1 Question Given the MNIST handwritten digits dataset, implement the following networks and report the results of the test accuracy via screenshot. Make sure to provide necessary comments on your code. (a) Implement the following deep learning architecture and evaluate its test accuracy. network = models.Sequential () network.add (layers.Dense (128, activation='sigmoid')) network.add (layers.Dense (10, activation='sigmoid')) network.compile(optimizer='sgd',loss='mse',metrics=['accuracy']) network.fit (X.train, y.train, epochs=20, batch size=16) (b) Given that you are able to change the mumber of layers, number of neurons, activaiton functions, and the optimizer. Design your own architecture, provide your models code, and a screenshot of the highest test accuracy of your model. activation : ['sigmoid', 'tanh', 'relu'] optimzer : ['sgd', 'RMSprop', 'Adam'] (c) Provide a high-level description of your model and include screenshot of your best network's wrong predicitons.

1 Question Given the MNIST handwritten digits dataset, implement the following networks and report the results of the test accuracy via screenshot. Make sure to provide necessary comments on your code. (a) Implement the following deep learning architecture and evaluate its test accuracy. network = models.Sequential () network.add (layers.Dense (128, activation='sigmoid')) network.add (layers.Dense (10, activation='sigmoid')) network.compile(optimizer='sgd',loss='mse',metrics=['accuracy']) network.fit (X.train, y.train, epochs=20, batch size=16) (b) Given that you are able to change the mumber of layers, number of neurons, activaiton functions, and the optimizer. Design your own architecture, provide your models code, and a screenshot of the highest test accuracy of your model. activation : ['sigmoid', 'tanh', 'relu'] optimzer : ['sgd', 'RMSprop', 'Adam'] (c) Provide a high-level description of your model and include screenshot of your best network's wrong predicitons.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

See similar textbooks

Related questions

Q: Implement the perceptron rule training of the network using f(net) = sgn (net), c = 1, and the…

A: The Perceptron Rule is a simple algorithm used for supervised learning of binary classifiers,…

Q: What is a modelled perceptron network? Can this method be used in basic linear classification, where…

A: Understanding the Perceptron Network Model A perceptron network is a fundamental building block of…

Q: Modify the original SEIR model in your computer and save it as a new model to include a population…

A: According to the question , we have to modify the original SEIR model in your computer and save it…

Q: You are getting continuous feeds from stock market. You need to develop an algorithm for real-time…

A: As per the Bartleby guidelines i have attended first three question at a time. K-Means Clustering…

Q: train an artificial neural network using CIFAR10 dataset. You can get the dataset from Keras similar…

A: utilize the enhancers from this rundown: SGD, SGD with force, RMSProp and Adam I'm involving keras…

Q: Give a concrete example of the difficulty that arises when a neural network has several layers.…

A: Answer:

Q: Use k-nearest neighbor (k=3) with Manhattan distance to classify the node (2,3) with an unknown…

A: Manhattan distance between the points x(x1,x2,...xn) and y(y1,y2...yn) is given below. Manhattan…

Q: Pipelining fails under the Princeton computing model. Give an explanation of the situation and offer…

A: Princeton Computing Model: PC engineering is based on John von Neumann's 1945 depiction of the von…

Q: With the rise of edge AI, how are serialization methods optimized for lightweight machine learning…

A: As the field of artificial intelligence continues to advance, one of the key trends that has emerged…

Q: QI. Sketch a GAN architecture where in addition to the true pattems (instances). we also have access…

A: GAN is a General Adversarial Networks approach which generates modeling using deep learning methods…

Q: 3. Two more lavers (quant. dequant) appeared after we guantized our model using the PyTorch OAT…

A: In this question we have to understand Let's understand and hope this helps if you have any queries…

Q: Provide an illustration of the challenge that arises when a neural network has a large number of…

A: Introduction: INTERACTIVE NEURAL NETWORK: An interactive neural network is a collection of…

Q: The architecture of a neural network is provided. The input is a 3D feature vector, and the output…

A: Het there, I am writing the required solution for the above stated question.

Q: Not all augmenting paths are equal, and starting with different paths leads to different residual…

A: Given that, Not all augmenting paths are equal, and starting with different paths leads to different…

Q: einsteineruploading up to get together with. In many aspects, a contemporary Turkish individual…

A: Automation deception: "No," it is not feasible to deceive people using a method like Mechanical…

Concept explainers

Use of XOR function

XOR function or exclusive OR function was introduced in Excel 2013. This function is available under excel logical functions. For the given statement, the XOR function will return true only when either of the statements is true. It returns false when both the statements are true or when both the statements are false.

Question

Expert Solution

This question has been solved!

Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.

This is a popular solution!

SEE SOLUTION Check out a sample Q&A here

Step 1

Step 2

Step 3

Step 4

Trending now

This is a popular solution!

Step by step

Solved in 4 steps with 6 images

SEE SOLUTION Check out a sample Q&A here

Knowledge Booster

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.

Similar questions

Use python machine learning. Compare gradient boosting trees and random forest. What are the pros and cons of these two models?
Consider several threads that are common throughout all modules of our course. They are reiterated, while belonging to different contexts. Initial slide in front of every module has … “focus on learning, as this is the only worthy pursuit, particularly in the context of this class”. Watts Humphrey introduced a narrative of constant learning, while arguing a logical order of unit test performed after peer reviews. State another example of a context (a place in our course) where this notion of a “permanent learning” is reiterated.
Justify your answer with specific instances of why you would employ one particular machine learning model over another. Both hierarchical and unstructured clustering techniques exist. K-Nearest Neighbor (a), Going Backwards (c), and Learning More (d)
Consider a dataset consisting of grayscale images of size 100 x 100 pixels and a binary label. A deep neural network combining convolutional, pooling and fullyconnected layers is chosen for building a classifier for this dataset.
Your task is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Implement and train a classification model for the Titanic dataset (the dataset can be found here: https://www.kaggle.com/c/titanic). Please ignore the test set (i.e., test.csv) and consider the given train set (i.e., train.csv) as the dataset. What you need to do: 1. Data cleansing 2. Split the dataset (i.e., train.csv) into a training set (80% samples) and a testing set (20% samples) 3. Train your model (see details below) 4. Report the overall classification accuracies on the training and testing sets 5. Report the precision, recall, and F-measure scores on the testing set 1. Required Model (100 pts): Implement and train a logistic regression as your classification model. • You have to use Sklearn deep learning library. • You may want to refer to this tutorial: https://bit.ly/37anOxi
train an artificial neural network using CIFAR10 dataset. You can get the dataset from Keras similar to mnist dataset - try to find the best performing model for your dataset (CIFAR-10), use the splitting for train/val/test as 80/10/10 attach the screenshot of code and output
Suppose you are fitting a neural network with one hidden layer to a training set. You find that the cross validation error Jcv (0) is much larger than the training error Jtrain (0). Is increasing the number of hidden units likely to help? CV Yes, because it is currently suffering from high bias. Ob. Yes, because this increases the number of parameters and lets the network represent more complex functions. Oc. No, because it is currently suffering from high variance, so adding hidden units is unlikely to help. Od. No, because it is currently suffering from high bias, so adding hidden units is unlikely to help.
Step 1. Intersection over Union def intersection_over_union(dt_bbox, gt_bbox): ---> return iou Step 2. Evaluate Sample def evaluate_sample(target_pred, target_true, iou_threshold=0.5): ----> return results Step 3. Evaluate Model def evaluate(model, test_loader, device): ---> return auc(recall, precision) Step 4. Train function Now define the functions for training the model. def train_one_epoch(model, train_dataloader, optimizer, device): # YOUR CODE HERE # TRAIN YOUR MODEL ON THE train_dataloader pass def train(model, train_dataloader, val_dataloader, optimizer, device, n_epochs=10): for epoch inrange(n_epochs): model.eval() test_auc = evaluate(model, val_dataloader, device=device) print("AUC ON TEST: {:.4f}".format(test_auc)) model.train() train_one_epoch(model, train_dataloader, optimizer, device=device) #for refrence and data detail go to --->…
Suppose you are designing a deep learning architecture to solve an object detection problem, where there areN numbers of objects and each object has M number of images. If the resolution of each image is 227 x 227 x 3, then answer the following questions: (a) Write down the name of the layers of your deep learning architecture including parameters dimension. (b) How many convolution layers will you use in your deep learning model and why? (c) Is it a good idea to use multiple fully connected layers? Motivate your answer
Question 1 We designed a MLNN in Python. How many parameters are in the first hidden layer of this NN? Remember to count for bias. model_a=Sequential() model_a.add(Dense(input_dim=4, units=300, activation='tanh')) model_a.add(Dense(units=10, activation="tanh')) model_a.add(Dense(units=len(classes), activation='softmax')) model_a.summary() Question 2 In the previous question, what is the number of parameters in the second hidden layer?
Using specific instances, explain why you would choose one machine learning model over another. Two different grouping techniques exist: (A) looking backward (C) using K-nearest neighbor (D) learning more.?
The following figure is a diagram of a small convolutional neural network that converts a 16x16 image into 4 output values (16x16 - 4x12x12 → 4x6x6 6x1). The network has the following layers/operations from input to output: convolution with 4 filters, max pooling, ReLu (after pooling), and finally a fully-connected layer (with no hidden layer). For this network we will not be using any bias/offset parameters. Please answer the following questions about this network. 16x16 Convolution 4 filters 5x5 Stride 1 4@12x12 max pooling 2x2 Stride 2 4@6x6 6x1 fully- connected