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Your statement that researchers build their network from the ground-up using C++ or some other low level library couldn't be further from the truth. You could take a look at this analysis showing the popularity of these two frameworks in the top ML conferences. The following Figure is taken from there. In CVPR-2020, for example, TensorFlow and pytorch ...


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I can reproduce this problem for an even more easily separable dataset: The ideal tree for it should be as follows: However, when I run DecisionTreeClassifier with the maximal depth = 2 in scikit-learn many times, it splits the dataset randomly and never gets it right. This is an example of 4 different runs: The problem is that scikit-learn has only two ...


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After some research on the internet, I realized that using VOSK toolkit in python, it can be found (detect) any particular word in audio file or real time audio streaming. https://alphacephei.com/vosk/


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Arthur Juliani has some interesting Medium articles on reinforcement learning with TensorFlow backed up with code on GitHub. Part 0 — Q-Learning Agents Part 1 — Two-Armed Bandit Part 1.5 — Contextual Bandits Part 2 — Policy-Based Agents Part 3 — Model-Based RL Part 4 — Deep Q-Networks and Beyond Part 5 — Visualizing an Agent’s Thoughts and Actions Part 6 — ...


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There are many papers on this but the following is a good start: How to unwrap wine labels programmatically. The author includes source code in Python. You mentioned you do not want to do a panoramic view but that has more than one meaning. If I assume you mean you do not want to rotate the can while taking multiple photos, or you don't want to take ...


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The approach that you don't train the whole net, but just the latter part of it (all starting with lstm in our case), can actually work. The idea is that the inception was already pretrained a very large dataset (imagenet for instance). And it's capable of extracting some useful information from it. Actually there are different domains of images in the ...


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On page 2 of Axis' web page Identification and Recognition there is an estimate of the minimum number of pixels needed for identification, recognition and detection.


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This is a pretty standard minimum "quality" (better said resolution in pixels between the eyes) needed for a facial recognition system: Ensure that the image contains a frontal view of the face, good lighting, and at least 80 pixels between the eyes. the bare minimum to identify a human face would be 25 to 75 pixels just between the eyes In the ...


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I think this should work for you: scipy.signal.correlate | SciPy I used it myself while I was writing a CNN in numpy.


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I suggest you to have a look at this repo. It contains state-of-the art algorithms, papers, frameworks, courses and some implementations. You can also check "Deep Reinforcement Learning Hands On" book examples written by Max Lapan here. This repo contains many programming and reinforcement learning examples with PyTorch framework.


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At a basic level, these kinds of low-dimensional plots where you look at one or two variables at a time can help to give you a sense of what types of relationships you might expect to see, such as linear, non-linear, or periodic relationships, which can steer you toward an appropriate family of models. You wouldn't want to use a linear model to predict data ...


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Both ways are valid. It depends on what you want from the model and expect from the data. Generally though I would use 1 assumption and stick with it (unless there was a specific reason not to), so I would use all lines for test if training done that way, and same for majority. Also note if you ever get more than 3 people, you can choose to do a variance ...


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In general, multi output models is not that different. I.e. As Raghu mentioned in commentary, you could train separate model for each output. There is even helper module in sklearn for that (MultiOutputRegressor) DecisionTreeRegressor from sklearn allows multiple outputs out-of-the box Any neural network framework allows any number of outputs In your ...


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Your call to model.predict() is returning the logits for softmax. This is useful for training purposes. To get probabilties, you need to apply softmax on the logits. import torch.nn.functional as F logits = model.predict() probabilities = F.softmax(logits, dim=-1) Now you can apply your threshold same as for the Keras model.


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Yes, there is. You can try Spacy. Here you go. import spacy from spacytextblob.spacytextblob import SpacyTextBlob nlp = spacy.load('en_core_web_sm') spacy_text_blob = SpacyTextBlob() nlp.add_pipe(spacy_text_blob) text = "i'm good" doc = nlp(text) print(doc._.sentiment.polarity) # 0.7 text = "i'm bad" doc = nlp(text) print(doc._....


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External sampling and outcome sampling are two ways of defining the sets $Q_1, \dots, Q_n$. I think your mistake is that you think of the $Q_i$ as fixed and taken as input in these shampling schemes. It is not the case. In external sampling, there is as many sets $Q_{\tau}$ as there are pure strategies for the opponent and the chance player (a pure strategy ...


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If you have sold only once or very few items you will need some prior input (domain knowledge). One term for search is intermittent time series. Here is a stored search. When you have many time series, related, and interest in both totals and single series, that is called hierarchical forecasting. One expert is here (the author of that blog was the founder ...


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From your question there is no indication that there is any pattern to these digits. If there were, the recommendation for an LSTM or RCNN would make sense. In the case of random values, I have found that a two or three layer CNN that then descends through two parallel dense networks does an excellent job identifying CAPTCHA style random characters. One ...


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Your task is text recognition, however your code is for classification task. So you need to use different approach for that. You mentioned that you're going to give model 123 and get 123. But you can not do that with just convolutional networks. Images with text are sequential, so you need to use CRNN(Convolutional-Recurrent-Neural-Networks), LSTM(Long-Short-...


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To answer the question in the title, your enclosed method is a valid way to use 2d convs after a flattened feature vector. However, the bad results you experience could come from the structure of your model or from the way you train it. Regarding you last question, it is very hard to give you an advice without knowing your intentions in detail. Regardless, ...


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This is an image to better understand lstm... At $f_t$, we are taking the sigmoid of a weight matrix * the input at the current timestep + another weight matrix * $h_{t-1}$ Code Sample for $f_t$: import numpy as np import math def sigmoid(values): sigmoid_applied = [] for value in values: result = 1 / (1 + math.pow(math.e, -value)) ...


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You can do this similar to the BIDE approach. It can be done like this: class TreeNode: def __init__(self, element, depth, count=0, parent=None): self.count= count self.element= element self.depth= depth self.subnodes= dict() self.parent= parent def __repr__(self): return f'{self.__class__....


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Firstly, concatenate only works on identical output shape of the axis. Otherwise, the function will not work. Now, your function output size is (None, 32, 50) and (None, 600, 1). Here, '32' and '600' must be same when you want to concatenate. I would like to suggest some advice based on your problem. You can flatten both of them first and then concatenate. ...


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This is not necessarily the only way to do this but it would be the approach I'd take. Assuming your agents position is a vector in $\mathbb{R}^d$, then I would have the network take as input this position vector and pass it through a fully connected layer. I would also take as input the matrix and pass it through a convolutional layer(s) and flatten the ...


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So generally, when you seperate your training data to 80%-20% then you fit method should get 2 x,y. better to call them x_train,y_train, x_val, y_val or something similar. Now its important you do the split before entering the fit, and not do it for each epoch or something alike. Once you do that and the fit method should be something like: def fit(self, ...


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If you're using a library such as Trax which contains great submodules for various Transformers (Skipping, BERT, Vanilla and Reformer) you can use the inbuilt trax.data.inputs.add_loss_weights() function and provide a value for the id_to_mask parameter. Example Usage: train_generator = trax.data.inputs.add_loss_weights( data_generator(batch_size, x_train, ...


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you could also just use a Task-agnostic CNN as an encoder to get extract features like in (1) and then use the output of the last global pooling layer and then feed that as an input to the LSTM layer or any other downstream task. Add another small Neural Network (projection head) after the CNN. And then use contrastive loss on output of this projection head ...


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I don't think there's a "standard way" of expressing the forward pass: you use the transpose when you need to use it, and this depends on how you define the weights and inputs matrices, and on the architecture of your neural network. For example, in a fully connected feedforward neural network, you know that every neuron in the previous layer is ...


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I had to change the actions selection function for this and tune some hyper-parameters. Here's what I did to make it converge: Sampled the noise from a standard normal distribution instead of sampling randomly. Changed the polyak constant (tau) from 0.99 to 0.001 (I didn't have an idea of what it should be, so I had just set it randomly in the first try) ...


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You may want to take a look at this article, but I'll summarize. You can use BERT (or some other tool) to make embeddings of every word in every sentence. Then for each word, make a contextualized embedding vector using the rest of the sentence. bert-embedding does all of this itself. Then keep the embedding vector for the important words. For each important ...


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