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https://github.com/bspeice/ecbm4040
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Add Homework 0 files
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Bradlee Speice
commit
825b0c0327
192
Homework 0/.ipynb_checkpoints/Homework 0-checkpoint.ipynb
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192
Homework 0/.ipynb_checkpoints/Homework 0-checkpoint.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Problem 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"!THEANO_FLAGS=device=gpu python DeepLearningTutorials/code/logistic_sgd.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"!THEANO_FLAGS=device=gpu python DeepLearningTutorials/code/convolutional_mlp.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"!THEANO_FLAGS=device=cpu python DeepLearningTutorials/code/logistic_sgd.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"!THEANO_FLAGS=device=cpu timeout 1200 python DeepLearningTutorials/code/convolutional_mlp.py"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Problem 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import theano.tensor as T\n",
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"from theano.tensor.shared_randomstreams import RandomStreams\n",
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"from theano import function, shared\n",
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"from theano.compile.sharedvalue import SharedVariable\n",
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"import numpy as np\n",
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"\n",
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"srng = RandomStreams(seed=1234)\n",
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"\n",
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"x = T.fcol()\n",
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"a = srng.uniform((10, 1))\n",
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"a_shared = SharedVariable(\n",
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" value=np.zeros((10, 1)),\n",
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" type=a.type,\n",
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" name='a',\n",
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" strict=True\n",
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")\n",
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"\n",
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"b = srng.uniform((10, 1))\n",
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"b_shared = SharedVariable(\n",
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" value=np.zeros((10, 1)),\n",
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" type=b.type,\n",
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" name='b',\n",
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" strict=True\n",
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")\n",
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"\n",
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"z = (x + a).T.dot(b)\n",
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"f = function([x], z, updates=[(a_shared, a), (b_shared, b)], allow_input_downcast=True)\n",
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"\n",
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"f(np.ones((10, 1)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"a_shared.get_value()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"b_shared.get_value()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"(np.ones((10, 1)) + a_shared.get_value()).T.dot(b_shared.get_value())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Problem 3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def fib(n):\n",
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" if n <= 1:\n",
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" return 0\n",
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" elif n == 2:\n",
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" return 1\n",
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" else:\n",
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" a = shared(0)\n",
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" b = shared(1)\n",
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" f = function([], a + b, updates=[(b, a + b), (a, b)])\n",
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" for i in range(1, n):\n",
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" f()\n",
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" \n",
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" return b.get_value()\n",
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" \n",
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"print('fib(10): {}'.format(fib(10)))\n",
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"print('fib(20): {}'.format(fib(20)))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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1
Homework 0/DeepLearningTutorials
Submodule
1
Homework 0/DeepLearningTutorials
Submodule
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Subproject commit e64a8fcc096f434f6ab2ba5ab26868a877ad5402
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BIN
Homework 0/E4040_2016Fall_HW0_Student_updated.pdf
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BIN
Homework 0/E4040_2016Fall_HW0_Student_updated.pdf
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Binary file not shown.
192
Homework 0/Homework 0.ipynb
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192
Homework 0/Homework 0.ipynb
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@ -0,0 +1,192 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Problem 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"!THEANO_FLAGS=device=gpu python DeepLearningTutorials/code/logistic_sgd.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"!THEANO_FLAGS=device=gpu python DeepLearningTutorials/code/convolutional_mlp.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"!THEANO_FLAGS=device=cpu python DeepLearningTutorials/code/logistic_sgd.py"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"%%time\n",
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"!THEANO_FLAGS=device=cpu timeout 1200 python DeepLearningTutorials/code/convolutional_mlp.py"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Problem 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import theano.tensor as T\n",
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"from theano.tensor.shared_randomstreams import RandomStreams\n",
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"from theano import function, shared\n",
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"from theano.compile.sharedvalue import SharedVariable\n",
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"import numpy as np\n",
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"\n",
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"srng = RandomStreams(seed=1234)\n",
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"\n",
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"x = T.fcol()\n",
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"a = srng.uniform((10, 1))\n",
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"a_shared = SharedVariable(\n",
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" value=np.zeros((10, 1)),\n",
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" type=a.type,\n",
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" name='a',\n",
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" strict=True\n",
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")\n",
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"\n",
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"b = srng.uniform((10, 1))\n",
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"b_shared = SharedVariable(\n",
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" value=np.zeros((10, 1)),\n",
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" type=b.type,\n",
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" name='b',\n",
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" strict=True\n",
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")\n",
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"\n",
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"z = (x + a).T.dot(b)\n",
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"f = function([x], z, updates=[(a_shared, a), (b_shared, b)], allow_input_downcast=True)\n",
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"\n",
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"f(np.ones((10, 1)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"a_shared.get_value()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"b_shared.get_value()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"(np.ones((10, 1)) + a_shared.get_value()).T.dot(b_shared.get_value())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Problem 3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def fib(n):\n",
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" if n <= 1:\n",
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" return 0\n",
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" elif n == 2:\n",
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" return 1\n",
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" else:\n",
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" a = shared(0)\n",
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" b = shared(1)\n",
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" f = function([], a + b, updates=[(b, a + b), (a, b)])\n",
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" for i in range(1, n):\n",
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" f()\n",
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" \n",
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" return b.get_value()\n",
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" \n",
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"print('fib(10): {}'.format(fib(10)))\n",
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"print('fib(20): {}'.format(fib(20)))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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849
Homework 0/best_model.pkl
Normal file
849
Homework 0/best_model.pkl
Normal file
File diff suppressed because one or more lines are too long
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