深度学习框架Tensorflow学习与应用/02-4 tensorflow版helloworld示例-拟合线性函数的k和b.ipynb

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    "## 示例\n",
    "* np.random.rand(100)\n",
    "* sess.run([k,b])) # 注意：要加[] [k,b]  f否则 TypeError: input must be a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
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    "tags": []
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      "0 [0.05394897, 0.10015694]\n",
      "20 [0.1035346, 0.19808814]\n",
      "40 [0.10203086, 0.19890156]\n",
      "60 [0.101166844, 0.19936888]\n",
      "80 [0.100670435, 0.19963738]\n",
      "100 [0.10038522, 0.19979165]\n",
      "120 [0.100221336, 0.19988029]\n",
      "140 [0.100127175, 0.19993122]\n",
      "160 [0.10007307, 0.19996049]\n",
      "180 [0.10004198, 0.1999773]\n",
      "200 [0.10002413, 0.19998695]\n"
     ]
    }
   ],
   "source": [
    "# 使用numpy 生成100个随机点\n",
    "x_data = np.random.rand(100)\n",
    "y_data = x_data*0.1 + 0.2\n",
    "\n",
    "# 构造一个线性模型\n",
    "b = tf.Variable(0.)\n",
    "k = tf.Variable(0.)\n",
    "y = k*x_data + b\n",
    "\n",
    "# 二次代价函数\n",
    "loss = tf.reduce_mean(tf.square(y_data-y))\n",
    "# 定义一个梯度下降法进行训练优化器\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.2)\n",
    "# 最小化代价函数\n",
    "train = optimizer.minimize(loss)\n",
    "\n",
    "# 初始化变量\n",
    "init= tf.global_variables_initializer()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(init)\n",
    "    for step in range(201):\n",
    "        sess.run(train)\n",
    "        if step%20==0:  # 每20次打印一下\n",
    "            print(step, sess.run([k,b])) # TypeError: input must be a dictionary\n"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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