CNN Classifier written from scratch in python without using any libraries like Keras and Tensorflow
- Set up a new git repository in your GitHub account
- Pick two datasets from https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research
- Choose a programming language (Python, C/C++, Java)
- Formulate ideas on how neural networks can be used to accomplish the task for the specific dataset
- Build a neural network to model the prediction process programmatically
- Document your process and results
- Commit your source code, documentation and other supporting files to the git repository in GitHub
tf.keras.datasets.mnist.load_data(path="mnist.npz")
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This is a dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images.
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x_train, x_test: uint8 arrays of grayscale image data with shapes (num_samples, 28, 28).
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y_train, y_test: uint8 arrays of digit labels (integers in range 0-9) with shapes (num_samples,).
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License: Yann LeCun and Corinna Cortes hold the copyright of MNIST dataset, which is a derivative work from original NIST datasets. MNIST dataset is made available under the terms of the Creative Commons Attribution-Share Alike 3.0 license.
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The data files train.csv and test.csv contain gray-scale images of hand-drawn digits, from zero through nine.
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Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive.
tf.keras.datasets.fashion_mnist.load_data()
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This is a dataset of 60,000 28x28 grayscale images of 10 fashion categories, along with a test set of 10,000 images. This dataset can be used as a drop-in replacement for MNIST. The class labels are:
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Label Description
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0 T-shirt/top
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1 Trouser
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2 Pullover
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3 Dress
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4 Coat
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5 Sandal
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6 Shirt
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7 Sneaker
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8 Bag
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9 Ankle boot
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Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. Zalando intends Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the same image size and structure of training and testing splits.
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License: The copyright for Fashion-MNIST is held by Zalando SE. Fashion-MNIST is licensed under the MIT license.
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Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255, inclusive.
- Input Layer has 784 neurons(28 x 28)
- Hidden Layer has 15 neurons
- Output Layer has 10 neurons(10 classes)
- Activation Function used: sigmoid function
- Softmax function is used in Output layer
- Loss Function: Sum of Squares.
- Here are some of the predictions:
- Here are some of the predictions:
- Our model trained pretty well but we could not obtain similar train and test accuracies this might be because of overfitting or not enough data to train our model or a shallow network with only one hidden layer.
- In future we can try to implement a deeper network with more hidden layers and also instead of initializing weights and biases with random values we can use some standard weights which are trained on more powerful machines.
- Project description
- Jupyter Notebook implementing Digit-Recognizer model.
- Jupyter Notebook implementing FashionClassifier model.
- Visualizations in png format images
- Info about Tools, frameworks and libraries required to reproduce the work flow