feature-extraction | Sample techniques for a variety of feature | Machine Learning library

Unfortunately, as you have rightly stated, the pipelines documentation is rather sparse. However, the source code specifies which models are used by default, see here. Specifically, the model is distilbert-base-cased.

For a way to use models, see a related answer by me here. You can simply specifiy the model and tokenizer parameters like this:

We have ended up training a model for POS Tagging (part of speech tagging) with the Hugging Face Transformers library. The resulting model is available here:

https://huggingface.co/gilf/french-postag-model?text=En+Turquie%2C+Recep+Tayyip+Erdogan+ordonne+la+reconversion+de+Sainte-Sophie+en+mosqu%C3%A9e

You can basically see how it assigns POS tags on the webpage mentioned above. If you have the Hugging Face Transformers library installed you can try it out in a Jupyter notebook with this code:

I had the same error, but according to https://github.com/tyiannak/pyAudioAnalysis/issues/72, I converted my stereo music to mono and it solved the problem for me.

You should union vectorizerN and vectorizerMX, not MX and XN. Change the line to

Ok, Now lets go through the documentation I gave in comments step by step:

Documents:

You may want to have a balanced dataset to prevent your model from learning a wrong probability distribution. If a category represents 95% of your dataset, a model that classifies everything as part of that category, will have an accuracy of 95%.

Let's first look at how the number of learnable parameters is calculated for each individual type of layer you have, and then calculate the number of parameters in your example.

• Input layer: All the input layer does is read the input image, so there are no parameters you could learn here.

• Convolutional layers: Consider a convolutional layer which takes l feature maps at the input, and has k feature maps as output. The filter size is n x m. For example, this will look like this:

  Here, the input has l=32 feature maps as input, k=64 feature maps as output, and the filter size is n=3 x m=3. It is important to understand, that we don't simply have a 3x3 filter, but actually a 3x3x32 filter, as our input has 32 dimensions. And we learn 64 different 3x3x32 filters. Thus, the total number of weights is n*m*k*l. Then, there is also a bias term for each feature map, so we have a total number of parameters of (n*m*l+1)*k.

• Pooling layers: The pooling layers e.g. do the following: "replace a 2x2 neighborhood by its maximum value". So there is no parameter you could learn in a pooling layer.
• Fully-connected layers: In a fully-connected layer, all input units have a separate weight to each output unit. For n inputs and m outputs, the number of weights is n*m. Additionally, you have a bias for each output node, so you are at (n+1)*m parameters.
• Output layer: The output layer is a normal fully-connected layer, so (n+1)*m parameters, where n is the number of inputs and m is the number of outputs.

The final difficulty is the first fully-connected layer: we do not know the dimensionality of the input to that layer, as it is a convolutional layer. To calculate it, we have to start with the size of the input image, and calculate the size of each convolutional layer. In your case, Lasagne already calculates this for you and reports the sizes - which makes it easy for us. If you have to calculate the size of each layer yourself, it's a bit more complicated:

• In the simplest case (like your example), the size of the output of a convolutional layer is input_size - (filter_size - 1), in your case: 28 - 4 = 24. This is due to the nature of the convolution: we use e.g. a 5x5 neighborhood to calculate a point - but the two outermost rows and columns don't have a 5x5 neighborhood, so we can't calculate any output for those points. This is why our output is 2*2=4 rows/columns smaller than the input.
• If one doesn't want the output to be smaller than the input, one can zero-pad the image (with the pad parameter of the convolutional layer in Lasagne). E.g. if you add 2 rows/cols of zeros around the image, the output size will be (28+4)-4=28. So in case of padding, the output size is input_size + 2*padding - (filter_size -1).
• If you explicitly want to downsample your image during the convolution, you can define a stride, e.g. stride=2, which means that you move the filter in steps of 2 pixels. Then, the expression becomes ((input_size + 2*padding - filter_size)/stride) +1.

In your case, the full calculations are:

All you’re looking for is a hash procedure that includes all the salient details of the class’s definition. (Base classes can be included by including their definitions recursively.) To minimize false matches, the basic idea is to apply a wide (cryptographic) hash to a serialization of your class. So start with pickle: it supports more types than hash and, when it uses identity, it uses a reproducible identity based on name. This makes it a good candidate for the base case of a recursive strategy: deal with the functions and classes whose contents are important and let it handle any ancillary objects referenced.

So define a serialization by cases. Call an object special if it falls under any case below but the last.

• For a tuple deemed to contain special objects:
  1. The character t
  2. The serialization of its len
  3. The serialization of each element, in order
• For a dict deemed to contain special objects:
  1. The character d
  2. The serialization of its len
  3. The serialization of each name and value, in sorted order
• For a class whose definition is salient:
  1. The character C
  2. The serialization of its __bases__
  3. The serialization of its vars
• For a function whose definition is salient:
  1. The character f
  2. The serialization of its __defaults__
  3. The serialization of its __kwdefaults__ (in Python 3)
  4. The serialization of its __closure__ (but with cell values instead of the cells themselves)
  5. The serialization of its vars
  6. The serialization of its __code__
• For a code object (since pickle doesn’t support them at all):
  1. The character c
  2. The serializations of its co_argcount, co_nlocals, co_flags, co_code, co_consts, co_names, co_freevars, and co_cellvars, in that order; none of these are ever special
• For a static or class method object:
  1. The character s or m
  2. The serialization of its __func__
• For a property:
  1. The character p
  2. The serializations of its fget, fset, and fdel, in that order
• For any other object: pickle.dumps(x,-1)

(You never actually store all this: just create a hashlib object of your choice in the top-level function, and in the recursive part update it with each piece of the serialization in turn.)

The type tags are to avoid collisions and in particular to be prefix-free. Binary pickles are already prefix-free. You can base the decision about a container on a deterministic analysis of its contents (even if heuristic) or on context, so long as you’re consistent.

As always, there is something of an art to balancing false positives against false negatives: for a function, you could include __globals__ (with pruning of objects already serialized to avoid large if not infinite serializations) or just any __name__ found therein. Omitting co_varnames ignores renaming local variables, which is good unless introspection is important; similarly for co_filename and co_name.

You may need to support more types: look for static attributes and default arguments that don’t pickle correctly (because they contain references to special types) or at all. Note of course that some types (like file objects) are unpicklable because it’s difficult or impossible to serialize them (although unlike pickle you can handle lambdas just like any other function once you’ve done code objects). At some risk of false matches, you can choose to serialize just the type of such objects (as always, prefixed with a character ? to distinguish from actually having the type in that position).

The authors of word2vec implemented the algorithm using an asynchronous SGD called: HogWild!. So you might want to look for tensor flow implementation of this algorithm.

In HogWild!, each thread takes a sample at a time and performs an update to the weights without any synchronization with other threads. These updates from different threads can potentially overwrite each other, leading to data race conditions. But Hogwild! authors show that it works well for very sparse data sets, where many samples are actually near independent since they write to mostly different indices of the model.