TensorFlow Python3 assignment

tensorflow assignment help using python3

Build a training model to recognize the uploaded image and display the results.

should be available at: [login to view URL]~xxxx/CPS5921/[login to view URL]

 Requirement and grading criteria: Build web pages on [login to view URL] that can

o Have at least 5 classes (training set), each should have at least 15 different images.

o (Build the training model based on YOUR training set.

o ) Test your training model with 10 known images and calculate the accuracy rate of your

training model.

Build a web page for users to upload and save new images into the “uploads” folder under

YOUR nune3 folder

Call the TensorFlow testing program, show the prediction result of the uploaded image on

the a web page based on YOUR training model

Display the uploaded image on the result web page

Documentations. Please write the followings under the upload button on nune3.html.

 List the labels and briefly describe each image dataset and the number of images  List your accuracy rate and how to improve your accuracy?


• TensorFlow - Google machine learning library

[login to view URL]

• Example, build a dog database (training set) based on 20,580 images as shown in the following link: [login to view URL]

• Develop methods to read an input dog image, call libraries to extract the features and predict the dog

type of an unknown dog image against the database. You can refer to the following information

[login to view URL]

• You will take/download some dog images to test your method.

• You will need to have a web interface to allow user upload the dog image and prediction results should

be shown on the browser in numbers.

• Please study and follow the following tutorials:

• TensorFlow Tutorial #07 Inception Model [login to view URL]

• Build a TensorFlow Image Classifier in 5 Min [login to view URL]

• Train an Image Classifier with TensorFlow for Poets [login to view URL]

• Python and TensorFlow tutorials:

– [login to view URL]

• Image recognition using TensorFlow Python API tutorials:

– [login to view URL]

• Google demo website for references: [login to view URL]

Квалификация: Python

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ID проекта: #19241856

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