tensorflow/tf_basic_train.ipynb

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   "source": [
    "# Linear model training\n",
    "\n",
    "* https://www.tensorflow.org/get_started/get_started\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
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   "source": [
    "import tensorflow as tf\n",
    "\n",
    "# create model\n",
    "# Model parameters\n",
    "W = tf.Variable([.3], dtype=tf.float32)\n",
    "b = tf.Variable([-.3], dtype=tf.float32)\n",
    "# Model input and output\n",
    "x = tf.placeholder(tf.float32)\n",
    "linear_model = W*x + b\n",
    "y = tf.placeholder(tf.float32)\n",
    "\n",
    "# creat loss\n",
    "loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "text": [
      "W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11\n"
     ]
    }
   ],
   "source": [
    "# optimizer\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.01) # learning_rate=0.01\n",
    "train = optimizer.minimize(loss)\n",
    "\n",
    "# training data set\n",
    "x_train = [1, 2, 3, 4]\n",
    "y_train = [0, -1, -2, -3]\n",
    "\n",
    "\n",
    "# training loop\n",
    "init = tf.global_variables_initializer()\n",
    "sess = tf.Session()\n",
    "sess.run(init) # reset values to wrong\n",
    "for i in range(1000): # train 1000 times\n",
    "    sess.run(train, {x: x_train, y: y_train})\n",
    "\n",
    "# evaluate training accuracy\n",
    "curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train})\n",
    "print(\"W: %s b: %s loss: %s\"%(curr_W, curr_b, curr_loss))\n"
   ]
  },
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   "execution_count": null,
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