Modify tf2 linear regression loss function (#371)

tensorflow_v2/notebooks/2_BasicModels/linear_regression.ipynb

@@ -56,8 +56,7 @@
     "X = np.array([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n",
     "              7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n",
     "Y = np.array([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n",
-    "              2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n",
-    "n_samples = X.shape[0]"
+    "              2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n"
    ]
   },
   {
@@ -76,7 +75,7 @@
     "\n",
     "# Mean square error.\n",
     "def mean_square(y_pred, y_true):\n",
-    "    return tf.reduce_sum(tf.pow(y_pred-y_true, 2)) / (2 * n_samples)\n",
+    "    return tf.reduce_mean(tf.square(y_pred - y_true))\n",
     "\n",
     "# Stochastic Gradient Descent Optimizer.\n",
     "optimizer = tf.optimizers.SGD(learning_rate)"