Rock-Paper-Scissors Classifier

Rock-Paper-Scissors Classifier is a convolutional neural network (CNN) model that classifies images of hands. The CNN model detects whether the image given is of a "rock", "paper", or "scissors" hand shape. The model is written in Python using Tensorflow and Keras libraries.

The dataset used is the Rock-Paper-Scissors dataset provided by Dicoding. After training and testing, the model has 96.48% accuracy and 96.58% validation accuracy. This model was compiled with the "categorical crossentropy" loss function, "Adamax" optimizer and uses accuracy as its metric.

This project was an assignment for Dicoding's Machine Learning path.

notebook-python
model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(256, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(3, activation='softmax')
])

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Google Colaboratory