mnist_softmax-train.dml

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#-------------------------------------------------------------

# MNIST Softmax - Train
#
# This script trains a softmax classifier on images of handwritten
# digits.
#
# Inputs:
#  - train: File containing labeled MNIST training images.
#     The format is "label, pixel_1, pixel_2, ..., pixel_n".
#  - test: File containing labeled MNIST test images.
#     The format is "label, pixel_1, pixel_2, ..., pixel_n".
#  - out_dir: Directory to store weights and bias matrices of
#     trained model, as well as final test accuracy.
#  - fmt: [DEFAULT: "csv"] File format of `train` and `test` data.
#     Options include: "csv", "mm", "text", and "binary".
#
# Outputs:
#  - W: File containing the trained weights of the model.
#  - b: File containing the trained biases of the model.
#  - accuracy: File containing the final accuracy on the test data.
#
# Data:
# The MNIST dataset contains labeled images of handwritten digits,
# where each example is a 28x28 pixel image of grayscale values in
# the range [0,255] stretched out as 784 pixels, and each label is
# one of 10 possible digits in [0,9].
#
# Sample Invocation (running from wihtin the `examples` folder):
# 1. Download data (60,000 training examples, and 10,000 test examples)
#   ```
#   nn/examples/get_mnist_data.sh
#   ```
#
# 2. Execute using Spark
#   ```
#   spark-submit --master local[*] --driver-memory 10G
#   --conf spark.driver.maxResultSize=0 --conf spark.rpc.message.maxSize=128
#   $SYSTEMDS_ROOT/target/SystemDS.jar -f nn/examples/mnist_softmax-train.dml
#   -nvargs train=nn/examples/data/mnist/mnist_train.csv test=nn/examples/data/mnist/mnist_test.csv
#   epochs=1 out_dir=nn/examples/model/mnist_softmax
#   ```
#
source("nn/examples/mnist_softmax.dml") as mnist_softmax

# Read training data
fmt = ifdef($fmt, "csv")
train = read($train, format=fmt)
test = read($test, format=fmt)
epochs = ifdef($epochs, 1)
out_dir = ifdef($out_dir, ".")

# Extract images and labels
images = train[,2:ncol(train)]
labels = train[,1]
X_test = test[,2:ncol(test)]
Y_test = test[,1]

# Scale images to [0,1], and one-hot encode the labels
n = nrow(train)
n_test = nrow(test)
classes = 10
images = images / 255.0
labels = table(seq(1, n), labels+1, n, classes)
X_test = X_test / 255.0
Y_test = table(seq(1, n_test), Y_test+1, n_test, classes)

# 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
[W, b] = mnist_softmax::train(X, Y, X_val, Y_val, epochs)

# Write model out
write(W, out_dir+"/W")
write(b, out_dir+"/b")

# Eval on test set
probs = mnist_softmax::predict(X_test, W, b)
[loss, accuracy] = mnist_softmax::eval(probs, Y_test)

# Output results
print("Test Accuracy: " + accuracy)
write(accuracy, out_dir+"/accuracy")

print("")
print("")