Add Homework 0 files

Homework 0/.ipynb_checkpoints/Homework 0-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Problem 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "!THEANO_FLAGS=device=gpu python DeepLearningTutorials/code/logistic_sgd.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "!THEANO_FLAGS=device=gpu python DeepLearningTutorials/code/convolutional_mlp.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "!THEANO_FLAGS=device=cpu python DeepLearningTutorials/code/logistic_sgd.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "!THEANO_FLAGS=device=cpu timeout 1200 python DeepLearningTutorials/code/convolutional_mlp.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Problem 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import theano.tensor as T\n",
+    "from theano.tensor.shared_randomstreams import RandomStreams\n",
+    "from theano import function, shared\n",
+    "from theano.compile.sharedvalue import SharedVariable\n",
+    "import numpy as np\n",
+    "\n",
+    "srng = RandomStreams(seed=1234)\n",
+    "\n",
+    "x = T.fcol()\n",
+    "a = srng.uniform((10, 1))\n",
+    "a_shared = SharedVariable(\n",
+    "    value=np.zeros((10, 1)),\n",
+    "    type=a.type,\n",
+    "    name='a',\n",
+    "    strict=True\n",
+    ")\n",
+    "\n",
+    "b = srng.uniform((10, 1))\n",
+    "b_shared = SharedVariable(\n",
+    "    value=np.zeros((10, 1)),\n",
+    "    type=b.type,\n",
+    "    name='b',\n",
+    "    strict=True\n",
+    ")\n",
+    "\n",
+    "z = (x + a).T.dot(b)\n",
+    "f = function([x], z, updates=[(a_shared, a), (b_shared, b)], allow_input_downcast=True)\n",
+    "\n",
+    "f(np.ones((10, 1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a_shared.get_value()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "b_shared.get_value()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "(np.ones((10, 1)) + a_shared.get_value()).T.dot(b_shared.get_value())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Problem 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def fib(n):\n",
+    "    if n <= 1:\n",
+    "        return 0\n",
+    "    elif n == 2:\n",
+    "        return 1\n",
+    "    else:\n",
+    "        a = shared(0)\n",
+    "        b = shared(1)\n",
+    "        f = function([], a + b, updates=[(b, a + b), (a, b)])\n",
+    "        for i in range(1, n):\n",
+    "            f()\n",
+    "            \n",
+    "    return b.get_value()\n",
+    "        \n",
+    "print('fib(10): {}'.format(fib(10)))\n",
+    "print('fib(20): {}'.format(fib(20)))"
+   ]
+  }
+ ],
+ "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
+}