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add examples from tensorflow book.
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‎Deep_Learning_with_TensorFlow/0.12.0/Chapter03/1. 图，张量及会话.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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"#### 1. 定义两个不同的图"
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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": "stdout",
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"output_type": "stream",
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"text": [
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"[ 0.]\n",
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"[ 1.]\n"
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]
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}
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],
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"source": [
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"import tensorflow as tf\n",
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"\n",
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"g1 = tf.Graph()\n",
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"with g1.as_default():\n",
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" v = tf.get_variable(\"v\", [1], initializer = tf.zeros_initializer) # 设置初始值为0\n",
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"\n",
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"g2 = tf.Graph()\n",
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"with g2.as_default():\n",
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" v = tf.get_variable(\"v\", [1], initializer = tf.ones_initializer()) # 设置初始值为1\n",
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" \n",
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"with tf.Session(graph = g1) as sess:\n",
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" tf.global_variables_initializer().run()\n",
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" with tf.variable_scope(\"\", reuse=True):\n",
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" print(sess.run(tf.get_variable(\"v\")))\n",
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"\n",
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"with tf.Session(graph = g2) as sess:\n",
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" tf.global_variables_initializer().run()\n",
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" with tf.variable_scope(\"\", reuse=True):\n",
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" print(sess.run(tf.get_variable(\"v\")))"
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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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"collapsed": true
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},
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"source": [
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"#### 2. 张量的概念"
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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": 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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"Tensor(\"add:0\", shape=(2,), dtype=float32)\n",
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"[ 3. 5.]\n"
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]
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}
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],
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"source": [
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"import tensorflow as tf\n",
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"a = tf.constant([1.0, 2.0], name=\"a\")\n",
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"b = tf.constant([2.0, 3.0], name=\"b\")\n",
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"result = a + b\n",
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"print result\n",
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"\n",
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"sess = tf.InteractiveSession ()\n",
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"print(result.eval())\n",
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"sess.close()"
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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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"#### 3. 会话的使用"
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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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"3.1 创建和关闭会话"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 3. 5.]\n"
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]
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}
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],
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"source": [
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"# 创建一个会话。\n",
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"sess = tf.Session()\n",
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"\n",
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"# 使用会话得到之前计算的结果。\n",
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"print(sess.run(result))\n",
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"\n",
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"# 关闭会话使得本次运行中使用到的资源可以被释放。\n",
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"sess.close()"
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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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"3.2 使用with statement 来创建会话"
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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": 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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"[ 3. 5.]\n"
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]
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}
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],
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"source": [
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"with tf.Session() as sess:\n",
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" print(sess.run(result))"
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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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"3.3 指定默认会话"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 3. 5.]\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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"with sess.as_default():\n",
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" print(result.eval())"
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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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"[ 3. 5.]\n",
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"[ 3. 5.]\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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"\n",
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"# 下面的两个命令有相同的功能。\n",
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"print(sess.run(result))\n",
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"print(result.eval(session=sess))"
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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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"#### 4. 使用tf.InteractiveSession构建会话"
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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": 7,
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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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"[ 3. 5.]\n"
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]
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}
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],
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"source": [
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"sess = tf.InteractiveSession ()\n",
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"print(result.eval())\n",
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"sess.close()"
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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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"#### 5. 通过ConfigProto配置会话"
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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": 8,
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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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"config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True)\n",
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"sess1 = tf.InteractiveSession(config=config)\n",
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"sess2 = tf.Session(config=config)"
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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": "2.7.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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