LiuShi is a deep-learning project that utilizes a custom-made Convolutional Neural Network (CNN) architecture to recognize handwritten Chinese numerals.
Model: "sequential"
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Layer (type) Output Shape Param #
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conv2d (Conv2D) (None, 64, 64, 32) 320
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max_pooling2d (MaxPooling2D) (None, 32, 32, 32) 0
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conv2d_1 (Conv2D) (None, 32, 32, 64) 18496
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max_pooling2d_1 (MaxPooling2 (None, 16, 16, 64) 0
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conv2d_2 (Conv2D) (None, 16, 16, 128) 73856
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max_pooling2d_2 (MaxPooling2 (None, 8, 8, 128) 0
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flatten (Flatten) (None, 8192) 0
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dense (Dense) (None, 1024) 8389632
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dense_1 (Dense) (None, 512) 524800
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dense_2 (Dense) (None, 256) 131328
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dense_3 (Dense) (None, 15) 3855
=================================================================
Total params: 9,142,287
Trainable params: 9,142,287
Non-trainable params: 0
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Built by Justine Paul Vitan as a solo project to demonstrate his capabilities in developing deep-learning applications with TensorFlow and Keras. The source code of this project is open and available to the public via GitHub for transparency and open-source collaboration.
This project is under the MIT license. Please read the terms and conditions stated within the license before attempting any modification or distribution of the software.