scripts/nn/examples/mnist_softmax-predict.dml

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# MNIST Softmax - Predict
#
# This script computes the class probability predictions of a
# trained softmax classifier on images of handwritten digits.
#
# Inputs:
#  - X: File containing training images.
#     The format is "pixel_1, pixel_2, ..., pixel_n".
#  - model_dir: Directory containing the trained weights and biases
#     of the model.
#  - out_dir: Directory to store class probability predictions for
#     each image.
#  - fmt: [DEFAULT: "csv"] File format of `X` and output predictions.
#     Options include: "csv", "mm", "text", and "binary".
#
# Outputs:
#  - probs: File containing class probability predictions for each
#     image.
#
# Data:
# The X file should contain 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.
#
# Sample Invocation:
# 1. Download images.
#
#   For example, save images to `nn/examples/data/mnist/images.csv`.
#
# 2. Execute using Spark
#   ```
#   spark-submit --master local[*] --driver-memory 5G
#   --conf spark.driver.maxResultSize=0 --conf spark.rpc.message.maxSize=128
#   $SYSTEMDS_ROOT/target/SystemDS.jar -f nn/examples/mnist_softmax-predict.dml
#   -nvargs X=nn/examples/data/mnist/images.csv
#   model_dir=nn/examples/model/mnist_softmax out_dir=nn/examples/data/mnist
#
source("nn/examples/mnist_softmax.dml") as mnist_softmax

# Read training data
fmt = ifdef($fmt, "csv")
X = read($X, format=fmt)

# Scale images to [0,1], and one-hot encode the labels
X = X / 255.0

# Read model coefficients
W = read($model_dir+"/W")
b = read($model_dir+"/b")

# Predict classes
probs = mnist_softmax::predict(X, W, b)

# Output results
write(probs, $out_dir+"/probs."+fmt, format=fmt)