Deep Learning (CS230) CS 231N

Deep Learning (CS230) CS 231N CS 231NConvolutional Neural Networks for Visual RecognitionSpring 2019 Sample Midterm ExamMay 7, 2019Full Name:Question ScoreTrue/False (20 pts)Multiple Choice (40 pts)Short Answer (40 pts)Total (100 pts)Welcome to the CS231N Midterm Exam!•The exam is 1 hour 15 minutes.•No notes or electronic devices are allowed.I understand and agree to uphold the Stanford Honor Code during this exam True / False (20 points)Fill in the circle next to True or False, or fill in neither. Fill it in completely like this: .No explanations are required.Scoring: Correct answer is worth 2 points. To discourage guessing, incorrect answers are worth -1 points.Leaving a question blank will give 0 points.1. Your Neural Network is not gradient checking. It could be that it’s because you’re using the AdaGradupdate instead of Vanilla SGD.#True#FalseSOLUTION:False. Gradient check happens before multiplication with learning rates.2. If the input to a ConvNet is a zero image (all zeros), then the class probabilities will come out uniform.#True#FalseSOLUTION:False. May have nonuniform bias terms.3. Turning off L2 weight regularization will likely lead to higher accuracy on the training set.#True#FalseSOLUTION:True. Turning off regularization increases chances of overfitting, which may give us higher accuracy onthe training set.4. It’s sufficient for symmetry breaking (i.e. there will be no symmetry in the weights after some updates)in a Neural Network to initialize all weights to 0, provided that the biases are random