| #------------------------------------------------------------- |
| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
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| # distributed with this work for additional information |
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| # to you under the Apache License, Version 2.0 (the |
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| # http://www.apache.org/licenses/LICENSE-2.0 |
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| #------------------------------------------------------------- |
| |
| /* |
| * This Example trains a feed forward neural network with one input layer, two hidden affine layers (200 neurons) with |
| * leaky relu activations and one affine output layer with a softmax activation |
| * |
| * The reason for this example is to test the performance differences between single threaded, with a parameter server |
| * for parallelization and finally federated across multiple SystemDS instances |
| * |
| * Inputs: |
| * - train: The file containing the training data |
| * - test: the file containing the test data |
| * |
| * The MNIST Data can be downloaded as follows: |
| * mkdir -p data/mnist/ |
| * cd data/mnist/ |
| * curl -O https://pjreddie.com/media/files/mnist_train.csv |
| * curl -O https://pjreddie.com/media/files/mnist_test.csv |
| * |
| * Sample Invocation |
| * |
| * systemds "<path to systemds repo>/systemds/scripts/nn/examples/Example-MNIST_2NN_Leaky_ReLu_Softmax.dml" |
| * -nvargs train=<path to data>/mnist_data/mnist_train.csv test=<path to data>/mnist_data/mnist_test.csv |
| */ |
| |
| source("nn/examples/mnist_2NN.dml") as mnist_2NN |
| |
| # Read training data |
| data = read($train, format="csv") |
| n = nrow(data) |
| |
| # Extract images and labels |
| images = data[,2:ncol(data)] |
| labels = data[,1] |
| |
| # Scale images to [0,1], and one-hot encode the labels |
| images = images / 255.0 |
| labels = table(seq(1, n), labels+1, n, 10) |
| |
| # Split into training (55,000 examples) and validation (5,000 examples) |
| X = images[5001:nrow(images),] |
| X_val = images[1:5000,] |
| y = labels[5001:nrow(images),] |
| y_val = labels[1:5000,] |
| |
| # Train |
| epochs = 1 |
| [W_1, b_1, W_2, b_2, W_3, b_3] = mnist_2NN::train(X, y, X_val, y_val, epochs) |
| |
| # Read test data |
| data = read($test, format="csv") |
| n = nrow(data) |
| |
| # Extract images and labels |
| X_test = data[,2:ncol(data)] |
| y_test = data[,1] |
| |
| # Scale images to [0,1], and one-hot encode the labels |
| X_test = X_test / 255.0 |
| y_test = table(seq(1, n), y_test+1, n, 10) |
| |
| # Eval on test set |
| probs = mnist_2NN::predict(X_test, W_1, b_1, W_2, b_2, W_3, b_3) |
| [loss, accuracy] = mnist_2NN::eval(probs, y_test) |
| |
| print("Test Accuracy: " + accuracy) |