Merge branch 'master' into patch-19

Author: 8bitmp3

site/en/guide/migrate/metrics_optimizers.ipynb

@@ -144,7 +144,7 @@
         "\n",
         "def _model_fn(features, labels, mode):\n",
         "  logits = tf1.layers.Dense(2)(features)\n",
-        "  predictions = tf.argmax(input=logits, axis=1)\n",
+        "  predictions = tf.math.argmax(input=logits, axis=1)\n",
         "  loss = tf1.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits)\n",
         "  optimizer = tf1.train.AdagradOptimizer(0.05)\n",
         "  train_op = optimizer.minimize(loss, global_step=tf1.train.get_global_step())\n",
@@ -223,7 +223,7 @@
         "\n",
         "inputs = tf.keras.Input((2,))\n",
         "logits = tf.keras.layers.Dense(2)(inputs)\n",
-        "predictions = tf.argmax(input=logits, axis=1)\n",
+        "predictions = tf.math.argmax(input=logits, axis=1)\n",
         "model = tf.keras.models.Model(inputs, predictions)\n",
         "optimizer = tf.keras.optimizers.Adagrad(learning_rate=0.05)\n",
         "\n",

site/en/guide/migrate/upgrade.ipynb

@@ -95,7 +95,7 @@
       "source": [
         "## Compatibility modules\n",
         "\n",
-        "Certain API symbols can not be upgraded simply by using a string replacement. Those that cannot be automatically upgraded will be mapped to their locations in the `compat.v1` module. This module replaces TF 1.x symbols like `tf.foo` with the equivalent `tf.compat.v1.foo` reference. If you are already using `compat.v1` APIs by importing TF via `import tensorflow.compat.v1 as tf`, the `tf_upgrade_v2` script will attempt to convert these usages to the non-compat APIs where possible. Note that while some `compat.v1` APIs are compatible with TF2.x behaviors, many are not. So, we recommend that you manually proofread replacements and migrate them to new APIs in the `tf.*` namespace instead of `tf.compat.v1` namespace as quickly as possible.\n",
+        "Certain API symbols can not be upgraded simply by using a string replacement. Those that cannot be automatically upgraded will be mapped to their locations in the `compat.v1` module. This module replaces TF 1.x symbols like `tf.foo` with the equivalent `tf.compat.v1.foo` reference. If you are already using `compat.v1` APIs by importing TF via `import tensorflow.compat.v1 as tf`, the `tf_upgrade_v2` script will attempt to convert these usages to the non-compat APIs where possible. Note that while some `compat.v1` APIs are compatible with TF2.x behaviors, many are not. Therefore, it's recommended to manually proofread replacements and migrate them to new APIs in the `tf.*` namespace instead of `tf.compat.v1` namespace as quickly as possible.\n",
         "\n",
         "Because of TensorFlow 2.x module deprecations (for example, `tf.flags` and `tf.contrib`), some changes can not be worked around by switching to `compat.v1`. Upgrading this code may require using an additional library (for example, [`absl.flags`](https://github.com/abseil/abseil-py)) or switching to a package in [tensorflow/addons](http://www.github.com/tensorflow/addons).\n"
       ]
@@ -574,7 +574,7 @@
       "source": [
         "## Caveats\n",
         "\n",
-        "- Do not update parts of your code manually before running this script. In particular, functions that have had reordered arguments like `tf.argmax` or `tf.batch_to_space` cause the script to incorrectly add keyword arguments that mismap your existing code.\n",
+        "- Do not update parts of your code manually before running this script. In particular, functions that have had reordered arguments like `tf.math.argmax` or `tf.batch_to_space` cause the script to incorrectly add keyword arguments that mismap your existing code.\n",
         "\n",
         "- The script assumes that `tensorflow` is imported using `import tensorflow as tf`, or `import tensorflow.compat.v1 as tf`.\n",
         "\n",

site/en/guide/ragged_tensor.ipynb

@@ -109,7 +109,7 @@
       "source": [
         "### What you can do with a ragged tensor\n",
         "\n",
-        "Ragged tensors are supported by more than a hundred TensorFlow operations, including math operations (such as `tf.add` and `tf.reduce_mean`), array operations (such as `tf.concat` and `tf.tile`), string manipulation ops (such as `tf.substr`), control flow operations (such as `tf.while_loop` and `tf.map_fn`), and many others:"
+        "Ragged tensors are supported by more than a hundred TensorFlow operations, including math operations (such as `tf.add` and `tf.reduce_mean`), array operations (such as `tf.concat` and `tf.tile`), string manipulation ops (such as `tf.strings.substr`), control flow operations (such as `tf.while_loop` and `tf.map_fn`), and many others:"
       ]
     },
     {

site/en/guide/tensor.ipynb

@@ -368,7 +368,7 @@
         "# Find the largest value\n",
         "print(tf.reduce_max(c))\n",
         "# Find the index of the largest value\n",
-        "print(tf.argmax(c))\n",
+        "print(tf.math.argmax(c))\n",
         "# Compute the softmax\n",
         "print(tf.nn.softmax(c))"
       ]

site/en/guide/variable.ipynb

@@ -166,7 +166,7 @@
       "source": [
         "print(\"A variable:\", my_variable)\n",
         "print(\"\\nViewed as a tensor:\", tf.convert_to_tensor(my_variable))\n",
-        "print(\"\\nIndex of highest value:\", tf.argmax(my_variable))\n",
+        "print(\"\\nIndex of highest value:\", tf.math.argmax(my_variable))\n",
         "\n",
         "# This creates a new tensor; it does not reshape the variable.\n",
         "print(\"\\nCopying and reshaping: \", tf.reshape(my_variable, [1,4]))"

site/en/install/lang_c.ipynb

@@ -81,15 +81,15 @@
         "id": "Vk--31hqIwSV"
       },
       "source": [
-        "## Nightly Libtensorflow C packages\n",
+        "## Nightly libtensorflow C packages\n",
         "\n",
-        "Libtensorflow packages are built nightly and uploaded to GCS for all supported\n",
+        "libtensorflow packages are built nightly and uploaded to GCS for all supported\n",
         "platforms. They are uploaded to the\n",
         "[libtensorflow-nightly GCS bucket](https://storage.googleapis.com/libtensorflow-nightly)\n",
         "and are indexed by operating system and date built. For MacOS and Linux shared\n",
-        "objects, we have a\n",
+        "objects, there is a\n",
         "[script](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/ci_build/builds/libtensorflow_nightly_symlink.sh)\n",
-        "that renames the .so files versioned to the current date copied into the\n",
+        "that renames the `.so` files versioned to the current date copied into the\n",
         "directory with the artifacts."
       ]
     },
@@ -123,32 +123,32 @@
         "id": "y50y01XUFVb2"
       },
       "source": [
-        "### Download & extract\n",
+        "### Download and extract\n",
         "\n",
         "<table>\n",
         "  <tr><th>TensorFlow C library</th><th>URL</th></tr>\n",
         "  <tr class=\"alt\"><td colspan=\"2\">Linux</td></tr>\n",
         "  <tr>\n",
         "    <td>Linux CPU only</td>\n",
-        "    <td class=\"devsite-click-to-copy\"><a href=\"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.7.0.tar.gz\">https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.7.0.tar.gz</a></td>\n",
+        "    <td class=\"devsite-click-to-copy\"><a href=\"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.8.0.tar.gz\">https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.8.0.tar.gz</a></td>\n",
         "  </tr>\n",
         "  <tr>\n",
         "    <td>Linux GPU support</td>\n",
-        "    <td class=\"devsite-click-to-copy\"><a href=\"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.7.0.tar.gz\">https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.7.0.tar.gz</a></td>\n",
+        "    <td class=\"devsite-click-to-copy\"><a href=\"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.8.0.tar.gz\">https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.8.0.tar.gz</a></td>\n",
         "  </tr>\n",
         "  <tr class=\"alt\"><td colspan=\"2\">macOS</td></tr>\n",
         "  <tr>\n",
         "    <td>macOS CPU only</td>\n",
-        "    <td class=\"devsite-click-to-copy\"><a href=\"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.7.0.tar.gz\">https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.7.0.tar.gz</a></td>\n",
+        "    <td class=\"devsite-click-to-copy\"><a href=\"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.8.0.tar.gz\">https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.8.0.tar.gz</a></td>\n",
         "  </tr>\n",
         "  <tr class=\"alt\"><td colspan=\"2\">Windows</td></tr>\n",
         "  <tr>\n",
         "    <td>Windows CPU only</td>\n",
-        "    <td class=\"devsite-click-to-copy\"><a href=\"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.7.0.zip\">https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.7.0.zip</a></td>\n",
+        "    <td class=\"devsite-click-to-copy\"><a href=\"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.8.0.zip\">https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.8.0.zip</a></td>\n",
         "  </tr>\n",
         "  <tr>\n",
         "    <td>Windows GPU only</td>\n",
-        "    <td class=\"devsite-click-to-copy\"><a href=\"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.7.0.zip\">https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.7.0.zip</a></td>\n",
+        "    <td class=\"devsite-click-to-copy\"><a href=\"https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.8.0.zip\">https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.8.0.zip</a></td>\n",
         "  </tr>\n",
         "</table>"
       ]
@@ -174,7 +174,7 @@
       "outputs": [],
       "source": [
         "%%bash\n",
-        "FILENAME=libtensorflow-cpu-linux-x86_64-2.7.0.tar.gz\n",
+        "FILENAME=libtensorflow-cpu-linux-x86_64-2.8.0.tar.gz\n",
         "wget -q --no-check-certificate https://storage.googleapis.com/tensorflow/libtensorflow/${FILENAME}\n",
         "sudo tar -C /usr/local -xzf ${FILENAME}"
       ]

site/en/tutorials/audio/simple_audio.ipynb

@@ -563,7 +563,7 @@
       "source": [
         "def get_spectrogram_and_label_id(audio, label):\n",
         "  spectrogram = get_spectrogram(audio)\n",
-        "  label_id = tf.argmax(label == commands)\n",
+        "  label_id = tf.math.argmax(label == commands)\n",
         "  return spectrogram, label_id"
       ]
     },

site/en/tutorials/audio/transfer_learning_audio.ipynb

@@ -286,7 +286,7 @@
       "source": [
         "scores, embeddings, spectrogram = yamnet_model(testing_wav_data)\n",
         "class_scores = tf.reduce_mean(scores, axis=0)\n",
-        "top_class = tf.argmax(class_scores)\n",
+        "top_class = tf.math.argmax(class_scores)\n",
         "inferred_class = class_names[top_class]\n",
         "\n",
         "print(f'The main sound is: {inferred_class}')\n",
@@ -736,7 +736,7 @@
       "outputs": [],
       "source": [
         "reloaded_results = reloaded_model(testing_wav_data)\n",
-        "cat_or_dog = my_classes[tf.argmax(reloaded_results)]\n",
+        "cat_or_dog = my_classes[tf.math.argmax(reloaded_results)]\n",
         "print(f'The main sound is: {cat_or_dog}')"
       ]
     },
@@ -758,7 +758,7 @@
       "outputs": [],
       "source": [
         "serving_results = reloaded_model.signatures['serving_default'](testing_wav_data)\n",
-        "cat_or_dog = my_classes[tf.argmax(serving_results['classifier'])]\n",
+        "cat_or_dog = my_classes[tf.math.argmax(serving_results['classifier'])]\n",
         "print(f'The main sound is: {cat_or_dog}')\n"
       ]
     },
@@ -805,13 +805,13 @@
         "# Run the model, check the output.\n",
         "scores, embeddings, spectrogram = yamnet_model(waveform)\n",
         "class_scores = tf.reduce_mean(scores, axis=0)\n",
-        "top_class = tf.argmax(class_scores)\n",
+        "top_class = tf.math.argmax(class_scores)\n",
         "inferred_class = class_names[top_class]\n",
         "top_score = class_scores[top_class]\n",
         "print(f'[YAMNet] The main sound is: {inferred_class} ({top_score})')\n",
         "\n",
         "reloaded_results = reloaded_model(waveform)\n",
-        "your_top_class = tf.argmax(reloaded_results)\n",
+        "your_top_class = tf.math.argmax(reloaded_results)\n",
         "your_inferred_class = my_classes[your_top_class]\n",
         "class_probabilities = tf.nn.softmax(reloaded_results, axis=-1)\n",
         "your_top_score = class_probabilities[your_top_class]\n",

site/en/tutorials/customization/custom_training_walkthrough.ipynb

@@ -475,7 +475,7 @@
       },
       "outputs": [],
       "source": [
-        "print(\"Prediction: {}\".format(tf.argmax(predictions, axis=1)))\n",
+        "print(\"Prediction: {}\".format(tf.math.argmax(predictions, axis=1)))\n",
         "print(\"    Labels: {}\".format(labels))"
       ]
     },
@@ -824,7 +824,7 @@
         "  # training=False is needed only if there are layers with different\n",
         "  # behavior during training versus inference (e.g. Dropout).\n",
         "  logits = model(x, training=False)\n",
-        "  prediction = tf.argmax(logits, axis=1, output_type=tf.int64)\n",
+        "  prediction = tf.math.argmax(logits, axis=1, output_type=tf.int64)\n",
         "  test_accuracy(prediction, y)\n",
         "\n",
         "print(\"Test set accuracy: {:.3%}\".format(test_accuracy.result()))"
@@ -895,7 +895,7 @@
         "predictions = model(predict_dataset, training=False)\n",
         "\n",
         "for i, logits in enumerate(predictions):\n",
-        "  class_idx = tf.argmax(logits).numpy()\n",
+        "  class_idx = tf.math.argmax(logits).numpy()\n",
         "  p = tf.nn.softmax(logits)[class_idx]\n",
         "  name = class_names[class_idx]\n",
         "  print(\"Example {} prediction: {} ({:4.1f}%)\".format(i, name, 100*p))"

site/en/tutorials/images/segmentation.ipynb

@@ -507,7 +507,7 @@
       "outputs": [],
       "source": [
         "def create_mask(pred_mask):\n",
-        "  pred_mask = tf.argmax(pred_mask, axis=-1)\n",
+        "  pred_mask = tf.math.argmax(pred_mask, axis=-1)\n",
         "  pred_mask = pred_mask[..., tf.newaxis]\n",
         "  return pred_mask[0]"
       ]