Setup notebook testing (tensorflow#2160)

* Setup notebook testing

* Trivial change

* Run on all notebooks

* Format networks_seq2seq_nmt.ipynb

* Lint all notebooks

* Change repo name

* Expose template to be formatted

Author: WindQAQ

.github/workflows/ci_test.yml

@@ -95,3 +95,32 @@ jobs:
       - run: pip install pygithub click
       - name: Check that the CODEOWNERS is valid
         run: python .github/workflows/notify_codeowners.py .github/CODEOWNERS
+  nbfmt:
+    name: Notebook format
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/setup-python@v1
+    - uses: actions/checkout@v2
+    - name: Install tensorflow-docs
+      run: python3 -m pip install -U git+https://github.com/tensorflow/docs
+    - name: Check notebook formatting
+      run: |
+        # Run on all notebooks to prevent upstream change.
+        echo "Check formatting with nbfmt:"
+        python3 -m tensorflow_docs.tools.nbfmt --test \
+            $(find docs/tutorials/ -type f -name *.ipynb)
+  nblint:
+    name: Notebook lint
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/setup-python@v1
+    - uses: actions/checkout@v2
+    - name: Install tensorflow-docs
+      run: python3 -m pip install -U git+https://github.com/tensorflow/docs
+    - name: Lint notebooks
+      run: |
+        # Run on all notebooks to prevent upstream change.
+        echo "Lint check with nblint:"
+        python3 -m tensorflow_docs.tools.nblint \
+            --arg=repo:tensorflow/addons \
+            $(find docs/tutorials/ -type f -name *.ipynb ! -path "docs/tutorials/_template.ipynb")

docs/tutorials/_template.ipynb

@@ -57,7 +57,7 @@
         "    <a target=\"_blank\" href=\"https://github.com/tensorflow/addons/blob/master/docs/tutorials/_template.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
         "  </td>\n",
         "      <td>\n",
-        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/docs/tutorials/_template.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
+        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/addons/docs/tutorials/_template.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
         "  </td>\n",
         "</table>"
       ]

docs/tutorials/image_ops.ipynb

@@ -64,7 +64,7 @@
         "## Overview\n",
         "This notebook will demonstrate how to use the some image operations in TensorFlow Addons.\n",
         "\n",
-        "Here is the list of image operations we'll be covering in this example:\n",
+        "Here is the list of image operations you'll be covering in this example:\n",
         "\n",
         "- `tfa.image.mean_filter2d`\n",
         "\n",

docs/tutorials/layers_weightnormalization.ipynb

@@ -78,7 +78,7 @@
         "\n",
         "Tim Salimans, Diederik P. Kingma (2016)\n",
         "\n",
-        "> By reparameterizing the weights in this way we improve the conditioning of the optimization problem and we speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time.\n",
+        "> By reparameterizing the weights in this way you improve the conditioning of the optimization problem and speed up convergence of stochastic gradient descent. Our reparameterization is inspired by batch normalization but does not introduce any dependencies between the examples in a minibatch. This means that our method can also be applied successfully to recurrent models such as LSTMs and to noise-sensitive applications such as deep reinforcement learning or generative models, for which batch normalization is less well suited. Although our method is much simpler, it still provides much of the speed-up of full batch normalization. In addition, the computational overhead of our method is lower, permitting more optimization steps to be taken in the same amount of time.\n",
         "\n",
         "> https://arxiv.org/abs/1602.07868 \n",
         "\n",

docs/tutorials/losses_triplet.ipynb

@@ -84,7 +84,7 @@
         "\n",
         "![function](https://user-images.githubusercontent.com/18154355/61484709-7589b800-a96d-11e9-9c3c-e880514af4b7.png)\n",
         "\n",
-        "Where A is our anchor input,  P is the positive sample input,  N is the negative sample input, and alpha is some margin we use to specify when a triplet has become too \"easy\" and we no longer want to adjust the weights from it."
+        "Where A is our anchor input,  P is the positive sample input,  N is the negative sample input, and alpha is some margin you use to specify when a triplet has become too \"easy\" and you no longer want to adjust the weights from it."
       ]
     },
     {
@@ -94,7 +94,7 @@
       },
       "source": [
         "## SemiHard Online Learning\n",
-        "As shown in the paper, the best results are from triplets known as \"Semi-Hard\". These are defined as triplets where the negative is farther from the anchor than the positive, but still produces a positive loss. To efficiently find these triplets we utilize online learning and only train from the Semi-Hard examples in each batch. \n"
+        "As shown in the paper, the best results are from triplets known as \"Semi-Hard\". These are defined as triplets where the negative is farther from the anchor than the positive, but still produces a positive loss. To efficiently find these triplets you utilize online learning and only train from the Semi-Hard examples in each batch. \n"
       ]
     },
     {