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Apoorvgarg-creatorApoorvgarg-creator
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tensor flow tutorial added
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‎Tensor flow_ Oct 7 /IntroTf.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import tensorflow as tf\n",
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"import time"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Detecting GPUs\n"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 11,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[name: \"/device:CPU:0\"\n",
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"device_type: \"CPU\"\n",
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"memory_limit: 268435456\n",
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"locality {\n",
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"}\n",
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"incarnation: 11702401161608791080\n",
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", name: \"/device:XLA_CPU:0\"\n",
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"device_type: \"XLA_CPU\"\n",
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"memory_limit: 17179869184\n",
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"locality {\n",
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"}\n",
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"incarnation: 2978008688873522925\n",
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"physical_device_desc: \"device: XLA_CPU device\"\n",
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"]\n"
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]
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}
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],
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"source": [
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"from tensorflow.python.client import device_lib\n",
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"print(device_lib.list_local_devices())\n"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## performance Comparison"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 12,
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"outputs": [],
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"source": [
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"ITER = 500\n",
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"X = np.random.randn(1000,1000)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Numpy performance\n"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 13,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Time taken by numpy8.148992\n"
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]
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}
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],
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"source": [
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"start = time.time()\n",
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"for i in range(ITER):\n",
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" out = np.matmul(X,X)\n",
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"\n",
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"end = time.time()\n",
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"\n",
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"print(\"Time taken by numpy{:.6f}\".format(end-start))"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Tensorflow performance"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 14,
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"outputs": [],
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"source": [
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"import tensorflow.compat.v1 as tf\n",
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"tf.disable_v2_behavior()"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 15,
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"outputs": [],
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"source": [
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"x = tf.placeholder( shape=(1000,1000),dtype=tf.float32)\n",
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"output = tf.matmul(x,x)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 16,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Time taken by Tensorflow --> 4.072855\n"
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]
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}
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],
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"source": [
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"sess = tf.Session()\n",
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"start = time.time()\n",
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"for i in range(ITER):\n",
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" sess.run(output, feed_dict={\n",
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" x: X\n",
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" })\n",
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"end = time.time()\n",
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"\n",
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"print(\"Time taken by Tensorflow --> {:.6f}\".format(end-start))"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 17,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Time taken by Tensorflow --> 4.189568\n"
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]
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}
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],
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"source": [
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"with tf.device('/device:CPU:0'):\n",
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" sess = tf.Session()\n",
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" start = time.time()\n",
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" for i in range(ITER):\n",
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" sess.run(output, feed_dict={\n",
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" x: X\n",
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" })\n",
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" end = time.time()\n",
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"\n",
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" print(\"Time taken by Tensorflow --> {:.6f}\".format(end-start))"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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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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