How do I write code for asking for the data set from the user in python? (I'm trying to code a generalised neural network from scratch. So number of layers and activations are to be input from the user. And it needs to be an object oriented approach. I'm having trouble connecting things
I think you should be more specific about what kind of data you want to feed. What this network is going to do? For example in case of a binary image classification input would be consisted of images splitted into train/test set with proper label data. This can be provided via various data structures, you can ask for a dataframe filled with loaded images and labels. However more efficient would be loading data into numpy arrays.
Related
I have a dataset which consists of power signals and the targets are multiple house appliances that might be on or off. I only need to do feature extraction on the signal using cnn and then save the dataset as a csv file to use it with another Machine learning method.
I used CNN before for classification on signals consisting of 6 classes. However, i am a bit confused and i need your help. I have two questions (might be stupid and am sorry)
Do i need the target variable in order to do feature extraction?
The shape of my dataset is for example 40000x100. I need my extracted dataset (the features learned using CNN) to have the same amount of rows (i.e. 40000). How can i do that?
I know that the answer might be simpler than i think but at the moment i feel quite lost.
I would appreciate any help.
Thanks
I was wondering if I could build a classification model that just works on simple data I for example stock market classification 1 if it goes up 0 of down. I ask because I never see models like this and tutorials only ever with image or text classification and I typically see regression models working with this form of data
Yes, definitely!
After all image is just a 2D array of numbers.
When you build your classification network, or modify an existing one, you just want to design it so the input layer of the neural network will be a simple array of numbers.
If I have to estimate its performance, I would bet on higher performance relative to a conversational image classifier that does the same task, because every bit of input data is relevant and the data will most likely be smaller without losing any details.
I'm new to machine learning, and I've been given a task where I'm asked to extract features from a data set with continuous data using representation learning (for example a stacked autoencoder).
Then I'm to combine these extracted features with the original features of the dataset and then use a feature selection technique to determine my final set of features that goes into my prediction model.
Could anyone point me to some resources or demos or sample code of how I could get started on this? I'm very confused on where to begin on this and would love some advice!
Okay, say you have an input of (1000 instances and 30 features). What I would do based on what you told us is:
Train an autoencoder, a neural network that compresses the input and then decompresses it, which has as a target your original input. The compressed representation lies in the latent space and encapsulates information about the input which is not directly accessible by humans. Now you may find such networks in tensorflow or pytorch. Tensorflow is easier and more straightforward so it could be better for you. I will provide this link (https://keras.io/examples/generative/vae/) for a variational autoencoder that may do the job for you. This has Conv2D layers so it performs really well for image data, but you can play around with the architecture. I cannot tell u more because you did not provide more info for your dataset. However, the important thing is the following:
After your autoencoder is trained properly and you need to make sure of it, (it adequately reconstructs the input) then you need to extract the aforementioned latent inputs (you will find more in the link). Now, that will be let's say 16 numbers but you can play with it. These 16 numbers were built to preserve info regarding your input. You said you wanted to combine these numbers with your input so might as well do that and end up with 46 input features. Now the feature selection part has to do with selecting the input features that are more useful for your model. That is not very interesting, you may find more information (https://towardsdatascience.com/feature-selection-techniques-in-machine-learning-with-python-f24e7da3f36e) and one way to select features is by training many models with different feature subsets. Remember, techniques such as PCA are for feature extraction not selection. I cannot provide any demo that does the whole thing but there are sources that can help. Remember, your autoencoder is supposed to return 16 numbers for each training example. Your autoencoder is trained only on your train data, with your train data as targets.
I am trying to set up a Multiple Artificial Neural Network as you can see here on image (a):
(source)
I want that each of the networks work independently on its own domain. The single networks must be built and trained for their specific task. The final decision will be make on the results of the individual networks, often called expert networks or agents.
Because of privacy, I could not share my data.
I try to set up this with Tensorflow in Python. Do you have an idea of how I would do it if that is achievable? At the moment I have not found any examples of this.
The way to go about this is to just take the outputs of the two networks and concatenate the resulting output tensors (and reshape them if needed) and then pass them into the final network. Take a look at here for the concatenation documentation and here for an example of taking the output from one network and feeding it into another. This should give you a place to start from.
As for (a), it is simple, just train the networks before hand and load them when you are training the final network. Then do the concatenation on the outputs.
Hope this helps
I have a audio data set and each of them has different length. There are some events in these audios, that I want to train and test but these events are placed randomly, plus the lengths are different, it is really hard to build a machine learning system with using that dataset. I thought fixing a default size of length and build a multilayer NN however, the length's of events are also different. Then I thought about using CNN, like it is used to recognise patterns or multiple humans on an image. The problem for that one is I am really struggling when I try to understand the audio file.
So, my questions, Is there anyone who can give me some tips about building a machine learning system that classifies different types of defined events with training itself on a dataset that has these events randomly(1 data contains more than 1 events and they are different from each other.) and each of them has different lenghts?
I will be so appreciated if anyone helps.
First, you need to annotate your events in the sound streams, i.e. specify bounds and labels for them.
Then, convert your sounds into sequences of feature vectors using signal framing. Typical choices are MFCCs or log-mel filtebank features (the latter corresponds to a spectrogram of a sound). Having done this, you will convert your sounds into sequences of fixed-size feature vectors that can be fed into a classifier. See this. for better explanation.
Since typical sounds have a longer duration than an analysis frame, you probably need to stack several contiguous feature vectors using sliding window and use these stacked frames as input to your NN.
Now you have a) input data and b) annotations for each window of analysis. So, you can try to train a DNN or a CNN or a RNN to predict a sound class for each window. This task is known as spotting. I suggest you to read Sainath, T. N., & Parada, C. (2015). Convolutional Neural Networks for Small-footprint Keyword Spotting. In Proceedings INTERSPEECH (pp. 1478–1482) and to follow its references for more details.
You can use a recurrent neural network (RNN).
https://www.tensorflow.org/versions/r0.12/tutorials/recurrent/index.html
The input data is a sequence and you can put a label in every sample of the time series.
For example a LSTM (a kind of RNN) is available in libraries like tensorflow.