Fix notebooks.

PiperOrigin-RevId: 305891180
jobpeters · Apr 14, 2020 · e540ee6 · e540ee6
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commit e540ee6
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Showing 5 changed files with 26 additions and 36 deletions.
diff --git a/examples/colab/biggan_generation_with_tf_hub.ipynb b/examples/colab/biggan_generation_with_tf_hub.ipynb
@@ -155,7 +155,6 @@
         "import numpy as np\n",
         "import PIL.Image\n",
         "from scipy.stats import truncnorm\n",
-        "import tensorflow as tf\n",
         "import tensorflow_hub as hub"
       ]
     },

diff --git a/examples/colab/cord_19_embeddings.ipynb b/examples/colab/cord_19_embeddings.ipynb
@@ -75,7 +75,7 @@
       "cell_type": "markdown",
       "metadata": {
         "colab_type": "text",
-        "id": "ORy-KvWXGXBo"
+        "id": "9VusdTAH0isl"
       },
       "source": [
         "The CORD-19 Swivel text embedding module from TF-Hub (https://tfhub.dev/tensorflow/cord-19/swivel-128d/1)\n",
@@ -88,11 +88,11 @@
         "- Train a classifier on the SciCite dataset using the CORD-19 embeddings\n"
       ]
     },
-        {
+    {
       "cell_type": "markdown",
       "metadata": {
         "colab_type": "text",
-        "id": "ORy-KvWXGXBo"
+        "id": "L69VQv2Z0isl"
       },
       "source": [
         "## Setup\n"
@@ -122,8 +122,7 @@
         "import tensorflow_datasets as tfds\n",
         "import tensorflow_hub as hub\n",
         "\n",
-        "from tqdm import trange\n",
-        "from google.colab import data_table"
+        "from tqdm import"
       ]
     },
     {
@@ -511,13 +510,12 @@
       "outputs": [],
       "source": [
         "first_10_predictions = list(itertools.islice(predictions, 10))\n",
-        "data_table.DataTable(\n",
-        "    pd.DataFrame({\n",
-        "        TEXT_FEATURE_NAME: [pred['features'].decode('utf8') for pred in first_10_predictions],\n",
-        "        LABEL_NAME: [THE_DATASET.class_names()[pred['labels']] for pred in first_10_predictions],\n",
-        "        'prediction': [THE_DATASET.class_names()[pred['predictions']] for pred in first_10_predictions]\n",
-        "    }),\n",
-        "    include_index=False)"
+        "\n",
+        "pd.DataFrame({\n",
+        "    TEXT_FEATURE_NAME: [pred['features'].decode('utf8') for pred in first_10_predictions],\n",
+        "    LABEL_NAME: [THE_DATASET.class_names()[pred['labels']] for pred in first_10_predictions],\n",
+        "    'prediction': [THE_DATASET.class_names()[pred['predictions']] for pred in first_10_predictions]\n",
+        "})"
       ]
     },
     {

diff --git a/examples/colab/cord_19_embeddings_keras.ipynb b/examples/colab/cord_19_embeddings_keras.ipynb
@@ -75,7 +75,7 @@
       "cell_type": "markdown",
       "metadata": {
         "colab_type": "text",
-        "id": "ORy-KvWXGXBo"
+        "id": "yI6Mh3-P0_Pk"
       },
       "source": [
         "The CORD-19 Swivel text embedding module from TF-Hub (https://tfhub.dev/tensorflow/cord-19/swivel-128d/2)\n",
@@ -92,7 +92,7 @@
       "cell_type": "markdown",
       "metadata": {
         "colab_type": "text",
-        "id": "ORy-KvWXGXBo"
+        "id": "gVWOrccw0_Pl"
       },
       "source": [
         "## Setup\n"
@@ -120,8 +120,7 @@
         "import tensorflow_datasets as tfds\n",
         "import tensorflow_hub as hub\n",
         "\n",
-        "from tqdm import trange\n",
-        "from google.colab import data_table"
+        "from tqdm import trange"
       ]
     },
     {
@@ -232,12 +231,11 @@
         "\n",
         "data = next(iter(train_data.batch(NUM_EXAMPLES)))\n",
         "\n",
-        "data_table.DataTable(\n",
-        "    pd.DataFrame({\n",
-        "        TEXT_FEATURE_NAME: [ex.numpy().decode('utf8') for ex in data[0]],\n",
-        "        LABEL_NAME: [label2str(x) for x in data[1]]\n",
-        "    }),\n",
-        "    include_index=False)"
+        "\n",
+        "pd.DataFrame({\n",
+        "    TEXT_FEATURE_NAME: [ex.numpy().decode('utf8') for ex in data[0]],\n",
+        "    LABEL_NAME: [label2str(x) for x in data[1]]\n",
+        "})"
       ]
     },
     {
@@ -405,13 +403,12 @@
         "\n",
         "predictions = [label2str(x) for x in model.predict_classes(prediction_texts)]\n",
         "\n",
-        "data_table.DataTable(\n",
-        "    pd.DataFrame({\n",
-        "        TEXT_FEATURE_NAME: prediction_texts,\n",
-        "        LABEL_NAME: prediction_labels,\n",
-        "        'prediction': predictions\n",
-        "    }),\n",
-        "    include_index=False)"
+        "\n",
+        "pd.DataFrame({\n",
+        "    TEXT_FEATURE_NAME: prediction_texts,\n",
+        "    LABEL_NAME: prediction_labels,\n",
+        "    'prediction': predictions\n",
+        "})"
       ]
     },
     {
@@ -444,9 +441,7 @@
   ],
   "metadata": {
     "colab": {
-      "collapsed_sections": [
-        "5wFF5JFyD2Ki"
-      ],
+      "collapsed_sections": [],
       "name": "Exploring the TF-Hub CORD-19 Swivel Embeddings",
       "private_outputs": true,
       "provenance": [],

diff --git a/examples/colab/retrieval_with_tf_hub_universal_encoder_qa.ipynb b/examples/colab/retrieval_with_tf_hub_universal_encoder_qa.ipynb
@@ -237,7 +237,6 @@
       "outputs": [],
       "source": [
         "#@title Download and extract SQuAD data\n",
-        "%%time\n",
         "squad_url = 'https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json' #@param [\"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json\", \"https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json\", \"https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json\", \"https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json\"]\n",
         "\n",
         "squad_json = download_squad(squad_url)\n",

diff --git a/examples/colab/semantic_similarity_with_tf_hub_universal_encoder.ipynb b/examples/colab/semantic_similarity_with_tf_hub_universal_encoder.ipynb
@@ -133,8 +133,7 @@
         "#@title Load the Universal Sentence Encoder's TF Hub module\n",
         "from absl import logging\n",
         "\n",
-        "import tensorflow.compat.v1 as tf\n",
-        "tf.disable_v2_behavior()\n",
+        "import tensorflow as tf\n",
         "\n",
         "import tensorflow_hub as hub\n",
         "import matplotlib.pyplot as plt\n",