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The reason for this seems to be your are importing from tensorflow.keras.layers import * But while your are calling you are using : layers.Input(shape=(IMG_SIZE, IMG_SIZE, 3)) this calling will give you an error so instead try below import Please try this import : from tensorflow.keras import layers


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Solution 1 : Target Encoding using Weight of evidence Weight of evidence would be a good candidate for this scenario. Initially when you have train data calculate weight of evidence on train data as follows; Calculate the number of events and non-events in each group (bin) Calculate the % of events and % of non-events in each group. Calculate WOE by taking ...


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It depends on the contextual link between A and B. If they are completely different categories with no or low correlation, there shouldn't be necessary to have a single class multi label. But if A and B are somehow connected, overall if they can represent a scale together (i.e. AB = [0 0] = 0 = "low impact" or AB = [1 1] = 3 = "high impact&...


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You can use bart-large-mnli model open-sourced by facebook and is available here Once you download all the files you can create a microservice and expose it as an API or alternatively run it in batch processing model


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I think, you are trying to link a particular analysis logic to model architecture, however this is not how this not how cnn and most DL models work. The logic behind decreasing the filter size or the network dimensionality as a general rule, is the "distillation" of characteristics that differentiate classes. In other words a representation of ...


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To answer my question, I will use three types of models - KNN Regression Regression Trees Complex models like NN or SVM KNN Regression It is a non-parametric regression model, and the confidence might be explicitly modeled using mean absolute error or mean squared error. At the test time, for a given instance, K nearest instances will be found, and ...


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This is evaluation and it's done experimentally: with a test set of fresh instances containing the true target value, apply the model and measure the error across all the instances (e.g. with MAE, MSE, RMSE...). Assuming that the test is a sufficiently large representative sample of the data, it's possible this way to estimate the quality of the model ...


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The procedure that you can use is the following. First cluster your data with gaussian mixture models. This method should also work with multiple lines with different slopes. It should be able to deal with intersections as points near an intersection can belong to both clusters and a wrong classification will not lead to huge differences in the results of ...


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Reinforcement learning (RL) is a useful way to frame problems when actions that are taken change the environment. The best way to assign credit for outcomes is through experimentation. By carefully designing an experiment, you can figure out if the intervention has a causal impact on the result. Often it takes a series of experiments to understand the cause-...


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