially the same thing: predicting.
The main difference here is neural network can have hidden nodes for concepts, if it's propperly set up (not easy), using these inputs to make the final decission.
Whereas linear regression is based on more obvious facts, and not side effects. A neural network should de able to make more accurate predictions than linear regression.
Neural networks excel at a variety of tasks, but to get an understanding of exactly why, it may be easier to take a particular task like classification and dive deeper.
In simple terms, machine learning techniques learn a function to predict which class a particular input belongs to, depending on past examples. What sets neural nets apart is their ability to construct these functions that can explain even complex patterns in the data. The heart of a neural network is an activation function like Relu, which allows it to draw some basic classification boundaries like:
Example classification boundaries of Relus
By composing hundreds of such Relus together, neural networks can create arbitrarily complex classification boundaries, for example:
Composing classification boundaries
The following article tries to explain the intuition behind how neural networks work: https://medium.com/machine-intelligence-report/how-do-neural-networks-work-57d1ab5337ce
Before you step into neural network see if you have assessed all aspects of normal regression.
Use this as a guide
and even before you discard normal regression - for curved type of dependencies - you should strongly consider kernels with SVM
Neural networks are defined with an objective and loss function. The only process that happens within a neural net is to optimize for the objective function by reducing the loss function or error. The back propagation helps in finding the optimized objective function and reach our output with an output condition.
In scenario 1, I had a multi-layer sparse autoencoder that tries to reproduce my input, so all my layers are trained together with random-initiated weights. Without a supervised layer, on my data this didn't learn any relevant information (the code works fine, verified as I've already used it in many other deep neural network problems)
In scenario 2, I simply train multiple auto-encoders in a greedy layer-wise training similar to that of deep learning (but without a supervised step in the end), each layer on the output of the hidden layer of the previous autoencoder. They'll now learn some patterns (as I see from the visualized weights) separately, but not awesome, as I'd expect it from single layer AEs.
So I've decided to try if now the pretrained layers connected into 1 multi-layer AE could perform better than the random-initialized version. As you see this is same as the idea of the fine-tuning step in deep neural networks.
But during my fine-tuning, instead of improvement, the neurons of all the layers seem to quickly converge towards an all-the-same pattern and end up learning nothing.
Question: What's the best configuration to train a fully unsupervised multi-layer reconstructive neural network? Layer-wise first and then some sort of fine tuning? Why is my configuration not working?
After some tests I've came up with a method that seems to give very good results, and as you'd expect from a 'fine-tuning' it improves the performance of all the layers:
Just like normally, during the greedy layer-wise learning phase, each new autoencoder tries to reconstruct the activations of the previous autoencoder's hidden layer. However, the last autoencoder (that will be the last layer of our multi-layer autoencoder during fine-tuning) is different, this one will use the activations of the previous layer and tries to reconstruct the 'global' input (ie the original input that was fed to the first layer).
This way when I connect all the layers and train them together, the multi-layer autoencoder will really reconstruct the original image in the final output. I found a huge improvement in the features learned, even without a supervised step.
I don't know if this is supposed to somehow correspond with standard implementations but I haven't found this trick anywhere before.