Incorporated CR feedback

Tutorials/CNTK_202_Language_Understanding.ipynb

@@ -51,7 +51,7 @@
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@@ -90,7 +90,7 @@
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@@ -185,7 +185,7 @@
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@@ -218,28 +218,11 @@
   },
   {
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+   "execution_count": null,
    "metadata": {
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-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3\n",
-      "(-1, 150)\n",
-      "[ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
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-   ],
+   "outputs": [],
    "source": [
     "# peek\n",
     "model = create_model()\n",
@@ -339,7 +322,7 @@
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@@ -354,7 +337,7 @@
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@@ -474,7 +457,7 @@
    "source": [
     "### Evaluating the model\n",
     "\n",
-    "Like the train() function, we also define a function to measure accuracy on a test set by computing the error over multiple minibatches of test data. For evaluating on a small sample read from a file, you can set a minibatch size reflecting the sample size and run the test_minibatch on that instance of data. To see how to evaluate a single sequence, we provide and instance later in the tutorial. "
+    "Like the train() function, we also define a function to measure accuracy on a test set by computing the error over multiple minibatches of test data. For evaluating on a small sample read from a file, you can set a minibatch size reflecting the sample size and run the test_minibatch on that instance of data. To see how to evaluate a single sequence, we provide an instance later in the tutorial. "
    ]
   },
   {
@@ -823,7 +806,6 @@
    },
    "outputs": [],
    "source": [
-    "if\n",
     "def create_model():\n",
     "    with default_options(initial_state=0.1):\n",
     "        return Sequential([\n",