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It seems that the SeqSelfAttention layer is expecting all the time-steps. i.e. return_sequences=True Same is shown in the home page example. Link import tensorflow as tf, numpy as np from tensorflow import keras from tensorflow.keras.layers import Dense, Dropout,Bidirectional,Masking,LSTM from keras_self_attention import SeqSelfAttention X_train = np....


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The way you have set your DQN up, it is designed to solve just one maze at a time. It has not (and cannot) learn to solve mazes in general, because it has no access to data about the layout of the maze, and a basic DQN agent has no capability to memorise layout seen so far. You could view the training process as general algorithm for "solving the maze&...


3

If you have two images, you first start to make histograms of the values (0-255) in the three color channels (red, green, and blue). In the article 25 bins are used, meaning that the values are assigned to one of 25 ranges. The second step is to then concatenate the the three single channel histograms to one histogram for the full image. Since each image ...


2

This is a pretty involved question since this is an active area of research. The first statement is that often, the architecture is important (or number of parameters) before we can say something to the effect of we require $O(n^{k} log(\frac{1}{\delta^i}))$ for $i, k \ge 1$ samples to converge to a local optima. Guaranteeing accuracy is also depending on ...


2

Why are the training and test accuracy almost identical? Nearly identical performance on the training set and test set is a good outcome, it means the model is doing what it's supposed to do. To give an intuitive comparison: The performance on the training set is equivalent to how well a student can redo the exercises which have been solved by the teacher ...


2

Assuming you cannot add more memory to your computer (or free up some of the memory), you could try 2 general approaches: Read only some of the data into memory e.g. a subset of the rows or columns. reduce the precision of the data from float64 to float32. From your error, it looks like you are loading data into a numpy array, so somewhere in your code, ...


2

The only thing that you need to do is to start your agent and the goal/end at a random (non-overlapping) location. You can try your setup initially with an empty grid (no walls). If DQN learns, your set up is good and you can start introducing obstacles into the grid. Gradually, the agent will start associating the end location inputs as something rewarding ...


2

For deep learning, there are a few model hubs where folks share models that are suitable for further fine-tuning or usage in given areas. None of these will be in the pickle format (from your question), but they are great resources nonetheless: PyTorch Model Hub Tensorflow Hub Hugging Face Models


1

That is indeed a drawback with grid search strategy, since you must know in advance each one of the possible combinations to try out, and that might be not optimal neither to get the best evaluation metric value nor in computation performance. You have other interesting strategies, not exhaustive hyperparameter search, for instance random search or based on ...


1

From the info you provide, it seems you are carrying feature selection based on the correlation between your predictor variables and the target. This is correct as a type of feature selection (see here) in the family of univariate filter selection, although not the only one. It is fast and intuitive, although you can have a look at other methods. You might ...


1

dW and db are simply the derivative of the loss function with regards to the weights and biases. Given the loss function $J = \frac{1}{m} \Sigma_{i=1}^{m}(y_i - h(x_i))^2$ the derivatives of the loss to the weights (dW) and bias are equal to $\frac{\partial}{\partial W} J = -\frac{2}{m} \Sigma_{i=1}^{m}(y_i - h(x_i)) * x_i$ $\frac{\partial}{\partial b} J = -\...


1

To address your two questions: Agglomerative clustering requires a distance metric, but you can compute this from your consensus-similarity matrix. The most basic way, is to do this: distance_matrix = 1 / similarity matrix Although, they may explicitly state in the paper what function they use for this transformation. I think this is just to say that the ...


1

The term is Oracle. Some references: SO question describing the term Scientific articles related to machine learning using the term


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Consensus clustering is an additional evaluation technique (i.e. how much consensus is there between clusters), like the Silhouette score, rather than explicit clustering. The clusters themselves would drop out earlier in the processing. The consensus metric is used to test for the best K value, in the K-means clustering algorithm.


1

Shallowness is when the Model has very few Or just one hidden layer. When it has a lot of hidden layers it will be a Deep Neural Network. Sum-product is basically a Neuron of a Hidden Layer. From the paper- Artificial neural networks with several hidden layers, called deep neural networks, have become popular due to their unprecedented success in a variety ...


1

This is a cross-posting from CrossValidated: In support vector regression (with linear loss), we minimise the objective function: \begin{align} \min_{\mathbf{w}, b, \mathbf{\xi}} \quad & \frac{1}{2}\| \mathbf{w}\|^2 + C\sum_i \mathbf{\xi}_i + \mathbf{\hat \xi}_i \\ \text{s.t.} \quad & (\mathbf{w} \cdot \mathbf{x}_i + b) - y_i \leq \varepsilon ...


1

The Euclidean between two images $p$ and $q$ can be calculated as follows: $d(p, q) = \sqrt{(q_1 - p_1)^2 + (q_2 - p_2)^2 + ... + (q_{49} - p_{49})^2}$ which is the distance between the 49 (7x7) features of the two images. This should then give you a vector of shape (1024, 1) where each value is the Euclidean distance of the feature maps of the previous ...


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