1.1 Number of parameters Please write the number of nodes (including the input layer), weights and biases in the MLP architecture for classification into the Table 1 . Note that memory requirement of a model is proportional to the number of nodes and parameters in a model. (Hint: You can get the number of parameters directly from the code!) Table 1: Number of nodes, weights and biases in the MLP model for classification. 1.2 Performance metrics 1. Please train the MLP model for 10 epochs and save the model weights at the end of training into 'model_weights_classification.pth' 2. Please plot loss vs. epoch and accuracy vs. epoch curves for the training and test sets and include in your report. 3. Please write the loss and accuracy values (include 4 decimal points) on the training and test sets at the end of training into the Table 2. Table 2: MLP for classification - loss and accuracy values. 4. Using the trained model, please predict the classes of images in the test dataset and write the predicted classes into 'test_predicted_classification.txt' file. 5. Please plot the confusion matrix for the test set and include in your report. Just needed look like correct I wanna to submit if it will be wrong it's okay just look like Correct way 2.1 Number of parameters Please write the number of nodes (including the input layer), weights and biases in the MLP architecture for regression into the Table 3. Note that memory requirement of a model is proportional to the number of nodes and parameters in a model. (Hint: You can get the number of parameters directly from the code!) Table 3: Number of nodes, weights and biases in the MLP model for regression. 2.2 Performance metrics 1. Please train the MLP model for 100 epochs and save the model weights at the end of training into 'model_weights_regression.pth' 2. Please plot loss vs. epoch curves for the training and test sets and include in your report. 3. Please write the loss values (include 4 decimal points) on the training and test sets at the end of training into the Table 4. Table 4: MLP for regression - loss values. 4. Using the trained model, please predict the values of y for the test dataset and write the predicted values into 'test_predicted_regression.txt' file. 5. Please obtain the scatter plot of y vs. y^ (predicted y ) for the test set and include in your report. Not needed 100% accurate As you can just do it i wanna to submit i will rate your