Initial commit: add notebooks

Author: nitnelave

.gitignore

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+.ipynb_checkpoints

00 Basic solver usage.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pycaffe tutorial\n",
+    "\n",
+    "This tutorial assumes that you already know how to use Caffe. If you don't,\n",
+    "consult the official caffe tutorials at:\n",
+    "- http://caffe.berkeleyvision.org/tutorial/\n",
+    "- http://caffe.berkeleyvision.org/gathered/examples/mnist.html\n",
+    "\n",
+    "Also assumed is a basic knowledge of Python. If it is not the case, have a look\n",
+    "at the official python tutorials here: https://docs.python.org/2/tutorial/\n",
+    "\n",
+    "Caffe uses Python 2.7, make sure you use are using the correct version by running\n",
+    "\n",
+    "`$ python --version`\n",
+    "\n",
+    "\n",
+    "\n",
+    "The objective of this tutorial is to present basic and more advanced uses of\n",
+    "the pycaffe interface, to use Caffe with Python.\n",
+    "\n",
+    "In this part of the tutorial, we will learn to start a standard training of\n",
+    "Caffe, with Python.  We will be using the data/networks provided in\n",
+    "http://caffe.berkeleyvision.org/gathered/examples/mnist.html\n",
+    "\n",
+    "Set the following variable to your caffe root:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "CAFFE_ROOT=\"/caffe\"\n",
+    "\n",
+    "import os\n",
+    "os.chdir(CAFFE_ROOT) # change the current directory to the caffe root, to help\n",
+    "                     # with the relative paths"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first step is to import the caffe library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import caffe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we decide whether to use CPU or GPU for the training.\n",
+    "\n",
+    "Training with a GPU\n",
+    "is faster, if the layers provide a GPU implementation. If you don't have a\n",
+    "compatible graphics card or your layers are not implemented for GPU, use the\n",
+    "CPU mode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialized caffe\n"
+     ]
+    }
+   ],
+   "source": [
+    "USE_GPU = True\n",
+    "\n",
+    "if USE_GPU:\n",
+    "    caffe.set_device(0) # Or the index of the GPU you want to use\n",
+    "    caffe.set_mode_gpu()\n",
+    "    # Multi-GPU training is not available from Python, see\n",
+    "    # https://github.com/BVLC/caffe/issues/2936\n",
+    "else:\n",
+    "    caffe.set_mode_cpu()\n",
+    "\n",
+    "print(\"Initialized caffe\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, as we have a solver file, we will load it (paths are relative to the caffe root)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "solver_file = \"examples/mnist/lenet_solver.prototxt\"\n",
+    "\n",
+    "solver = caffe.SGDSolver(solver_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, just run the training."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "solver.solve()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your network is now trained, and ready to go! The output is in `examples/mnist/lenet_iter_10000.caffemodel`.\n",
+    "\n",
+    "If you want to resume training from a snapshot, it's very simple too:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "snapshot_file = \"examples/mnist/lenet_iter_5000.solverstate\"\n",
+    "solver.solve(snapshot_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('iter', 10000),\n",
+       " ('net', <caffe._caffe.Net at 0x1f9d488>),\n",
+       " ('restore',\n",
+       "  <bound method SGDSolver.restore of <caffe._caffe.SGDSolver object at 0x1fb19f0>>),\n",
+       " ('set_iter',\n",
+       "  <bound method SGDSolver.set_iter of <caffe._caffe.SGDSolver object at 0x1fb19f0>>),\n",
+       " ('snapshot',\n",
+       "  <bound method SGDSolver.snapshot of <caffe._caffe.SGDSolver object at 0x1fb19f0>>),\n",
+       " ('solve',\n",
+       "  <bound method SGDSolver.solve of <caffe._caffe.SGDSolver object at 0x1fb19f0>>),\n",
+       " ('step',\n",
+       "  <bound method SGDSolver.step of <caffe._caffe.SGDSolver object at 0x1fb19f0>>),\n",
+       " ('test_nets', <caffe._caffe.NetVec at 0x49f4de0>)]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(e, getattr(solver, e)) for e in dir(solver) if not e.startswith(\"__\")]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And there you go! This is the Python equivalent of\n",
+    "`example/mnist/train_lenet.sh`, or\n",
+    "\n",
+    "`$ caffe train --solver=examples/mnist/lenet_solver.prototxt`\n",
+    "\n",
+    "But you can do much more than that with python. The next part covers network manipulation."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}