Construct a Convolutional Neural Network (CNN) architecture from scratch to extract features from the images.
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Write a python program to perform the following tasks:1. Load the dataset for all three classes and resize each image to (32 x 32). Apply the required preprocessing steps to employ the data into Machine Learning / Deep Learning algorithms.2. Construct a Convolutional Neural Network (CNN) architecture from scratch to extract featuresfrom the images. (HINT: Extract features for train and test set separately. Extract the featuresconstructed by the convolutional layers from an intermediate dense layer. Please refrain fromusing any pre-trained model for implementing this step)3. Apply the K-Nearest Neighbor (KNN) algorithm to the extracted features from CNN and find theoptimal value of K. The value of K can be considered as [3, 5, 7, 9]. Determine the performance ofthe model using an appropriate performance metric. Draw a graph of K values and theircorresponding performance in order to represent your results.4. Apply Random Forest (RF) algorithm to the extracted features from CNN. Tune at least twohyperparameters using random search. Determine the model’s optimal performance, theconfusion matrix, and the value of hyperparameters producing the optimal performance.5. Report the performance of each model and explain your results. (eg. overfitting, underfitting, etc.)
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