01_linear_regression.ipynb

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   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
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    "trX = np.linspace(-1, 1, 101)\n",
    "trY = 2 * trX + np.random.randn(*trX.shape) * 0.33 # create a y value which is approximately linear but with some random noise"
   ]
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    "X = tf.placeholder(\"float\") # create symbolic variables\n",
    "Y = tf.placeholder(\"float\")\n",
    "\n",
    "def model(X, w):\n",
    "    return tf.multiply(X, w) # lr is just X*w so this model line is pretty simple\n",
    "\n",
    "w = tf.Variable(0.0, name=\"weights\") # create a shared variable (like theano.shared) for the weight matrix\n",
    "y_model = model(X, w)\n",
    "\n",
    "cost = tf.square(Y - y_model) # use square error for cost function\n",
    "\n",
    "train_op = tf.train.GradientDescentOptimizer(0.01).minimize(cost) # construct an optimizer to minimize cost and fit line to my data"
   ]
  },
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     "text": [
      "2.00863\n"
     ]
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   "source": [
    "# Launch the graph in a session\n",
    "with tf.Session() as sess:\n",
    "    # you need to initialize variables (in this case just variable W)\n",
    "    tf.global_variables_initializer().run()\n",
    "\n",
    "    for i in range(100):\n",
    "        for (x, y) in zip(trX, trY):\n",
    "            sess.run(train_op, feed_dict={X: x, Y: y})\n",
    "\n",
    "    print(sess.run(w))  # It should be something around 2"
   ]
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   "execution_count": null,
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