Cat-Dog-Mouse Classifier

By Arda Mavi with edits by Benny Kolbeson and additional content by Paul Phelps

cat-dog-mouse classifier hybrid of the following tutorial and repository:

https://stackabuse.com/creating-a-neural-network-from-scratch-in-python-multi-class-classification/
https://github.com/ardamavi/Dog-Cat-Classifier.

Look up Data/Layer_Outputs folder for other outputs.

Using TensorBoard:

tensorboard --logdir=Data/Checkpoints/./logs

Model Architecture:

• Input Data Shape: 64x64x3

Layer 1:

• Convolutional Layer 32 filter Filter shape: 3x3

• Activation Function: ReLu

• Max Pooling Pool shape: 2x2

Layer 2:

• Convolutional Layer 32 filter Filter shape: 3x3

• Activation Function: ReLu

• Max Pooling Pool shape: 2x2

Layer 3:

• Convolutional Layer 64 filter Filter shape: 3x3

• Activation Function: ReLu

• Max Pooling Pool shape: 2x2

Classification:

• Flatten

• Dense Size: 64

• Activation Function: ReLu

• Dropout Rate: 0.5

• Dense Size: 2

• Activation Function: Sigmoid

Optimizer: Adadelta

Loss: Binary Crossentropy

Adding new train dataset:

If you want to add new dataset to datasets, you create a directory and rename what you want to add category (like 'cat' or 'phone').

If you want to add a new training image to previously category datasets, you add a image to about category directory and if you have npy files in Data folder delete npy_train_data folder.

Note: We work on 64x64 image also if you use bigger or smaller, program will automatically return to 64x64.

Important Notes:

• Used Python Version: 3.6.9
• tensorflow version 2.0.1
• Install necessary modules with sudo pip3 install -r requirements.txt command.