scripts/nn/examples/Example - MNIST LeNet deprecated.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick Setup - Warning: Deprecated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a SystemDS MLContext object\n",
    "from systemds 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": [
    "## SystemDS \"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"
   ]
  }
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