Merge branch 'master' into patch-3

Author: 8bitmp3

site/en/community/sig_playbook.md

@@ -55,7 +55,7 @@ must demonstrate:
     application area)
 *   Two or more contributors willing to act as group leads, existence of other
     contributors, and evidence of demand for the group
-*   Resources it will initially require (usually, mailing list and regular VC
+*   Resources it will initially require (usually, mailing list and regular video conference 
     call.) 
 
 Approval for the group will be given by a decision of the TF Community Team,

site/en/guide/_toc.yaml

@@ -19,6 +19,8 @@ toc:
   path: /guide/basic_training_loops
 
 - heading: "Keras"
+- title: "Overview"
+  path: /guide/keras
 - include: /guide/keras/_toc.yaml
 
 - heading: "Build with Core"

site/en/guide/basics.ipynb

@@ -956,7 +956,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "basics.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {

site/en/guide/core/distribution.ipynb

@@ -13,6 +13,7 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
+        "cellView": "form",
         "id": "AwOEIRJC6Une"
       },
       "outputs": [],
@@ -337,9 +338,7 @@
         "\n",
         "Create a DTensor mesh that consists of a single batch dimension, where each device becomes a replica that receives a shard from the global batch. Use this mesh to instantiate an MLP mode with the following architecture:\n",
         "\n",
-        "Forward Pass: ReLU(784 x 700) x ReLU(700 x 500) x Softmax(500 x 10)\n",
-        "\n",
-        "\n"
+        "Forward Pass: ReLU(784 x 700) x ReLU(700 x 500) x Softmax(500 x 10)\n"
       ]
     },
     {
@@ -486,7 +485,7 @@
         "    queue = tf.nest.map_structure(lambda x: tf.split(x, num_splits, axis=axis), queue)\n",
         "    sharded_dims.append(dim)\n",
         "\n",
-        "  # Now we can build the list of component tensors by looking up the location in\n",
+        "  # Now you can build the list of component tensors by looking up the location in\n",
         "  # the nested list of split-tensors created in queue[0].\n",
         "  components = []\n",
         "  for locations in layout.mesh.local_device_locations():\n",
@@ -663,8 +662,7 @@
       "source": [
         "## Saving your model\n",
         "\n",
-        "The integration of `tf.saved_model` and DTensor is still under development. As of TensorFlow 2.9.0, tf.saved_model only accepts DTensor models with fully replicated variables. As a workaround, you can convert a DTensor model to a fully replicated one by reloading a checkpoint. However, after a model is saved, all DTensor annotations are lost and the saved signatures can only be used with regular Tensors. This tutorial will be updated to showcase the integration once it is solidified.\n",
-        "\n"
+        "The integration of `tf.saved_model` and DTensor is still under development. As of TensorFlow 2.9.0, tf.saved_model only accepts DTensor models with fully replicated variables. As a workaround, you can convert a DTensor model to a fully replicated one by reloading a checkpoint. However, after a model is saved, all DTensor annotations are lost and the saved signatures can only be used with regular Tensors. This tutorial will be updated to showcase the integration once it is solidified.\n"
       ]
     },
     {
@@ -690,7 +688,6 @@
         "FhGuhbZ6M5tl"
       ],
       "name": "distribution.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {

site/en/guide/core/logistic_regression_core.ipynb

@@ -769,7 +769,7 @@
         "  # Compute the confusion matrix and normalize it\n",
         "  plt.figure(figsize=(10,10))\n",
         "  confusion = sk_metrics.confusion_matrix(y.numpy(), y_classes.numpy())\n",
-        "  confusion_normalized = confusion / confusion.sum(axis=1)\n",
+        "  confusion_normalized = confusion / confusion.sum(axis=1, keepdims=True)\n",
         "  axis_labels = range(2)\n",
         "  ax = sns.heatmap(\n",
         "      confusion_normalized, xticklabels=axis_labels, yticklabels=axis_labels,\n",

site/en/guide/core/matrix_core.ipynb

@@ -13,6 +13,7 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
+        "cellView": "form",
         "id": "AwOEIRJC6Une"
       },
       "outputs": [],
@@ -718,7 +719,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "matrix_core.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {

site/en/guide/core/mlp_core.ipynb

@@ -13,6 +13,7 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
+        "cellView": "form",
         "id": "AwOEIRJC6Une"
       },
       "outputs": [],
@@ -817,7 +818,6 @@
         "id": "_zZxO8iqBGZ-"
       },
       "source": [
-        "\n",
         "Load the saved model with `tf.saved_model.load` and examine its performance on the unseen test data."
       ]
     },
@@ -952,10 +952,8 @@
         "FhGuhbZ6M5tl"
       ],
       "name": "mlp_core.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
-    "gpuClass": "standard",
     "kernelspec": {
       "display_name": "Python 3",
       "name": "python3"

site/en/guide/core/quickstart_core.ipynb

@@ -579,7 +579,6 @@
         "rX8mhOLljYeM"
       ],
       "name": "quickstart_core.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {

site/en/guide/data_performance.ipynb

@@ -1153,7 +1153,6 @@
     "colab": {
       "collapsed_sections": [],
       "name": "data_performance.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {

site/en/guide/estimator.ipynb

@@ -869,7 +869,6 @@
         "A_lvUsSLZzVg"
       ],
       "name": "estimator.ipynb",
-      "provenance": [],
       "toc_visible": true
     },
     "kernelspec": {