{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Copyright (C) 2021 Intel Corporation*
\n",
"*SPDX-License-Identifier: BSD-3-Clause*
\n",
"*See: https://spdx.org/licenses/*\n",
"\n",
"---\n",
"\n",
"# MNIST Digit Classification with Lava"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_**Motivation**: In this tutorial, we will build a Lava Process for an MNIST\n",
"classifier, using the Lava Processes for LIF neurons and Dense connectivity.\n",
"The tutorial is useful to get started with Lava in a few minutes._"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### This tutorial assumes that you:\n",
"- have the [Lava framework installed](../in_depth/tutorial01_installing_lava.ipynb \"Tutorial on Installing Lava\")\n",
"- are familiar with the [Process concept in Lava](../in_depth/tutorial02_processes.ipynb \"Tutorial on Processes\")\n",
"\n",
"#### This tutorial gives a bird's-eye view of\n",
"- how Lava Process(es) can perform the MNIST digit classification task using\n",
"[Leaky Integrate-and-Fire (LIF)](https://github.com/lava-nc/lava/tree/main/src/lava/proc/lif \"Lava's LIF neuron\") neurons and [Dense\n",
"(fully connected)](https://github.com/lava-nc/lava/tree/main/src/lava/proc/dense \"Lava's Dense Connectivity\") connectivity.\n",
"- how to create a Process \n",
"- how to create Python ProcessModels \n",
"- how to connect Processes\n",
"- how to execute them"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Our MNIST Classifier\n",
"In this tutorial, we will build a multi-layer feed-forward classifier without\n",
" any convolutional layers. The architecture is shown below.\n",
"\n",
"> **Important Note**:\n",
">\n",
"> The classifier is a simple feed-forward model using pre-trained network \n",
"> parameters (weights and biases). It illustrates how to build, compile and \n",
"> run a functional model in Lava. Please refer to \n",
"> [Lava-DL](https://github.com/lava-nc/lava-dl) to understand how to train \n",
"> deep networks with Lava."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"