Merge pull request #257 from Matanley/patch-1

Fix typo

Author: abhiojha8

batch-norm/Batch_Normalization_Lesson.ipynb

@@ -125,7 +125,7 @@
     "            followed by a hidden layer with 100 nodes, followed by an output layer with 10 nodes.\n",
     "        :param activation_fn: Callable\n",
     "            The function used for the output of each hidden layer. The network will use the same\n",
-    "            activation function on every hidden layer and no activate function on the output layer.\n",
+    "            activation function on every hidden layer and no activation function on the output layer.\n",
     "            e.g. Pass tf.nn.relu to use ReLU activations on your hidden layers.\n",
     "        :param use_batch_norm: bool\n",
     "            Pass True to create a network that uses batch normalization; False otherwise\n",
@@ -278,7 +278,7 @@
     "            if False, perform inference with batch normalization using estimated population mean and variance.\n",
     "            Note: in real life, *always* perform inference using the population mean and variance.\n",
     "                  This parameter exists just to support demonstrating what happens if you don't.\n",
-    "        :param include_individual_predictions: bool (default True)\n",
+    "        :param include_individual_predictions: bool (default False)\n",
     "            This function always performs an accuracy test against the entire test set. But if this parameter\n",
     "            is True, it performs an extra test, doing 200 predictions one at a time, and displays the results\n",
     "            and accuracy.\n",
@@ -1806,8 +1806,8 @@
     "            train_mean = tf.assign(pop_mean, pop_mean * decay + batch_mean * (1 - decay))\n",
     "            train_variance = tf.assign(pop_variance, pop_variance * decay + batch_variance * (1 - decay))\n",
     "\n",
-    "            # The 'tf.control_dependencies' context tells TensorFlow it must calculate 'train_mean' \n",
-    "            # and 'train_variance' before it calculates the 'tf.nn.batch_normalization' layer.\n",
+    "            # The `tf.control_dependencies` context tells TensorFlow it must calculate `train_mean` \n",
+    "            # and `train_variance` before it calculates the `tf.nn.batch_normalization` layer.\n",
     "            # This is necessary because the those two operations are not actually in the graph\n",
     "            # connecting the linear_output and batch_normalization layers, \n",
     "            # so TensorFlow would otherwise just skip them.\n",