| #------------------------------------------------------------- |
| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| # |
| #------------------------------------------------------------- |
| |
| # 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) |
| |