1-基本结构.ipynb

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   "source": [
    "import tensorflow as tf\n",
    "# https://www.lfd.uci.edu/~gohlke/pythonlibs/#xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    {
     "ename": "NameError",
     "evalue": "name 'tensorflow' is not defined",
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      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-bbd2316c42a5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'tensorflow' is not defined"
     ]
    }
   ],
   "source": [
    "tensorflow.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow是什么\n",
    "\n",
    "Tensorflow是一个Google开发的第二代机器学习系统，克服了第一代系统DistBelief仅能开发神经网络算法、难以配置、依赖Google内部硬件等局限性，应用更加广泛，并且提高了灵活性和可移植性，速度和扩展性也有了大幅提高。字面上理解，TensorFlow就是以张量（Tensor）在计算图（Graph）上流动（Flow）的方式的实现和执行机器学习算法的框架。具有以下特点：\n",
    "\n",
    "* 灵活性。TensorFlow不是一个严格的“神经网络”库。只要可以将计算表示成数据流图，就可以使用TensorFlow，比如科学计算中的偏微分求解等。（实际上其官网的介绍中对TF的定位就是基于数据流图的科学计算库，而非仅仅是机器学习库）\n",
    "* 可移植性。同一份代码几乎不经过修改既可以部署到有任意数量CPU、GPU或TPU（Tensor Processing Unit，Google专门为机器学习开发的处理器）的PC、服务器或移动设备上。\n",
    "* 自动求微分。同Theano一样，TensorFlow也支持自动求微分，用户不需要再通过反向传播求解梯度。\n",
    "* 多语言支持。TensorFlow官方支持Python、C++、Go和Java接口，用户可以在硬件配置较好的机器中用Python进行实验，在资源较紧张或需要低延迟的环境中用C++进行部署。\n",
    "* 性能。虽然TensorFlow最开始发布时仅支持单机，在性能评测上并不出色，但是凭借Google强大的开发实力，TensorFlow性能已经追上了其他框架"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Google第一代分布式机器学习框架DistBelief在内部大规模使用后没有选择开源，而第二代TensorFlow于2015年11月在GitHub上开源，并在持续快速开发迭代中。TensorFlow最早由Google Brain的工程师开发，设计初衷是加速机器学习的研究，并快速地将研究原型转化为产品。Google选择开源TensorFlow的原因很简单：第一是希望借助社区的力量，大家一起完善TensorFlow。第二是回馈社区，Google希望让这个优秀的工具得到更多的应用，提高学术界和工业界使用机器学习的效率。\n",
    "\n",
    "自从2015年11月开源以来，TensorFlow迅速在众多的机器学习框架中脱颖而出，在Github上获得了最多的Star.\n"
   ]
  },
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   "metadata": {},
   "source": [
    "<img src=\"./img/1.png\" alt=\"FAO\" width=\"790\">\n"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"./img/2.png\" alt=\"FAO\" width=\"690\">\n",
    "<img src=\"./img/3.png\" alt=\"FAO\" width=\"690\">"
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     "text": [
      "[[ 2.]]\n"
     ]
    }
   ],
   "source": [
    "a = 3\n",
    "# 创建一个变量\n",
    "w = tf.Variable([[0.5,1.0]])\n",
    "x = tf.Variable([[2.0],[1.0]]) \n",
    "# 创建一个操作\n",
    "y = tf.matmul(w, x)  \n",
    "\n",
    "\n",
    "#全局变量初始化\n",
    "init_op = tf.global_variables_initializer()\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init_op)\n",
    "    print (y.eval())"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tensorflow很多操作跟numpy有些类似的\n",
    "\n",
    "* tf.zeros([3, 4], int32) ==> [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]\n",
    "\n",
    "* tf.zeros_like(tensor) ==> [[0, 0, 0], [0, 0, 0]]\n",
    "\n",
    "* tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]]\n",
    "\n",
    "* tf.ones_like(tensor) ==> [[1, 1, 1], [1, 1, 1]]\n",
    "\n",
    "* tensor = tf.constant([1, 2, 3, 4, 5, 6, 7]) => [1 2 3 4 5 6 7]\n",
    "\n",
    "* tensor = tf.constant(-1.0, shape=[2, 3]) => [[-1. -1. -1.]                                              [-1. -1. -1.]]\n",
    "* tf.linspace(10.0, 12.0, 3, name=\"linspace\") => [ 10.0  11.0  12.0]\n",
    "\n",
    "* tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "[[-5.58110332  0.84881377  7.51961231]\n",
      " [ 3.27404118 -7.22483826  7.70631599]]\n",
      "[[5 6]\n",
      " [1 2]\n",
      " [3 4]]\n"
     ]
    }
   ],
   "source": [
    "#生成的值服从具有指定平均值和标准偏差的正态分布\n",
    "norm = tf.random_normal([2, 3], mean=-1, stddev=4)\n",
    "\n",
    "# 洗牌\n",
    "c = tf.constant([[1, 2], [3, 4], [5, 6]])\n",
    "shuff = tf.random_shuffle(c)\n",
    "\n",
    "# 每一次执行结果都会不同\n",
    "sess = tf.Session()\n",
    "print (sess.run(norm))\n",
    "print (sess.run(shuff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "state = tf.Variable(0)\n",
    "new_value = tf.add(state, tf.constant(1))\n",
    "update = tf.assign(state, new_value)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    print(sess.run(state))    \n",
    "    for _ in range(3):\n",
    "        sess.run(update)\n",
    "        print(sess.run(state))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  0.]\n",
      " [ 0.  0.  0.]\n",
      " [ 0.  0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.zeros((3,3))\n",
    "ta = tf.convert_to_tensor(a)\n",
    "with tf.Session() as sess:\n",
    "     print(sess.run(ta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a = 5.0\n",
      "b = 10.0\n",
      "a + b = 15.0\n",
      "a/b = 0.5\n"
     ]
    }
   ],
   "source": [
    "a = tf.constant(5.0)\n",
    "b = tf.constant(10.0)\n",
    "\n",
    "x = tf.add(a, b, name=\"add\")\n",
    "y = tf.div(a, b, name=\"divide\")\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    print(\"a =\", sess.run(a))\n",
    "    print(\"b =\", sess.run(b))\n",
    "    print(\"a + b =\", sess.run(x))\n",
    "    print(\"a/b =\", sess.run(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([ 14.], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "input1 = tf.placeholder(tf.float32)\n",
    "input2 = tf.placeholder(tf.float32)\n",
    "output = tf.multiply(input1, input2)\n",
    "with tf.Session() as sess:\n",
    "    print(sess.run([output], feed_dict={input1:[7.], input2:[2.]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
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