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Posted to commits@systemml.apache.org by du...@apache.org on 2017/04/26 21:42:28 UTC
[02/11] incubator-systemml git commit: [SYSTEMML-1524] Move
`examples` into `nn`
[SYSTEMML-1524] Move `examples` into `nn`
Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/1f5cf697
Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/1f5cf697
Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/1f5cf697
Branch: refs/heads/master
Commit: 1f5cf697c49313861a3bdbcc634f7a56daabdc16
Parents: aa2211a
Author: Mike Dusenberry <mw...@us.ibm.com>
Authored: Wed Apr 26 14:40:40 2017 -0700
Committer: Mike Dusenberry <mw...@us.ibm.com>
Committed: Wed Apr 26 14:40:40 2017 -0700
----------------------------------------------------------------------
scripts/staging/SystemML-NN/README.md | 2 +-
.../examples/Example - MNIST LeNet.ipynb | 198 -----------
.../Example - MNIST Softmax Classifier.ipynb | 185 -----------
scripts/staging/SystemML-NN/examples/README.md | 75 -----
.../SystemML-NN/examples/get_mnist_data.sh | 28 --
.../examples/mnist_lenet-predict.dml | 87 -----
.../SystemML-NN/examples/mnist_lenet-train.dml | 123 -------
.../SystemML-NN/examples/mnist_lenet.dml | 331 -------------------
.../examples/mnist_softmax-predict.dml | 74 -----
.../examples/mnist_softmax-train.dml | 108 ------
.../SystemML-NN/examples/mnist_softmax.dml | 177 ----------
scripts/staging/SystemML-NN/examples/nn | 1 -
.../nn/examples/Example - MNIST LeNet.ipynb | 189 +++++++++++
.../Example - MNIST Softmax Classifier.ipynb | 179 ++++++++++
.../staging/SystemML-NN/nn/examples/README.md | 74 +++++
.../SystemML-NN/nn/examples/get_mnist_data.sh | 28 ++
.../nn/examples/mnist_lenet-predict.dml | 91 +++++
.../nn/examples/mnist_lenet-train.dml | 123 +++++++
.../SystemML-NN/nn/examples/mnist_lenet.dml | 331 +++++++++++++++++++
.../nn/examples/mnist_softmax-predict.dml | 77 +++++
.../nn/examples/mnist_softmax-train.dml | 110 ++++++
.../SystemML-NN/nn/examples/mnist_softmax.dml | 178 ++++++++++
22 files changed, 1381 insertions(+), 1388 deletions(-)
----------------------------------------------------------------------
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/README.md
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/README.md b/scripts/staging/SystemML-NN/README.md
index 3943765..b80f2c6 100644
--- a/scripts/staging/SystemML-NN/README.md
+++ b/scripts/staging/SystemML-NN/README.md
@@ -22,7 +22,7 @@ limitations under the License.
### A deep learning library for [Apache SystemML](https://github.com/apache/incubator-systemml).
## Examples:
-#### Please see the [`examples`](examples) folder for more detailed examples, or view the following two quick examples.
+#### Please see the [`examples`](nn/examples) folder for more detailed examples, or view the following two quick examples.
### Neural net for regression with vanilla SGD:
```python
# Imports
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/examples/Example - MNIST LeNet.ipynb
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/examples/Example - MNIST LeNet.ipynb b/scripts/staging/SystemML-NN/examples/Example - MNIST LeNet.ipynb
deleted file mode 100644
index 3ad210e..0000000
--- a/scripts/staging/SystemML-NN/examples/Example - MNIST LeNet.ipynb
+++ /dev/null
@@ -1,198 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Quick Setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "# Create a SystemML MLContext object\n",
- "from systemml import MLContext, dml\n",
- "ml = MLContext(sc)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Download Data - MNIST"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "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]. Here, we download 60,000 training examples, and 10,000 test examples, where the format is \"label, pixel_1, pixel_2, ..., pixel_n\"."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "%%sh\n",
- "mkdir -p data/mnist/\n",
- "cd data/mnist/\n",
- "curl -O http://pjreddie.com/media/files/mnist_train.csv\n",
- "curl -O http://pjreddie.com/media/files/mnist_test.csv"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## SystemML \"LeNet\" Neural Network"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1. Train"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "script_string = \"\"\"\n",
- "source(\"mnist_lenet.dml\") as mnist_lenet\n",
- "\n",
- "# Read training data\n",
- "data = read($data, format=\"csv\")\n",
- "n = nrow(data)\n",
- "\n",
- "# Extract images and labels\n",
- "images = data[,2:ncol(data)]\n",
- "labels = data[,1]\n",
- "\n",
- "# Scale images to [-1,1], and one-hot encode the labels\n",
- "images = (images / 255.0) * 2 - 1\n",
- "labels = table(seq(1, n), labels+1, n, 10)\n",
- "\n",
- "# Split into training (55,000 examples) and validation (5,000 examples)\n",
- "X = images[5001:nrow(images),]\n",
- "X_val = images[1:5000,]\n",
- "y = labels[5001:nrow(images),]\n",
- "y_val = labels[1:5000,]\n",
- "\n",
- "# Train\n",
- "[W1, b1, W2, b2, W3, b3, W4, b4] = mnist_lenet::train(X, y, X_val, y_val, C, Hin, Win)\n",
- "\"\"\"\n",
- "script = (dml(script_string).input(\"$data\", \"data/mnist/mnist_train.csv\")\n",
- " .input(C=1, Hin=28, Win=28)\n",
- " .output(\"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\", \"W4\", \"b4\"))\n",
- "W1, b1, W2, b2, W3, b3, W4, b4 = (ml.execute(script)\n",
- " .get(\"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\", \"W4\", \"b4\"))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2. Compute Test Accuracy"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "script_string = \"\"\"\n",
- "source(\"mnist_lenet.dml\") as mnist_lenet\n",
- "\n",
- "# Read test data\n",
- "data = read($data, format=\"csv\")\n",
- "n = nrow(data)\n",
- "\n",
- "# Extract images and labels\n",
- "X_test = data[,2:ncol(data)]\n",
- "y_test = data[,1]\n",
- "\n",
- "# Scale images to [-1,1], and one-hot encode the labels\n",
- "X_test = (X_test / 255.0) * 2 - 1\n",
- "y_test = table(seq(1, n), y_test+1, n, 10)\n",
- "\n",
- "# Eval on test set\n",
- "probs = mnist_lenet::predict(X_test, C, Hin, Win, W1, b1, W2, b2, W3, b3, W4, b4)\n",
- "[loss, accuracy] = mnist_lenet::eval(probs, y_test)\n",
- "\n",
- "print(\"Test Accuracy: \" + accuracy)\n",
- "\"\"\"\n",
- "script = dml(script_string).input(**{\"$data\": \"data/mnist/mnist_train.csv\",\n",
- " \"C\": 1, \"Hin\": 28, \"Win\": 28,\n",
- " \"W1\": W1, \"b1\": b1,\n",
- " \"W2\": W2, \"b2\": b2,\n",
- " \"W3\": W3, \"b3\": b3,\n",
- " \"W4\": W4, \"b4\": b4})\n",
- "ml.execute(script)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3. Extract Model Into Spark DataFrames For Future Use"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "W1_df = W1.toDF()\n",
- "b1_df = b1.toDF()\n",
- "W2_df = W2.toDF()\n",
- "b2_df = b2.toDF()\n",
- "W3_df = W3.toDF()\n",
- "b3_df = b3.toDF()\n",
- "W4_df = W4.toDF()\n",
- "b4_df = b4.toDF()\n",
- "W1_df, b1_df, W2_df, b2_df, W3_df, b3_df, W4_df, b4_df"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.5.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/examples/Example - MNIST Softmax Classifier.ipynb
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/examples/Example - MNIST Softmax Classifier.ipynb b/scripts/staging/SystemML-NN/examples/Example - MNIST Softmax Classifier.ipynb
deleted file mode 100644
index 7f2c2f0..0000000
--- a/scripts/staging/SystemML-NN/examples/Example - MNIST Softmax Classifier.ipynb
+++ /dev/null
@@ -1,185 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Quick Setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "# Create a SystemML MLContext object\n",
- "from systemml import MLContext, dml\n",
- "ml = MLContext(sc)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Download Data - MNIST"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "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]. Here, we download 60,000 training examples, and 10,000 test examples, where the format is \"label, pixel_1, pixel_2, ..., pixel_n\"."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": [
- "%%sh\n",
- "mkdir -p data/mnist/\n",
- "cd data/mnist/\n",
- "curl -O http://pjreddie.com/media/files/mnist_train.csv\n",
- "curl -O http://pjreddie.com/media/files/mnist_test.csv"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## SystemML Softmax Model"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1. Train"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "training = \"\"\"\n",
- "source(\"mnist_softmax.dml\") as mnist_softmax\n",
- "\n",
- "# Read training data\n",
- "data = read($data, format=\"csv\")\n",
- "n = nrow(data)\n",
- "\n",
- "# Extract images and labels\n",
- "images = data[,2:ncol(data)]\n",
- "labels = data[,1]\n",
- "\n",
- "# Scale images to [0,1], and one-hot encode the labels\n",
- "images = images / 255.0\n",
- "labels = table(seq(1, n), labels+1, n, 10)\n",
- "\n",
- "# Split into training (55,000 examples) and validation (5,000 examples)\n",
- "X = images[5001:nrow(images),]\n",
- "X_val = images[1:5000,]\n",
- "y = labels[5001:nrow(images),]\n",
- "y_val = labels[1:5000,]\n",
- "\n",
- "# Train\n",
- "[W, b] = mnist_softmax::train(X, y, X_val, y_val)\n",
- "\"\"\"\n",
- "script = dml(training).input(\"$data\", \"data/mnist/mnist_train.csv\").output(\"W\", \"b\")\n",
- "W, b = ml.execute(script).get(\"W\", \"b\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2. Compute Test Accuracy"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "testing = \"\"\"\n",
- "source(\"mnist_softmax.dml\") as mnist_softmax\n",
- "\n",
- "# Read test data\n",
- "data = read($data, format=\"csv\")\n",
- "n = nrow(data)\n",
- "\n",
- "# Extract images and labels\n",
- "X_test = data[,2:ncol(data)]\n",
- "y_test = data[,1]\n",
- "\n",
- "# Scale images to [0,1], and one-hot encode the labels\n",
- "X_test = X_test / 255.0\n",
- "y_test = table(seq(1, n), y_test+1, n, 10)\n",
- "\n",
- "# Eval on test set\n",
- "probs = mnist_softmax::predict(X_test, W, b)\n",
- "[loss, accuracy] = mnist_softmax::eval(probs, y_test)\n",
- "\n",
- "print(\"Test Accuracy: \" + accuracy)\n",
- "\"\"\"\n",
- "script = dml(testing).input(\"$data\", \"data/mnist/mnist_test.csv\", W=W, b=b)\n",
- "ml.execute(script)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3. Extract Model Into Spark DataFrames For Future Use"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "W_df = W.toDF()\n",
- "b_df = b.toDF()\n",
- "W_df, b_df"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.5.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/examples/README.md
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/examples/README.md b/scripts/staging/SystemML-NN/examples/README.md
deleted file mode 100644
index ffacea2..0000000
--- a/scripts/staging/SystemML-NN/examples/README.md
+++ /dev/null
@@ -1,75 +0,0 @@
-<!--
-{% comment %}
-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.
-{% endcomment %}
--->
-
-# SystemML-NN Examples
-
-#### This folder contains scripts and PySpark Jupyter notebooks serving as examples of using the *SystemML-NN* (`nn`) deep learning library.
-
----
-
-# Examples
-### MNIST Softmax Classifier
-
-* This example trains a softmax classifier, which is essentially a multi-class logistic regression model, on the MNIST data. The model will be trained on the *training* images, validated on the *validation* images, and tested for final performance metrics on the *test* images.
-* Notebook: `Example - MNIST Softmax Classifier.ipynb`.
-* DML Functions: `mnist_softmax.dml`
-* Training script: `mnist_softmax-train.dml`
-* Prediction script: `mnist_softmax-predict.dml`
-
-### MNIST "LeNet" Neural Net
-
-* This example trains a neural network on the MNIST data using a ["LeNet" architecture](http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf). The model will be trained on the *training* images, validated on the *validation* images, and tested for final performance metrics on the *test* images.
-* Notebook: `Example - MNIST LeNet.ipynb`.
-* DML Functions: `mnist_lenet.dml`
-* Training script: `mnist_lenet-train.dml`
-* Prediction script: `mnist_lenet-predict.dml`
-
----
-
-# Setup
-## Code
-* To run the examples, please first download and unzip the project via GitHub using the "Clone or download" button on the [homepage of the project](https://github.com/dusenberrymw/systemml-nn), *or* via the following commands:
-
- ```
- curl -LO https://github.com/dusenberrymw/systemml-nn/archive/master.zip
- unzip master.zip
- ```
-
-* Then, move into the `examples` folder via:
- ```
- cd systemml-nn-master/examples/
- ```
-
-## Data
-* These examples use the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, which contains labeled 28x28 pixel images of handwritten digits in the range of 0-9. There are 60,000 training images, and 10,000 testing images. Of the 60,000 training images, 5,000 will be used as validation images.
-* **Download**:
- * **Notebooks**: The data will be automatically downloaded as a step in either of the example notebooks.
- * **Training scripts**: Please run `get_mnist_data.sh` to download the data separately.
-
-## Execution
-* These examples contain scripts written in SystemML's R-like language (`*.dml`), as well as PySpark Jupyter notebooks (`*.ipynb`). The scripts contain the math for the algorithms, enclosed in functions, and the notebooks serve as full, end-to-end examples of reading in data, training models using the functions within the scripts, and evaluating final performance.
-* **Notebooks**: To run the notebook examples, please install the SystemML Python package with `pip install systemml`, and then startup Jupyter in the following manner from this directory (or for more information, please see [this great blog post](http://spark.tc/0-to-life-changing-application-with-apache-systemml/)):
-
- ```
- PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark --master local[*] --driver-memory 3G --driver-class-path SystemML.jar --jars SystemML.jar
- ```
-
- Note that all printed output, such as training statistics, from the SystemML scripts will be sent to the terminal in which Jupyter was started (for now...).
-
-* **Scripts**: To run the scripts from the command line using `spark-submit`, please see the comments located at the top of the `-train` and `-predict` scripts.
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/examples/get_mnist_data.sh
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/examples/get_mnist_data.sh b/scripts/staging/SystemML-NN/examples/get_mnist_data.sh
deleted file mode 100755
index deb0c40..0000000
--- a/scripts/staging/SystemML-NN/examples/get_mnist_data.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/usr/bin/env bash
-#-------------------------------------------------------------
-#
-# 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.
-#
-#-------------------------------------------------------------
-
-DIR="$(cd "$(dirname "$0")" && pwd)"
-mkdir -p $DIR/data/mnist/
-cd $DIR/data/mnist/
-curl -O https://pjreddie.com/media/files/mnist_train.csv
-curl -O https://pjreddie.com/media/files/mnist_test.csv
-
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/examples/mnist_lenet-predict.dml
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/examples/mnist_lenet-predict.dml b/scripts/staging/SystemML-NN/examples/mnist_lenet-predict.dml
deleted file mode 100644
index 759418d..0000000
--- a/scripts/staging/SystemML-NN/examples/mnist_lenet-predict.dml
+++ /dev/null
@@ -1,87 +0,0 @@
-#-------------------------------------------------------------
-#
-# 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 LeNet - Predict
-#
-# This script computes the class probability predictions of a
-# trained convolutional net using the "LeNet" architecture on
-# images of handwritten digits.
-#
-# Inputs:
-# - X: File containing training images.
-# The format is "pixel_1, pixel_2, ..., pixel_n".
-# - C: Number of color chanels in the images.
-# - Hin: Input image height.
-# - Win: Input image width.
-# - 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:
-# Execute using Spark
-# ```
-# spark-submit --master local[*] --driver-memory 5G
-# --conf spark.driver.maxResultSize=0 --conf spark.akka.frameSize=128
-# $SYSTEMML_HOME/target/SystemML.jar -f mnist_lenet-predict.dml
-# -nvargs X=data/mnist/images.csv C=1 Hin=28 Win=28
-# model_dir=model/mnist_lenet out_dir=data/mnist
-# ```
-#
-source("mnist_lenet.dml") as mnist_lenet
-
-# Read training data
-fmt = ifdef($fmt, "csv")
-X = read($X, format=fmt)
-C = $C
-Hin = $Hin
-Win = $Win
-
-# Scale images to [-1,1]
-X = (X / 255.0) * 2 - 1
-
-# Read model coefficients
-W1 = read($model_dir+"/W1")
-b1 = read($model_dir+"/b1")
-W2 = read($model_dir+"/W2")
-b2 = read($model_dir+"/b2")
-W3 = read($model_dir+"/W3")
-b3 = read($model_dir+"/b3")
-W4 = read($model_dir+"/W4")
-b4 = read($model_dir+"/b4")
-
-# Predict classes
-probs = mnist_lenet::predict(X, C, Hin, Win, W1, b1, W2, b2, W3, b3, W4, b4)
-
-# Output results
-write(probs, $out_dir+"/probs."+fmt, format=fmt)
-
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/examples/mnist_lenet-train.dml
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/examples/mnist_lenet-train.dml b/scripts/staging/SystemML-NN/examples/mnist_lenet-train.dml
deleted file mode 100644
index eafb34c..0000000
--- a/scripts/staging/SystemML-NN/examples/mnist_lenet-train.dml
+++ /dev/null
@@ -1,123 +0,0 @@
-#-------------------------------------------------------------
-#
-# 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 LeNet - Train
-#
-# This script trains a convolutional net using the "LeNet" architecture
-# 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".
-# - C: Number of color chanels in the images.
-# - Hin: Input image height.
-# - Win: Input image width.
-# - epochs: [DEFAULT: 10] Total number of full training loops over
-# the full data set.
-# - out_dir: [DEFAULT: "."] 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:
-# - W1, W2, W3, W4: Files containing the trained weights of the model.
-# - b1, b2, b3, b4: Files 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)
-# ```
-# get_mnist_data.sh
-# ```
-#
-# 2. Execute using Spark
-# ```
-# spark-submit --master local[*] --driver-memory 10G
-# --conf spark.driver.maxResultSize=0 --conf spark.akka.frameSize=128
-# $SYSTEMML_HOME/target/SystemML.jar -f mnist_lenet-train.dml
-# -nvargs train=data/mnist/mnist_train.csv test=data/mnist/mnist_test.csv
-# C=1 Hin=28 Win=28 epochs=10 out_dir=model/mnist_lenet
-# ```
-#
-source("mnist_lenet.dml") as mnist_lenet
-
-# Read training data & settings
-fmt = ifdef($fmt, "csv")
-train = read($train, format=fmt)
-test = read($test, format=fmt)
-C = $C
-Hin = $Hin
-Win = $Win
-epochs = ifdef($epochs, 10)
-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 [-1,1], and one-hot encode the labels
-n = nrow(train)
-n_test = nrow(test)
-images = (images / 255.0) * 2 - 1
-labels = table(seq(1, n), labels+1, n, 10)
-X_test = (X_test / 255.0) * 2 - 1
-y_test = table(seq(1, n_test), y_test+1, n_test, 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
-[W1, b1, W2, b2, W3, b3, W4, b4] = mnist_lenet::train(X, y, X_val, y_val, C, Hin, Win, epochs)
-
-# Write model out
-write(W1, out_dir+"/W1")
-write(b1, out_dir+"/b1")
-write(W2, out_dir+"/W2")
-write(b2, out_dir+"/b2")
-write(W3, out_dir+"/W3")
-write(b3, out_dir+"/b3")
-write(W4, out_dir+"/W4")
-write(b4, out_dir+"/b4")
-
-# Eval on test set
-probs = mnist_lenet::predict(X_test, C, Hin, Win, W1, b1, W2, b2, W3, b3, W4, b4)
-[loss, accuracy] = mnist_lenet::eval(probs, y_test)
-
-# Output results
-print("Test Accuracy: " + accuracy)
-write(accuracy, out_dir+"/accuracy")
-
-print("")
-print("")
-
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/examples/mnist_lenet.dml
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/examples/mnist_lenet.dml b/scripts/staging/SystemML-NN/examples/mnist_lenet.dml
deleted file mode 100644
index e5755c4..0000000
--- a/scripts/staging/SystemML-NN/examples/mnist_lenet.dml
+++ /dev/null
@@ -1,331 +0,0 @@
-#-------------------------------------------------------------
-#
-# 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 LeNet Example
- */
-# Imports
-source("nn/layers/affine.dml") as affine
-source("nn/layers/conv2d_builtin.dml") as conv2d
-source("nn/layers/cross_entropy_loss.dml") as cross_entropy_loss
-source("nn/layers/dropout.dml") as dropout
-source("nn/layers/l2_reg.dml") as l2_reg
-source("nn/layers/max_pool2d_builtin.dml") as max_pool2d
-source("nn/layers/relu.dml") as relu
-source("nn/layers/softmax.dml") as softmax
-source("nn/optim/sgd_nesterov.dml") as sgd_nesterov
-
-train = function(matrix[double] X, matrix[double] y,
- matrix[double] X_val, matrix[double] y_val,
- int C, int Hin, int Win, int epochs)
- return (matrix[double] W1, matrix[double] b1,
- matrix[double] W2, matrix[double] b2,
- matrix[double] W3, matrix[double] b3,
- matrix[double] W4, matrix[double] b4) {
- /*
- * Trains a convolutional net using the "LeNet" architecture.
- *
- * The input matrix, X, has N examples, each represented as a 3D
- * volume unrolled into a single vector. The targets, y, have K
- * classes, and are one-hot encoded.
- *
- * Inputs:
- * - X: Input data matrix, of shape (N, C*Hin*Win).
- * - y: Target matrix, of shape (N, K).
- * - X_val: Input validation data matrix, of shape (N, C*Hin*Win).
- * - y_val: Target validation matrix, of shape (N, K).
- * - C: Number of input channels (dimensionality of input depth).
- * - Hin: Input height.
- * - Win: Input width.
- * - epochs: Total number of full training loops over the full data set.
- *
- * Outputs:
- * - W1: 1st layer weights (parameters) matrix, of shape (F1, C*Hf*Wf).
- * - b1: 1st layer biases vector, of shape (F1, 1).
- * - W2: 2nd layer weights (parameters) matrix, of shape (F2, F1*Hf*Wf).
- * - b2: 2nd layer biases vector, of shape (F2, 1).
- * - W3: 3rd layer weights (parameters) matrix, of shape (F2*(Hin/4)*(Win/4), N3).
- * - b3: 3rd layer biases vector, of shape (1, N3).
- * - W4: 4th layer weights (parameters) matrix, of shape (N3, K).
- * - b4: 4th layer biases vector, of shape (1, K).
- */
- N = nrow(X)
- K = ncol(y)
-
- # Create network:
- # conv1 -> relu1 -> pool1 -> conv2 -> relu2 -> pool2 -> affine3 -> relu3 -> affine4 -> softmax
- Hf = 5 # filter height
- Wf = 5 # filter width
- stride = 1
- pad = 2 # For same dimensions, (Hf - stride) / 2
-
- F1 = 32 # num conv filters in conv1
- F2 = 64 # num conv filters in conv2
- N3 = 512 # num nodes in affine3
- # Note: affine4 has K nodes, which is equal to the number of target dimensions (num classes)
-
- [W1, b1] = conv2d::init(F1, C, Hf, Wf) # inputs: (N, C*Hin*Win)
- [W2, b2] = conv2d::init(F2, F1, Hf, Wf) # inputs: (N, F1*(Hin/2)*(Win/2))
- [W3, b3] = affine::init(F2*(Hin/2/2)*(Win/2/2), N3) # inputs: (N, F2*(Hin/2/2)*(Win/2/2))
- [W4, b4] = affine::init(N3, K) # inputs: (N, N3)
- W4 = W4 / sqrt(2) # different initialization, since being fed into softmax, instead of relu
-
- # Initialize SGD w/ Nesterov momentum optimizer
- lr = 0.01 # learning rate
- mu = 0.9 #0.5 # momentum
- decay = 0.95 # learning rate decay constant
- vW1 = sgd_nesterov::init(W1); vb1 = sgd_nesterov::init(b1)
- vW2 = sgd_nesterov::init(W2); vb2 = sgd_nesterov::init(b2)
- vW3 = sgd_nesterov::init(W3); vb3 = sgd_nesterov::init(b3)
- vW4 = sgd_nesterov::init(W4); vb4 = sgd_nesterov::init(b4)
-
- # Regularization
- lambda = 5e-04
-
- # Optimize
- print("Starting optimization")
- batch_size = 64
- iters = ceil(N / batch_size)
- for (e in 1:epochs) {
- for(i in 1:iters) {
- # Get next batch
- beg = ((i-1) * batch_size) %% N + 1
- end = min(N, beg + batch_size - 1)
- X_batch = X[beg:end,]
- y_batch = y[beg:end,]
-
- # Compute forward pass
- ## layer 1: conv1 -> relu1 -> pool1
- [outc1, Houtc1, Woutc1] = conv2d::forward(X_batch, W1, b1, C, Hin, Win, Hf, Wf, stride, stride,
- pad, pad)
- outr1 = relu::forward(outc1)
- [outp1, Houtp1, Woutp1] = max_pool2d::forward(outr1, F1, Houtc1, Woutc1, Hf=2, Wf=2,
- strideh=2, stridew=2, pad=0, pad=0)
- ## layer 2: conv2 -> relu2 -> pool2
- [outc2, Houtc2, Woutc2] = conv2d::forward(outp1, W2, b2, F1, Houtp1, Woutp1, Hf, Wf,
- stride, stride, pad, pad)
- outr2 = relu::forward(outc2)
- [outp2, Houtp2, Woutp2] = max_pool2d::forward(outr2, F2, Houtc2, Woutc2, Hf=2, Wf=2,
- strideh=2, stridew=2, pad=0, pad=0)
- ## layer 3: affine3 -> relu3 -> dropout
- outa3 = affine::forward(outp2, W3, b3)
- outr3 = relu::forward(outa3)
- [outd3, maskd3] = dropout::forward(outr3, 0.5, -1)
- ## layer 4: affine4 -> softmax
- outa4 = affine::forward(outd3, W4, b4)
- probs = softmax::forward(outa4)
-
- # Compute loss & accuracy for training & validation data every 100 iterations.
- if (i %% 100 == 0) {
- # Compute training loss & accuracy
- loss_data = cross_entropy_loss::forward(probs, y_batch)
- loss_reg_W1 = l2_reg::forward(W1, lambda)
- loss_reg_W2 = l2_reg::forward(W2, lambda)
- loss_reg_W3 = l2_reg::forward(W3, lambda)
- loss_reg_W4 = l2_reg::forward(W4, lambda)
- loss = loss_data + loss_reg_W1 + loss_reg_W2 + loss_reg_W3 + loss_reg_W4
- accuracy = mean(rowIndexMax(probs) == rowIndexMax(y_batch))
-
- # Compute validation loss & accuracy
- probs_val = predict(X_val, C, Hin, Win, W1, b1, W2, b2, W3, b3, W4, b4)
- loss_val = cross_entropy_loss::forward(probs_val, y_val)
- accuracy_val = mean(rowIndexMax(probs_val) == rowIndexMax(y_val))
-
- # Output results
- print("Epoch: " + e + ", Iter: " + i + ", Train Loss: " + loss + ", Train Accuracy: "
- + accuracy + ", Val Loss: " + loss_val + ", Val Accuracy: " + accuracy_val)
- }
-
- # Compute data backward pass
- ## loss:
- dprobs = cross_entropy_loss::backward(probs, y_batch)
- ## layer 4: affine4 -> softmax
- douta4 = softmax::backward(dprobs, outa4)
- [doutd3, dW4, db4] = affine::backward(douta4, outr3, W4, b4)
- ## layer 3: affine3 -> relu3 -> dropout
- doutr3 = dropout::backward(doutd3, outr3, 0.5, maskd3)
- douta3 = relu::backward(doutr3, outa3)
- [doutp2, dW3, db3] = affine::backward(douta3, outp2, W3, b3)
- ## layer 2: conv2 -> relu2 -> pool2
- doutr2 = max_pool2d::backward(doutp2, Houtp2, Woutp2, outr2, F2, Houtc2, Woutc2, Hf=2, Wf=2,
- strideh=2, stridew=2, pad=0, pad=0)
- doutc2 = relu::backward(doutr2, outc2)
- [doutp1, dW2, db2] = conv2d::backward(doutc2, Houtc2, Woutc2, outp1, W2, b2, F1,
- Houtp1, Woutp1, Hf, Wf, stride, stride, pad, pad)
- ## layer 1: conv1 -> relu1 -> pool1
- doutr1 = max_pool2d::backward(doutp1, Houtp1, Woutp1, outr1, F1, Houtc1, Woutc1, Hf=2, Wf=2,
- strideh=2, stridew=2, pad=0, pad=0)
- doutc1 = relu::backward(doutr1, outc1)
- [dX_batch, dW1, db1] = conv2d::backward(doutc1, Houtc1, Woutc1, X_batch, W1, b1, C, Hin, Win,
- Hf, Wf, stride, stride, pad, pad)
-
- # Compute regularization backward pass
- dW1_reg = l2_reg::backward(W1, lambda)
- dW2_reg = l2_reg::backward(W2, lambda)
- dW3_reg = l2_reg::backward(W3, lambda)
- dW4_reg = l2_reg::backward(W4, lambda)
- dW1 = dW1 + dW1_reg
- dW2 = dW2 + dW2_reg
- dW3 = dW3 + dW3_reg
- dW4 = dW4 + dW4_reg
-
- # Optimize with SGD w/ Nesterov momentum
- [W1, vW1] = sgd_nesterov::update(W1, dW1, lr, mu, vW1)
- [b1, vb1] = sgd_nesterov::update(b1, db1, lr, mu, vb1)
- [W2, vW2] = sgd_nesterov::update(W2, dW2, lr, mu, vW2)
- [b2, vb2] = sgd_nesterov::update(b2, db2, lr, mu, vb2)
- [W3, vW3] = sgd_nesterov::update(W3, dW3, lr, mu, vW3)
- [b3, vb3] = sgd_nesterov::update(b3, db3, lr, mu, vb3)
- [W4, vW4] = sgd_nesterov::update(W4, dW4, lr, mu, vW4)
- [b4, vb4] = sgd_nesterov::update(b4, db4, lr, mu, vb4)
- }
- # Anneal momentum towards 0.999
- #mu = mu + (0.999 - mu)/(1+epochs-e)
- # Decay learning rate
- lr = lr * decay
- }
-}
-
-predict = function(matrix[double] X, int C, int Hin, int Win,
- matrix[double] W1, matrix[double] b1,
- matrix[double] W2, matrix[double] b2,
- matrix[double] W3, matrix[double] b3,
- matrix[double] W4, matrix[double] b4)
- return (matrix[double] probs) {
- /*
- * Computes the class probability predictions of a convolutional
- * net using the "LeNet" architecture.
- *
- * The input matrix, X, has N examples, each represented as a 3D
- * volume unrolled into a single vector.
- *
- * Inputs:
- * - X: Input data matrix, of shape (N, C*Hin*Win).
- * - C: Number of input channels (dimensionality of input depth).
- * - Hin: Input height.
- * - Win: Input width.
- * - W1: 1st layer weights (parameters) matrix, of shape (F1, C*Hf*Wf).
- * - b1: 1st layer biases vector, of shape (F1, 1).
- * - W2: 2nd layer weights (parameters) matrix, of shape (F2, F1*Hf*Wf).
- * - b2: 2nd layer biases vector, of shape (F2, 1).
- * - W3: 3rd layer weights (parameters) matrix, of shape (F2*(Hin/4)*(Win/4), N3).
- * - b3: 3rd layer biases vector, of shape (1, N3).
- * - W4: 4th layer weights (parameters) matrix, of shape (N3, K).
- * - b4: 4th layer biases vector, of shape (1, K).
- *
- * Outputs:
- * - probs: Class probabilities, of shape (N, K).
- */
- N = nrow(X)
-
- # Network:
- # conv1 -> relu1 -> pool1 -> conv2 -> relu2 -> pool2 -> affine3 -> relu3 -> affine4 -> softmax
- Hf = 5 # filter height
- Wf = 5 # filter width
- stride = 1
- pad = 2 # For same dimensions, (Hf - stride) / 2
-
- F1 = nrow(W1) # num conv filters in conv1
- F2 = nrow(W2) # num conv filters in conv2
- N3 = ncol(W3) # num nodes in affine3
- K = ncol(W4) # num nodes in affine4, equal to number of target dimensions (num classes)
-
- # Compute predictions over mini-batches
- probs = matrix(0, rows=N, cols=K)
- batch_size = 64
- iters = ceil(N / batch_size)
- for(i in 1:iters) {
- # Get next batch
- beg = ((i-1) * batch_size) %% N + 1
- end = min(N, beg + batch_size - 1)
- X_batch = X[beg:end,]
-
- # Compute forward pass
- ## layer 1: conv1 -> relu1 -> pool1
- [outc1, Houtc1, Woutc1] = conv2d::forward(X_batch, W1, b1, C, Hin, Win, Hf, Wf, stride, stride,
- pad, pad)
- outr1 = relu::forward(outc1)
- [outp1, Houtp1, Woutp1] = max_pool2d::forward(outr1, F1, Houtc1, Woutc1, Hf=2, Wf=2,
- strideh=2, stridew=2, pad=0, pad=0)
- ## layer 2: conv2 -> relu2 -> pool2
- [outc2, Houtc2, Woutc2] = conv2d::forward(outp1, W2, b2, F1, Houtp1, Woutp1, Hf, Wf,
- stride, stride, pad, pad)
- outr2 = relu::forward(outc2)
- [outp2, Houtp2, Woutp2] = max_pool2d::forward(outr2, F2, Houtc2, Woutc2, Hf=2, Wf=2,
- strideh=2, stridew=2, pad=0, pad=0)
- ## layer 3: affine3 -> relu3
- outa3 = affine::forward(outp2, W3, b3)
- outr3 = relu::forward(outa3)
- ## layer 4: affine4 -> softmax
- outa4 = affine::forward(outr3, W4, b4)
- probs_batch = softmax::forward(outa4)
-
- # Store predictions
- probs[beg:end,] = probs_batch
- }
-}
-
-eval = function(matrix[double] probs, matrix[double] y)
- return (double loss, double accuracy) {
- /*
- * Evaluates a convolutional net using the "LeNet" architecture.
- *
- * The probs matrix contains the class probability predictions
- * of K classes over N examples. The targets, y, have K classes,
- * and are one-hot encoded.
- *
- * Inputs:
- * - probs: Class probabilities, of shape (N, K).
- * - y: Target matrix, of shape (N, K).
- *
- * Outputs:
- * - loss: Scalar loss, of shape (1).
- * - accuracy: Scalar accuracy, of shape (1).
- */
- # Compute loss & accuracy
- loss = cross_entropy_loss::forward(probs, y)
- correct_pred = rowIndexMax(probs) == rowIndexMax(y)
- accuracy = mean(correct_pred)
-}
-
-generate_dummy_data = function()
- return (matrix[double] X, matrix[double] y, int C, int Hin, int Win) {
- /*
- * Generate a dummy dataset similar to the MNIST dataset.
- *
- * Outputs:
- * - X: Input data matrix, of shape (N, D).
- * - y: Target matrix, of shape (N, K).
- * - C: Number of input channels (dimensionality of input depth).
- * - Hin: Input height.
- * - Win: Input width.
- */
- # Generate dummy input data
- N = 1024 # num examples
- C = 1 # num input channels
- Hin = 28 # input height
- Win = 28 # input width
- K = 10 # num target classes
- X = rand(rows=N, cols=C*Hin*Win, pdf="normal")
- classes = round(rand(rows=N, cols=1, min=1, max=K, pdf="uniform"))
- y = table(seq(1, N), classes) # one-hot encoding
-}
-
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/examples/mnist_softmax-predict.dml
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/examples/mnist_softmax-predict.dml b/scripts/staging/SystemML-NN/examples/mnist_softmax-predict.dml
deleted file mode 100644
index 4101e27..0000000
--- a/scripts/staging/SystemML-NN/examples/mnist_softmax-predict.dml
+++ /dev/null
@@ -1,74 +0,0 @@
-#-------------------------------------------------------------
-#
-# 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:
-# Execute using Spark
-# ```
-# spark-submit --master local[*] --driver-memory 5G
-# --conf spark.driver.maxResultSize=0 --conf spark.akka.frameSize=128
-# $SYSTEMML_HOME/target/SystemML.jar -f mnist_softmax-predict.dml
-# -nvargs X=data/mnist/images.csv model_dir=model/mnist_softmax
-# out_dir=data/mnist
-# ```
-#
-source("mnist_softmax.dml") as mnist_softmax
-
-# Read training data
-fmt = ifdef($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)
-
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/examples/mnist_softmax-train.dml
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/examples/mnist_softmax-train.dml b/scripts/staging/SystemML-NN/examples/mnist_softmax-train.dml
deleted file mode 100644
index 2941dfa..0000000
--- a/scripts/staging/SystemML-NN/examples/mnist_softmax-train.dml
+++ /dev/null
@@ -1,108 +0,0 @@
-#-------------------------------------------------------------
-#
-# 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 - 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)
-# ```
-# get_mnist_data.sh
-# ```
-#
-# 2. Execute using Spark
-# ```
-# spark-submit --master local[*] --driver-memory 5G
-# --conf spark.driver.maxResultSize=0 --conf spark.akka.frameSize=128
-# $SYSTEMML_HOME/target/SystemML.jar -f mnist_softmax-train.dml
-# -nvargs train=data/mnist/mnist_train.csv test=data/mnist/mnist_test.csv
-# out_dir=model/mnist_softmax
-# ```
-#
-source("mnist_softmax.dml") as mnist_softmax
-
-# Read training data
-fmt = ifdef($fmt, "csv")
-train = read($train, format=fmt)
-test = read($test, format=fmt)
-
-# 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)
-
-# 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("")
-
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/examples/mnist_softmax.dml
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/examples/mnist_softmax.dml b/scripts/staging/SystemML-NN/examples/mnist_softmax.dml
deleted file mode 100644
index dc712f6..0000000
--- a/scripts/staging/SystemML-NN/examples/mnist_softmax.dml
+++ /dev/null
@@ -1,177 +0,0 @@
-#-------------------------------------------------------------
-#
-# 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 Example
- */
-# Imports
-source("nn/layers/affine.dml") as affine
-source("nn/layers/cross_entropy_loss.dml") as cross_entropy_loss
-source("nn/layers/softmax.dml") as softmax
-source("nn/optim/sgd_nesterov.dml") as sgd_nesterov
-
-train = function(matrix[double] X, matrix[double] y,
- matrix[double] X_val, matrix[double] y_val)
- return (matrix[double] W, matrix[double] b) {
- /*
- * Trains a softmax classifier.
- *
- * The input matrix, X, has N examples, each with D features.
- * The targets, y, have K classes, and are one-hot encoded.
- *
- * Inputs:
- * - X: Input data matrix, of shape (N, D).
- * - y: Target matrix, of shape (N, K).
- * - X_val: Input validation data matrix, of shape (N, C*Hin*Win).
- * - y_val: Target validation matrix, of shape (N, K).
- *
- * Outputs:
- * - W: Weights (parameters) matrix, of shape (D, M).
- * - b: Biases vector, of shape (1, M).
- */
- N = nrow(X) # num examples
- D = ncol(X) # num features
- K = ncol(y) # num classes
-
- # Create softmax classifier:
- # affine -> softmax
- [W, b] = affine::init(D, K)
- W = W / sqrt(2.0/(D)) * sqrt(1/(D))
-
- # Initialize SGD w/ Nesterov momentum optimizer
- lr = 0.2 # learning rate
- mu = 0 # momentum
- decay = 0.99 # learning rate decay constant
- vW = sgd_nesterov::init(W) # optimizer momentum state for W
- vb = sgd_nesterov::init(b) # optimizer momentum state for b
-
- # Optimize
- print("Starting optimization")
- batch_size = 50
- epochs = 1
- iters = 1000 #ceil(N / batch_size)
- for (e in 1:epochs) {
- for(i in 1:iters) {
- # Get next batch
- beg = ((i-1) * batch_size) %% N + 1
- end = min(N, beg + batch_size - 1)
- X_batch = X[beg:end,]
- y_batch = y[beg:end,]
-
- # Compute forward pass
- ## affine & softmax:
- out = affine::forward(X_batch, W, b)
- probs = softmax::forward(out)
-
- # Compute loss & accuracy for training & validation data
- loss = cross_entropy_loss::forward(probs, y_batch)
- accuracy = mean(rowIndexMax(probs) == rowIndexMax(y_batch))
- probs_val = predict(X_val, W, b)
- loss_val = cross_entropy_loss::forward(probs_val, y_val)
- accuracy_val = mean(rowIndexMax(probs_val) == rowIndexMax(y_val))
- print("Epoch: " + e + ", Iter: " + i + ", Train Loss: " + loss + ", Train Accuracy: " +
- accuracy + ", Val Loss: " + loss_val + ", Val Accuracy: " + accuracy_val)
-
- # Compute backward pass
- ## loss:
- dprobs = cross_entropy_loss::backward(probs, y_batch)
- ## affine & softmax:
- dout = softmax::backward(dprobs, out)
- [dX_batch, dW, db] = affine::backward(dout, X_batch, W, b)
-
- # Optimize with SGD w/ Nesterov momentum
- [W, vW] = sgd_nesterov::update(W, dW, lr, mu, vW)
- [b, vb] = sgd_nesterov::update(b, db, lr, mu, vb)
- }
- # Anneal momentum towards 0.999
- mu = mu + (0.999 - mu)/(1+epochs-e)
- # Decay learning rate
- lr = lr * decay
- }
-}
-
-predict = function(matrix[double] X, matrix[double] W, matrix[double] b)
- return (matrix[double] probs) {
- /*
- * Computes the class probability predictions of a softmax classifier.
- *
- * The input matrix, X, has N examples, each with D features.
- *
- * Inputs:
- * - X: Input data matrix, of shape (N, D).
- * - W: Weights (parameters) matrix, of shape (D, M).
- * - b: Biases vector, of shape (1, M).
- *
- * Outputs:
- * - probs: Class probabilities, of shape (N, K).
- */
- # Compute forward pass
- ## affine & softmax:
- out = affine::forward(X, W, b)
- probs = softmax::forward(out)
-}
-
-eval = function(matrix[double] probs, matrix[double] y)
- return (double loss, double accuracy) {
- /*
- * Evaluates a softmax classifier.
- *
- * The probs matrix contains the class probability predictions
- * of K classes over N examples. The targets, y, have K classes,
- * and are one-hot encoded.
- *
- * Inputs:
- * - probs: Class probabilities, of shape (N, K).
- * - y: Target matrix, of shape (N, K).
- *
- * Outputs:
- * - loss: Scalar loss, of shape (1).
- * - accuracy: Scalar accuracy, of shape (1).
- */
- # Compute loss & accuracy
- loss = cross_entropy_loss::forward(probs, y)
- correct_pred = rowIndexMax(probs) == rowIndexMax(y)
- accuracy = mean(correct_pred)
-}
-
-generate_dummy_data = function()
- return (matrix[double] X, matrix[double] y, int C, int Hin, int Win) {
- /*
- * Generate a dummy dataset similar to the MNIST dataset.
- *
- * Outputs:
- * - X: Input data matrix, of shape (N, D).
- * - y: Target matrix, of shape (N, K).
- * - C: Number of input channels (dimensionality of input depth).
- * - Hin: Input height.
- * - Win: Input width.
- */
- # Generate dummy input data
- N = 1024 # num examples
- C = 1 # num input channels
- Hin = 28 # input height
- Win = 28 # input width
- T = 10 # num targets
- X = rand(rows=N, cols=C*Hin*Win, pdf="normal")
- classes = round(rand(rows=N, cols=1, min=1, max=T, pdf="uniform"))
- y = table(seq(1, N), classes) # one-hot encoding
-}
-
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/examples/nn
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/examples/nn b/scripts/staging/SystemML-NN/examples/nn
deleted file mode 120000
index cfe2905..0000000
--- a/scripts/staging/SystemML-NN/examples/nn
+++ /dev/null
@@ -1 +0,0 @@
-../nn
\ No newline at end of file
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/nn/examples/Example - MNIST LeNet.ipynb
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/nn/examples/Example - MNIST LeNet.ipynb b/scripts/staging/SystemML-NN/nn/examples/Example - MNIST LeNet.ipynb
new file mode 100644
index 0000000..0423269
--- /dev/null
+++ b/scripts/staging/SystemML-NN/nn/examples/Example - MNIST LeNet.ipynb
@@ -0,0 +1,189 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Quick Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create a SystemML MLContext object\n",
+ "from systemml import MLContext, dml\n",
+ "ml = MLContext(sc)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Download Data - MNIST"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "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]. Here, we download 60,000 training examples, and 10,000 test examples, where the format is \"label, pixel_1, pixel_2, ..., pixel_n\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%sh\n",
+ "mkdir -p data/mnist/\n",
+ "cd data/mnist/\n",
+ "curl -O https://pjreddie.com/media/files/mnist_train.csv\n",
+ "curl -O https://pjreddie.com/media/files/mnist_test.csv"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## SystemML \"LeNet\" Neural Network"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1. Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "script_string = \"\"\"\n",
+ "source(\"nn/examples/mnist_lenet.dml\") as mnist_lenet\n",
+ "\n",
+ "# Read training data\n",
+ "data = read($data, format=\"csv\")\n",
+ "n = nrow(data)\n",
+ "\n",
+ "# Extract images and labels\n",
+ "images = data[,2:ncol(data)]\n",
+ "labels = data[,1]\n",
+ "\n",
+ "# Scale images to [-1,1], and one-hot encode the labels\n",
+ "images = (images / 255.0) * 2 - 1\n",
+ "labels = table(seq(1, n), labels+1, n, 10)\n",
+ "\n",
+ "# Split into training (55,000 examples) and validation (5,000 examples)\n",
+ "X = images[5001:nrow(images),]\n",
+ "X_val = images[1:5000,]\n",
+ "y = labels[5001:nrow(images),]\n",
+ "y_val = labels[1:5000,]\n",
+ "\n",
+ "# Train\n",
+ "epochs = 10\n",
+ "[W1, b1, W2, b2, W3, b3, W4, b4] = mnist_lenet::train(X, y, X_val, y_val, C, Hin, Win, epochs)\n",
+ "\"\"\"\n",
+ "script = (dml(script_string).input(\"$data\", \"data/mnist/mnist_train.csv\")\n",
+ " .input(C=1, Hin=28, Win=28)\n",
+ " .output(\"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\", \"W4\", \"b4\"))\n",
+ "W1, b1, W2, b2, W3, b3, W4, b4 = (ml.execute(script)\n",
+ " .get(\"W1\", \"b1\", \"W2\", \"b2\", \"W3\", \"b3\", \"W4\", \"b4\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2. Compute Test Accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "script_string = \"\"\"\n",
+ "source(\"nn/examples/mnist_lenet.dml\") as mnist_lenet\n",
+ "\n",
+ "# Read test data\n",
+ "data = read($data, format=\"csv\")\n",
+ "n = nrow(data)\n",
+ "\n",
+ "# Extract images and labels\n",
+ "X_test = data[,2:ncol(data)]\n",
+ "y_test = data[,1]\n",
+ "\n",
+ "# Scale images to [-1,1], and one-hot encode the labels\n",
+ "X_test = (X_test / 255.0) * 2 - 1\n",
+ "y_test = table(seq(1, n), y_test+1, n, 10)\n",
+ "\n",
+ "# Eval on test set\n",
+ "probs = mnist_lenet::predict(X_test, C, Hin, Win, W1, b1, W2, b2, W3, b3, W4, b4)\n",
+ "[loss, accuracy] = mnist_lenet::eval(probs, y_test)\n",
+ "\n",
+ "print(\"Test Accuracy: \" + accuracy)\n",
+ "\"\"\"\n",
+ "script = dml(script_string).input(**{\"$data\": \"data/mnist/mnist_train.csv\",\n",
+ " \"C\": 1, \"Hin\": 28, \"Win\": 28,\n",
+ " \"W1\": W1, \"b1\": b1,\n",
+ " \"W2\": W2, \"b2\": b2,\n",
+ " \"W3\": W3, \"b3\": b3,\n",
+ " \"W4\": W4, \"b4\": b4})\n",
+ "ml.execute(script)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3. Extract Model Into Spark DataFrames For Future Use"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "W1_df = W1.toDF()\n",
+ "b1_df = b1.toDF()\n",
+ "W2_df = W2.toDF()\n",
+ "b2_df = b2.toDF()\n",
+ "W3_df = W3.toDF()\n",
+ "b3_df = b3.toDF()\n",
+ "W4_df = W4.toDF()\n",
+ "b4_df = b4.toDF()\n",
+ "W1_df, b1_df, W2_df, b2_df, W3_df, b3_df, W4_df, b4_df"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 + Spark 2.x + SystemML",
+ "language": "python",
+ "name": "pyspark3_2.x"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/nn/examples/Example - MNIST Softmax Classifier.ipynb
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/nn/examples/Example - MNIST Softmax Classifier.ipynb b/scripts/staging/SystemML-NN/nn/examples/Example - MNIST Softmax Classifier.ipynb
new file mode 100644
index 0000000..5e7182a
--- /dev/null
+++ b/scripts/staging/SystemML-NN/nn/examples/Example - MNIST Softmax Classifier.ipynb
@@ -0,0 +1,179 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Quick Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "# Create a SystemML MLContext object\n",
+ "from systemml import MLContext, dml\n",
+ "ml = MLContext(sc)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Download Data - MNIST"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "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]. Here, we download 60,000 training examples, and 10,000 test examples, where the format is \"label, pixel_1, pixel_2, ..., pixel_n\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "%%sh\n",
+ "mkdir -p data/mnist/\n",
+ "cd data/mnist/\n",
+ "curl -O https://pjreddie.com/media/files/mnist_train.csv\n",
+ "curl -O https://pjreddie.com/media/files/mnist_test.csv"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## SystemML Softmax Model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 1. Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "training = \"\"\"\n",
+ "source(\"nn/examples/mnist_softmax.dml\") as mnist_softmax\n",
+ "\n",
+ "# Read training data\n",
+ "data = read($data, format=\"csv\")\n",
+ "n = nrow(data)\n",
+ "\n",
+ "# Extract images and labels\n",
+ "images = data[,2:ncol(data)]\n",
+ "labels = data[,1]\n",
+ "\n",
+ "# Scale images to [0,1], and one-hot encode the labels\n",
+ "images = images / 255.0\n",
+ "labels = table(seq(1, n), labels+1, n, 10)\n",
+ "\n",
+ "# Split into training (55,000 examples) and validation (5,000 examples)\n",
+ "X = images[5001:nrow(images),]\n",
+ "X_val = images[1:5000,]\n",
+ "y = labels[5001:nrow(images),]\n",
+ "y_val = labels[1:5000,]\n",
+ "\n",
+ "# Train\n",
+ "epochs = 1\n",
+ "[W, b] = mnist_softmax::train(X, y, X_val, y_val, epochs)\n",
+ "\"\"\"\n",
+ "script = dml(training).input(\"$data\", \"data/mnist/mnist_train.csv\").output(\"W\", \"b\")\n",
+ "W, b = ml.execute(script).get(\"W\", \"b\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 2. Compute Test Accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "testing = \"\"\"\n",
+ "source(\"nn/examples/mnist_softmax.dml\") as mnist_softmax\n",
+ "\n",
+ "# Read test data\n",
+ "data = read($data, format=\"csv\")\n",
+ "n = nrow(data)\n",
+ "\n",
+ "# Extract images and labels\n",
+ "X_test = data[,2:ncol(data)]\n",
+ "y_test = data[,1]\n",
+ "\n",
+ "# Scale images to [0,1], and one-hot encode the labels\n",
+ "X_test = X_test / 255.0\n",
+ "y_test = table(seq(1, n), y_test+1, n, 10)\n",
+ "\n",
+ "# Eval on test set\n",
+ "probs = mnist_softmax::predict(X_test, W, b)\n",
+ "[loss, accuracy] = mnist_softmax::eval(probs, y_test)\n",
+ "\n",
+ "print(\"Test Accuracy: \" + accuracy)\n",
+ "\"\"\"\n",
+ "script = dml(testing).input(\"$data\", \"data/mnist/mnist_test.csv\", W=W, b=b)\n",
+ "ml.execute(script)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### 3. Extract Model Into Spark DataFrames For Future Use"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "W_df = W.toDF()\n",
+ "b_df = b.toDF()\n",
+ "W_df, b_df"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.1"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/nn/examples/README.md
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diff --git a/scripts/staging/SystemML-NN/nn/examples/README.md b/scripts/staging/SystemML-NN/nn/examples/README.md
new file mode 100644
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+<!--
+{% comment %}
+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.
+{% endcomment %}
+-->
+
+# SystemML-NN Examples
+
+#### This folder contains scripts and PySpark Jupyter notebooks serving as examples of using the *SystemML-NN* (`nn`) deep learning library.
+
+---
+
+# Examples
+### MNIST Softmax Classifier
+
+* This example trains a softmax classifier, which is essentially a multi-class logistic regression model, on the MNIST data. The model will be trained on the *training* images, validated on the *validation* images, and tested for final performance metrics on the *test* images.
+* Notebook: `Example - MNIST Softmax Classifier.ipynb`.
+* DML Functions: `mnist_softmax.dml`
+* Training script: `mnist_softmax-train.dml`
+* Prediction script: `mnist_softmax-predict.dml`
+
+### MNIST "LeNet" Neural Net
+
+* This example trains a neural network on the MNIST data using a ["LeNet" architecture](http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf). The model will be trained on the *training* images, validated on the *validation* images, and tested for final performance metrics on the *test* images.
+* Notebook: `Example - MNIST LeNet.ipynb`.
+* DML Functions: `mnist_lenet.dml`
+* Training script: `mnist_lenet-train.dml`
+* Prediction script: `mnist_lenet-predict.dml`
+
+---
+
+# Setup
+## Code
+* To run the examples, please first download and unzip the project via GitHub using the "Clone or download" button on the [homepage of the project](https://github.com/dusenberrymw/systemml-nn), *or* via the following commands:
+
+ ```
+ git clone https://github.com/dusenberrymw/systemml-nn.git
+ ```
+
+* Then, move into the `systemml-nn` folder via:
+ ```
+ cd systemml-nn
+ ```
+
+## Data
+* These examples use the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, which contains labeled 28x28 pixel images of handwritten digits in the range of 0-9. There are 60,000 training images, and 10,000 testing images. Of the 60,000 training images, 5,000 will be used as validation images.
+* **Download**:
+ * **Notebooks**: The data will be automatically downloaded as a step in either of the example notebooks.
+ * **Training scripts**: Please run `get_mnist_data.sh` to download the data separately.
+
+## Execution
+* These examples contain scripts written in SystemML's R-like language (`*.dml`), as well as PySpark Jupyter notebooks (`*.ipynb`). The scripts contain the math for the algorithms, enclosed in functions, and the notebooks serve as full, end-to-end examples of reading in data, training models using the functions within the scripts, and evaluating final performance.
+* **Notebooks**: To run the notebook examples, please install the SystemML Python package with `pip install systemml`, and then startup Jupyter in the following manner from this directory (or for more information, please see [this great blog post](http://spark.tc/0-to-life-changing-application-with-apache-systemml/)):
+
+ ```
+ PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark --master local[*] --driver-memory 3G --driver-class-path SystemML.jar --jars SystemML.jar
+ ```
+
+ Note that all printed output, such as training statistics, from the SystemML scripts will be sent to the terminal in which Jupyter was started (for now...).
+
+* **Scripts**: To run the scripts from the command line using `spark-submit`, please see the comments located at the top of the `-train` and `-predict` scripts.
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/nn/examples/get_mnist_data.sh
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diff --git a/scripts/staging/SystemML-NN/nn/examples/get_mnist_data.sh b/scripts/staging/SystemML-NN/nn/examples/get_mnist_data.sh
new file mode 100755
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--- /dev/null
+++ b/scripts/staging/SystemML-NN/nn/examples/get_mnist_data.sh
@@ -0,0 +1,28 @@
+#!/usr/bin/env bash
+#-------------------------------------------------------------
+#
+# 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.
+#
+#-------------------------------------------------------------
+
+DIR="$(cd "$(dirname "$0")" && pwd)"
+mkdir -p $DIR/data/mnist/
+cd $DIR/data/mnist/
+curl -O https://pjreddie.com/media/files/mnist_train.csv
+curl -O https://pjreddie.com/media/files/mnist_test.csv
+
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/nn/examples/mnist_lenet-predict.dml
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/nn/examples/mnist_lenet-predict.dml b/scripts/staging/SystemML-NN/nn/examples/mnist_lenet-predict.dml
new file mode 100644
index 0000000..85a5307
--- /dev/null
+++ b/scripts/staging/SystemML-NN/nn/examples/mnist_lenet-predict.dml
@@ -0,0 +1,91 @@
+#-------------------------------------------------------------
+#
+# 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 LeNet - Predict
+#
+# This script computes the class probability predictions of a
+# trained convolutional net using the "LeNet" architecture on
+# images of handwritten digits.
+#
+# Inputs:
+# - X: File containing training images.
+# The format is "pixel_1, pixel_2, ..., pixel_n".
+# - C: Number of color chanels in the images.
+# - Hin: Input image height.
+# - Win: Input image width.
+# - 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 (running from outside the `nn` folder):
+# 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
+# $SYSTEMML_HOME/target/SystemML.jar -f nn/examples/mnist_lenet-predict.dml
+# -nvargs X=nn/examples/data/mnist/images.csv C=1 Hin=28 Win=28
+# model_dir=nn/examples/model/mnist_lenet out_dir=nn/examples/data/mnist
+# ```
+#
+source("nn/examples/mnist_lenet.dml") as mnist_lenet
+
+# Read training data
+fmt = ifdef($fmt, "csv")
+X = read($X, format=fmt)
+C = $C
+Hin = $Hin
+Win = $Win
+
+# Scale images to [-1,1]
+X = (X / 255.0) * 2 - 1
+
+# Read model coefficients
+W1 = read($model_dir+"/W1")
+b1 = read($model_dir+"/b1")
+W2 = read($model_dir+"/W2")
+b2 = read($model_dir+"/b2")
+W3 = read($model_dir+"/W3")
+b3 = read($model_dir+"/b3")
+W4 = read($model_dir+"/W4")
+b4 = read($model_dir+"/b4")
+
+# Predict classes
+probs = mnist_lenet::predict(X, C, Hin, Win, W1, b1, W2, b2, W3, b3, W4, b4)
+
+# Output results
+write(probs, $out_dir+"/probs."+fmt, format=fmt)
+
http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/1f5cf697/scripts/staging/SystemML-NN/nn/examples/mnist_lenet-train.dml
----------------------------------------------------------------------
diff --git a/scripts/staging/SystemML-NN/nn/examples/mnist_lenet-train.dml b/scripts/staging/SystemML-NN/nn/examples/mnist_lenet-train.dml
new file mode 100644
index 0000000..0fc733e
--- /dev/null
+++ b/scripts/staging/SystemML-NN/nn/examples/mnist_lenet-train.dml
@@ -0,0 +1,123 @@
+#-------------------------------------------------------------
+#
+# 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 LeNet - Train
+#
+# This script trains a convolutional net using the "LeNet" architecture
+# 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".
+# - C: Number of color chanels in the images.
+# - Hin: Input image height.
+# - Win: Input image width.
+# - epochs: [DEFAULT: 10] Total number of full training loops over
+# the full data set.
+# - out_dir: [DEFAULT: "."] 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:
+# - W1, W2, W3, W4: Files containing the trained weights of the model.
+# - b1, b2, b3, b4: Files 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 outside the `nn` 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
+# $SYSTEMML_HOME/target/SystemML.jar -f nn/examples/mnist_lenet-train.dml
+# -nvargs train=nn/examples/data/mnist/mnist_train.csv test=nn/examples/data/mnist/mnist_test.csv
+# C=1 Hin=28 Win=28 epochs=10 out_dir=nn/examples/model/mnist_lenet
+# ```
+#
+source("nn/examples/mnist_lenet.dml") as mnist_lenet
+
+# Read training data & settings
+fmt = ifdef($fmt, "csv")
+train = read($train, format=fmt)
+test = read($test, format=fmt)
+C = $C
+Hin = $Hin
+Win = $Win
+epochs = ifdef($epochs, 10)
+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 [-1,1], and one-hot encode the labels
+n = nrow(train)
+n_test = nrow(test)
+images = (images / 255.0) * 2 - 1
+labels = table(seq(1, n), labels+1, n, 10)
+X_test = (X_test / 255.0) * 2 - 1
+y_test = table(seq(1, n_test), y_test+1, n_test, 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
+[W1, b1, W2, b2, W3, b3, W4, b4] = mnist_lenet::train(X, y, X_val, y_val, C, Hin, Win, epochs)
+
+# Write model out
+write(W1, out_dir+"/W1")
+write(b1, out_dir+"/b1")
+write(W2, out_dir+"/W2")
+write(b2, out_dir+"/b2")
+write(W3, out_dir+"/W3")
+write(b3, out_dir+"/b3")
+write(W4, out_dir+"/W4")
+write(b4, out_dir+"/b4")
+
+# Eval on test set
+probs = mnist_lenet::predict(X_test, C, Hin, Win, W1, b1, W2, b2, W3, b3, W4, b4)
+[loss, accuracy] = mnist_lenet::eval(probs, y_test)
+
+# Output results
+print("Test Accuracy: " + accuracy)
+write(accuracy, out_dir+"/accuracy")
+
+print("")
+print("")
+