Vgg16 using R

see details 1. Clear the session and load the CIFAR10 data into a variable called cifar. (5 points)

2. Create a small training dataset using the first 1000 training images (and the corresponding labels) from CIFAR10. Similarly, create a small test dataset (and the corresponding labels) using the first test 500 images from CIFAR10. (5 points).

3. Create one-hot encoding for the labels for both train and test labels. (5 points)

4. Instantiate a VGG16 convolutional base without the top layer. (5 points)

5. Extract features from the CIFAR10 images so as to fit the conv_base. (40 points)

6. Flatten the features in order to feed them to a densely connected classifier. (5 points)

7. Build a model with one dense layer with 256 units and “relu” activation, one dropout alyer with 50% dropout rate, and a dense output layer with appropriate parameters. (15 points)

8. Compile the model with categorical_crossentropy as the loss function and optimizer_rmsprop with 0.01% learning rate (lr=0.0001). (5 points)

9. Fit the model using 30 epochs. Plot the loss and accuracies. (5 points)

10. Note that the model is likely to have low accuracy. Explain why. (10 points)

Deadline- 1 day

Also, add the comments in the code based on Question Number.

Skills: Artificial Intelligence, Keras, R Programming Language

See more: open notepad exe using vb6 pass parameters, track phone using imei number, can send sms using imei number, how to train vgg16 model, vgg 16 architecture explained, vgg16 architecture keras, vgg16 25088, image classification using vgg16, how to load vgg16 in tensorflow, vgg16 tensorflow tutorial, vgg feature extraction, using wordpress number directory, mobile website login using phone number, cell phone tracker using phone number, mobile phone spy using phone number, ping location using phone number, maximum number parameters mips, using mobile number unique identifier loyalty programs, calculate gpa using java number grade credits, using phone number java program

About the Employer:
( 1 review ) Ahmedabad, India

Project ID: #19339519