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Deep Convolutional Neural Network to classify emotions from input images.

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EmotionDetector

Model to classify happy or not happy from input images.

The code trains a 6 layer deep neural network, 4 convolutional layers and 2 fully connected layers for 50 epochs with the Adam Optimizer.
This model is trained on CPU.
Training Accuracy : 99.86%
Testing Accuracy : 95.99%
Train, Test split : 600 images for X_train, 150 images for X_test

You can test out this model using your own photos. Just put in the image path in the img_path variable and run the last cell.

Author : Sammya Majumdar 12th July 2020.

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Deep Convolutional Neural Network to classify emotions from input images.

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