The objective of this project is to investigate the images using Keras, TensorFlow, and Deep Learning techniques. You may resize data (i.e. into 128×128) as it would be more convenient. Use 80% of samples (positive and negative) for training and the remaining 20% for testing per category. Question 1: Implement and select DCNN model that provides a good accuracy for dataset. For designing the best DCNN model, consider the following criteria: Good initialization method (Xavier or He initializer) Activation function: ReLU Question 2: Evaluated the performance of the model for different optimization functions: SGDAdamRMSProp Question 3: Show and explain the structure of the model used (can be in word with flowcharts or on paper) Question 4: Design a graphical user interface (GUI) that allows the user to select one image from a local file, run the testing process, and return the output as a message that says “You have chosen” either ‘POSITIVE ‘or ‘NEGATIVE’. NOTE: The language used should be Python and to be executed in Anaconda. And use local files rather than using Drive module.
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