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添加了THEANO 中新的部分：条件语句（已完成），循环（未完成）
Former-commit-id: d0f7369 Former-commit-id: 830e102eb3fb331a5b9d5cef04035ebac58c1fe4 [formerly a74a6fa2bc5e126fc771e2b9fffd1127466655c0] [formerly a95a51beb664dd5f44ba31b69757a78884fec49b [formerly 5024c97f116b12566d0041d0c2d2763e299ed92f]] [formerly abc5fff86cee35a0453398c2215c54a64312de6b [formerly b893ba81d31ba86d58ac4d7485ee37956154fca4] [formerly 4ee8a24f7c9a690ad0477c3e875af230f365c085 [formerly 4816dd69373c205704811e22c0d3dc6686ddcb56]]] [formerly 83c9c62360418c82879fd3f8bdbd3dcf14ac715f [formerly cf4eccbcfa0fd33aee74ea64a324e62cb23ea938] [formerly f6d0328bd300e94cdedc1f98d985b8f9871f1fef [formerly 825c002aa06506676622d7672aea87dc4f69a6e8]] [formerly ca61cb190cfe285d61ebda83e9b899c541608f2f [formerly 13112ce28c1a83a07e6ff34ee04cd4a93540a0cf] [formerly 2e6e92068692127ce8bfe96caa3c8f498b9689c9 [formerly 47c0039]]]] Former-commit-id: aed0815352cbb90ebd870e0ae87ffc8a3cf1994f [formerly cccc4ecb14a70bf019a1c3e9f676a6b1f4900562] [formerly f2631154b38bae9b3d6c2d5ddb25f489019a6e16 [formerly 38f29af4a384f5307f074f5736c7833397a6ae4d]] [formerly b3982ab7425260f663ff9ace79accee5aa92ce97 [formerly f12026ece163fba818314e6a0b0529b814d89131] [formerly 705c62c50912239893e9cb12af82e15c8c279d0e [formerly 22f1fa88cb9c73a2b0a87392e93ef8ee2e57b733]]] Former-commit-id: d89b1e6618d41ea5e66a41b79d0479e5af45ffb6 [formerly e43c6b5a8bcb757ae9f14c0a213419ea118753a2] [formerly e03fecdf2108b995b10c78f0576d02dd4fa8fbde [formerly e0e189f56d02385bb26db357131d1c2b05c776f9]] Former-commit-id: 4c36a74815ed018469e50c2909645e0e7b113e95 [formerly 98d24e1371efa6164aa2a67d90d21baf4b737756] Former-commit-id: 5c40852455052bde2a93a8254eea9cafa5abfcb2
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‎09. theano/09.03 gpu on windows.ipynb.REMOVED.git-id

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‎09. theano/09.04 linear regression.ipynb.REMOVED.git-id

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‎09. theano/09.05 configuration settings and compiling modes.ipynb.REMOVED.git-id

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‎09. theano/09.05 logistic regression .ipynb.REMOVED.git-id

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‎09. theano/09.06 conditions in theano.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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"# Theano 条件语句"
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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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"`theano` 中提供了两种条件语句，`ifelse` 和 `switch`，两者都是用于在符号变量上使用条件语句：\n",
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"\n",
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"- `ifelse(condition, var1, var2)`\n",
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" - 如果 `condition` 为 `true`，返回 `var1`，否则返回 `var2`\n",
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"- `switch(tensor, var1, var2)`\n",
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" - Elementwise `ifelse` 操作，更一般化\n",
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"- `switch` 会计算两个输出，而 `ifelse` 只会根据给定的条件，计算相应的输出。\n",
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"\n",
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"`ifelse` 需要从 `theano.ifelse` 中导入，而 `switch` 在 `theano.tensor` 模块中。"
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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": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using gpu device 1: Tesla K10.G2.8GB (CNMeM is disabled)\n"
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]
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}
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],
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"source": [
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"import theano, time\n",
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"import theano.tensor as T\n",
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"import numpy as np\n",
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"from theano.ifelse import ifelse"
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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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"假设我们有两个标量参数：$a, b$，和两个矩阵 $\\mathbf{x, y}$，定义函数为：\n",
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"\n",
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"$$ \n",
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"\\mathbf z = f(a, b,\\mathbf{x, y}) = \\left\\{ \n",
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"\\begin{aligned}\n",
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" \\mathbf x & ,\\ a <= b\\\\\n",
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" \\mathbf y & ,\\ a > b\n",
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"\\end{aligned}\n",
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"\\right.\n",
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"$$\n",
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"\n",
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"定义变量："
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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": 2,
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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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"a, b = T.scalars('a', 'b')\n",
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"x, y = T.matrices('x', 'y')"
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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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"用 `ifelse` 构造，小于等于用 `T.lt()`，大于等于用 `T.gt()`："
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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": 3,
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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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"z_ifelse = ifelse(T.lt(a, b), x, y)\n",
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"\n",
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"f_ifelse = theano.function([a, b, x, y], z_ifelse)"
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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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"用 `switch` 构造："
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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": 4,
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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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"z_switch = T.switch(T.lt(a, b), x, y)\n",
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"\n",
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"f_switch = theano.function([a, b, x, y], z_switch)"
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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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"测试数据："
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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": 5,
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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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"val1 = 0.\n",
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"val2 = 1.\n",
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"big_mat1 = np.ones((10000, 1000), dtype=theano.config.floatX)\n",
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"big_mat2 = np.ones((10000, 1000), dtype=theano.config.floatX)"
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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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"比较两者的运行速度："
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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": 6,
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"metadata": {
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"collapsed": false
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},
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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 spent evaluating both values 0.638598 sec\n",
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"time spent evaluating one value 0.461249 sec\n"
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]
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}
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],
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"source": [
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"n_times = 10\n",
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"\n",
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"tic = time.clock()\n",
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"for i in xrange(n_times):\n",
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" f_switch(val1, val2, big_mat1, big_mat2)\n",
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"print 'time spent evaluating both values %f sec' % (time.clock() - tic)\n",
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"\n",
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"tic = time.clock()\n",
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"for i in xrange(n_times):\n",
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" f_ifelse(val1, val2, big_mat1, big_mat2)\n",
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"print 'time spent evaluating one value %f sec' % (time.clock() - tic)"
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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 2",
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"language": "python",
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"name": "python2"
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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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