16_tf_basic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 创建计算图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1 创建常数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = tf.constant(2, name='c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'c:0' shape=() dtype=int32>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2 创建变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "var = tf.Variable(c + 5, name='var')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Variable 'var:0' shape=() dtype=int32_ref>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 执行计算图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c: 2\n",
      "var: 7\n"
     ]
    }
   ],
   "source": [
    "print('c:', sess.run(c))\n",
    "print('var:', sess.run(var))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c: 2\n",
      "var: 7\n"
     ]
    }
   ],
   "source": [
    "print('c:', c.eval(session=sess))\n",
    "print('var:', var.eval(session=sess))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 使用 with 语句打开 Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c: 2\n",
      "var: 7\n"
     ]
    }
   ],
   "source": [
    "c = tf.constant(2, name='c')\n",
    "var = tf.Variable(c + 5, name='var')\n",
    "with tf.Session() as sess:\n",
    "    init = tf.global_variables_initializer()\n",
    "    sess.run(init)\n",
    "    print('c:', sess.run(c))\n",
    "    print('var:', sess.run(var))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. placeholder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "width = tf.placeholder('int32')\n",
    "height = tf.placeholder('int32')\n",
    "area = tf.multiply(width, height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "area: 48\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    init = tf.global_variables_initializer()\n",
    "    sess.run(init)\n",
    "    print('area:', sess.run(area, feed_dict={width: 6, height: 8}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. 创建多维 tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1 创建一维 tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4 0.2 0.4]\n"
     ]
    }
   ],
   "source": [
    "var = tf.Variable([0.4, 0.2, 0.4])\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    result = sess.run(var)\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n"
     ]
    }
   ],
   "source": [
    "print(result.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2 创建二维 tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.4 0.2 0.4]]\n"
     ]
    }
   ],
   "source": [
    "var = tf.Variable([[0.4, 0.2, 0.4]])\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    result = sess.run(var)\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3)\n"
     ]
    }
   ],
   "source": [
    "print(result.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.5 -0.2]\n",
      " [-0.3  0.4]\n",
      " [-0.5  0.2]]\n"
     ]
    }
   ],
   "source": [
    "W = tf.Variable([\n",
    "    [-0.5, -0.2],\n",
    "    [-0.3, 0.4],\n",
    "    [-0.5, 0.2]\n",
    "])\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    result = sess.run(W)\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 2)\n"
     ]
    }
   ],
   "source": [
    "print(result.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. 矩阵基本运算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1 矩阵乘法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "product: [[-1.3  0.4]]\n"
     ]
    }
   ],
   "source": [
    "X = tf.Variable([[1.0, 1.0, 1.0]])\n",
    "W = tf.Variable([\n",
    "    [-0.5, -0.2],\n",
    "    [-0.3, 0.4],\n",
    "    [-0.5, 0.2]\n",
    "])\n",
    "product = tf.matmul(X, W)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    print(\"product:\", sess.run(product))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.2 矩阵加法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "result: [[-1.1999999  0.6      ]]\n"
     ]
    }
   ],
   "source": [
    "b = tf.Variable([[0.1, 0.2]])\n",
    "product = tf.Variable([[-1.3, 0.4]])\n",
    "result = product + b\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    print('result:', sess.run(result))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.3 矩阵乘法与加法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "result: [[-1.1999999  0.6      ]]\n"
     ]
    }
   ],
   "source": [
    "X = tf.Variable([[1.0, 1.0, 1.0]])\n",
    "W = tf.Variable([\n",
    "    [-0.5, -0.2],\n",
    "    [-0.3, 0.4],\n",
    "    [-0.5, 0.2]\n",
    "])\n",
    "b = tf.Variable([[0.1, 0.2]])\n",
    "result = tf.matmul(X, W) + b\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    print('result:', sess.run(result))"
   ]
  }
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