1_linear_regression_model.ipynb

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    "# pip3 install matplotlib\n",
    "# 1.1 Import tensorflow and other libraries.\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
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    "# 1.2 Create input data using NumPy. y = x * 0.1 + 0.3 + noise\n",
    "x_data = np.random.rand(100).astype(np.float32)\n",
    "noise = np.random.normal(scale=0.01, size=len(x_data))\n",
    "y_data = x_data * 0.1 + 0.3 + noise\n",
    "\n",
    "# Uncomment the following line to plot our input data.\n",
    "#plt.plot(x_data,y_data,'b.')\n",
    "#plt.show()"
   ]
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    "# 1.3 Buld inference graph.\n",
    "# Create Variables W and b that compute y_data = W * x_data + b\n",
    "W = tf.Variable(tf.random_uniform([1], 0.0, 1.0))\n",
    "b = tf.Variable(tf.zeros([1]))\n",
    "y = W * x_data + b\n",
    "\n",
    "# Uncomment the following lines to see W and b are.\n",
    "#print(W)\n",
    "#print(b)"
   ]
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    "# 1.4 Build training graph.\n",
    "loss = tf.reduce_mean(tf.square(y - y_data))  # Create an operation that calculates loss.\n",
    "optimizer = tf.train.GradientDescentOptimizer(0.5)  # Create an optimizer.\n",
    "train = optimizer.minimize(loss)  # Create an operation that minimizes loss.\n",
    "init = tf.global_variables_initializer()  # Create an operation initializes all the variables.\n",
    "\n",
    "# Uncomment the following 3 lines to see what 'loss', 'optimizer' and 'train' are.\n",
    "#print(\"loss:\", loss)\n",
    "#print(\"optimizer:\", optimizer)\n",
    "#print(\"train:\", train)\n",
    "#print(init)"
   ]
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    "# 1.5 Uncomment the following line to see what we have built.\n",
    "#grph = tf.summary.FileWriter(\"graph\",graph=tf.Session().graph)\n",
    "#grph.close()"
   ]
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    "# 1.6 Create a session and launch the graph.\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "y_initial_values = sess.run(y)  # Save initial values for plotting later.\n",
    "\n",
    "# Uncomment the following line to see the initial W and b values.\n",
    "#print(sess.run([W, b]))"
   ]
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   "execution_count": 15,
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     "text": [
      "[array([ 0.10040134], dtype=float32), array([ 0.30125529], dtype=float32)]\n"
     ]
    }
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   "source": [
    "# 1.7 Perform training.\n",
    "for step in range(201):\n",
    "    sess.run(train)\n",
    "    # Uncomment the following two lines to watch training happen real time.\n",
    "    # if step % 20 == 0:\n",
    "    #    print(step, sess.run([W, b]))\n",
    "\n",
    "print(sess.run([W, b]))"
   ]
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   "source": [
    "# 1.8 Uncomment the following lines to compare.\n",
    "#plt.plot(x_data,y_data,'b.')\n",
    "#plt.plot(x_data,y_initial_values,'y.')\n",
    "#plt.plot(x_data,sess.run(y),'r.')\n",
    "#plt.legend(['y = target_values', 'y = initial_values', 'y = trained_values'], loc='lower right')\n",
    "#plt.show()"
   ]
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   "source": [
    "<p>Back to [0_tf_hello_world.ipynb](0_tf_hello_world.ipynb).</p>\n",
    "<p>Next to [2_mnist.ipynb](2_mnist.ipynb).</p>"
   ]
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