A set of known flaw characteristics in FIG reconstituted wafer (Reclaim wafer defective feature map) ofInformation ( such as accessories ) , please Keras API   construct valid MLP and CN classifier (. Classifier) , this information for classification into the required ten kinds of classification, must be based on a series of experiments (DOE) decided to train ( and predict ) the Relevant parameters, in order to get the best classification and prediction results, and complete the relevant report with words ; use Python , according to the following description, gradually complete the required functions ( please complete the function according to the function, to benefit the score; another, take-home exam to honor the exam, do not discuss, a total of 110 points, up to 100 dollars )Python program write requirements: Create a databalancing function (or method): Read 10 types of data into the system (various types), and copy the number of types of data to the maximum number of classes (as long as they are close), and at the same time After performing shuffle and split, return train_feature_list , train_target_list , test_feature_list , andTest_target_list, which is printed on the screen; please confirm that the data is correctly read and stored as feature andTarget Four txt files for subsequent reading (in self-built function, other methods do not give points, please scale at the same timeInterval data of [0, 1] (10%)Comparing MLP data balacing: Constructing models of various architectures of MLP, comparing the results of training and testing with data balancing respectively (Note: self-determination and decision to stop conditions, all training, testing, model data and processes must be recorded, including: Train acc, val acc, train loss, val loss, model structure, model, confusion matrix, and draw graphics with excel for comparison, to show whether data balacing has improved training effect, and provide the best model and its acc, loss, etc. Information) (20%)Compare CNN s data balacing: Construct models of CNN s various architectures, compare and compare data balancingTraining and testing results (Note: ibid.) (20%)Compare MLP data augment: Submit the above questions, construct various architecture models of MLP, compare data augment to classified images, data augment must include: up and down flapping, left and right flipping, rotation 45 degrees(or more), the requirements are the same as the first two questions (the same training, test process, best model, recognition rate, etc. must be provided, and data augment can be raised to improve the recognition rate) (20%)Compare CNN s data augment: Same as above, but use CNN (20%)Confirmatory experiment: The above-mentioned MLP and CNN optimal network were repetitive experiments (three times each run), and the comparison of the average discriminating ability of MLP and CNN for the data was compared, and the final conclusion of the individual was made (conclusion includes : Overall experimental results, and comment on the impact of data balancing, data augment on MLP and CNN training; comparison of MLP and CNN, etc. (20%)Chart suggested format: Note:

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