Merge pull request #2142 from synandi:patch-16

copybara-github · copybara-github · commit 00b7460c94e1 · 2022-10-25T13:06:22.000-07:00
PiperOrigin-RevId: 483755179
diff --git a/site/en/guide/migrate/canned_estimators.ipynb b/site/en/guide/migrate/canned_estimators.ipynb
@@ -67,15 +67,15 @@
       "source": [
         "Canned (or Premade) Estimators have traditionally been used in TensorFlow 1 as quick and easy ways to train models for a variety of typical use cases. TensorFlow 2 provides straightforward approximate substitutes for a number of them by way of Keras models. For those canned estimators that do not have built-in TensorFlow 2 substitutes, you can still build your own replacement fairly easily.\n",
         "\n",
-        "This guide walks through a few examples of direct equivalents and custom substitutions to demonstrate how TensorFlow 1's `tf.estimator`-derived models can be migrated to TF2 with Keras.\n",
+        "This guide will walk you through a few examples of direct equivalents and custom substitutions to demonstrate how TensorFlow 1's `tf.estimator`-derived models can be migrated to TensorFlow 2 with Keras.\n",
         "\n",
         "Namely, this guide includes examples for migrating:\n",
         "*    From `tf.estimator`'s `LinearEstimator`, `Classifier` or `Regressor` in TensorFlow 1 to Keras `tf.compat.v1.keras.models.LinearModel` in TensorFlow 2\n",
         "*    From `tf.estimator`'s `DNNEstimator`, `Classifier` or `Regressor` in TensorFlow 1 to a custom Keras DNN ModelKeras in TensorFlow 2\n",
         "*    From `tf.estimator`'s `DNNLinearCombinedEstimator`, `Classifier` or `Regressor` in TensorFlow 1 to `tf.compat.v1.keras.models.WideDeepModel` in TensorFlow 2\n",
         "*    From `tf.estimator`'s `BoostedTreesEstimator`, `Classifier` or `Regressor` in TensorFlow 1 to `tfdf.keras.GradientBoostedTreesModel` in TensorFlow 2\n",
         "\n",
-        "A common precursor to the training of a model is feature preprocessing, which is done for TensorFlow 1 Estimator models with `tf.feature_column`. For more information on feature preprocessing in TensorFlow 2, check out [this guide on migrating from feature columns to the Keras preprocessing layers API](migrating_feature_columns.ipynb)."
+        "A common precursor to the training of a model is feature preprocessing, which is done for TensorFlow 1 Estimator models with `tf.feature_column`. For more information on feature preprocessing in TensorFlow 2, see [this guide on migrating from feature columns to the Keras preprocessing layers API](migrating_feature_columns.ipynb)."
       ]
     },
     {
@@ -183,7 +183,7 @@
         "id": "bYSgoezeMrpI"
       },
       "source": [
-        "and create a method to instantiate a simplistic sample optimizer to use with our various TensorFlow 1 Estimator and TensorFlow 2 Keras models."
+        "and create a method to instantiate a simplistic sample optimizer to use with various TensorFlow 1 Estimator and TensorFlow 2 Keras models."
       ]
     },
     {
@@ -226,7 +226,7 @@
         "id": "_O7fyhCnpvED"
       },
       "source": [
-        "### TF1: Using LinearEstimator"
+        "### TensorFlow 1: Using LinearEstimator"
       ]
     },
     {
@@ -270,7 +270,7 @@
         "id": "KEmzBjfnsxwT"
       },
       "source": [
-        "### TF2: Using Keras LinearModel"
+        "### TensorFlow 2: Using Keras LinearModel"
       ]
     },
     {
@@ -311,7 +311,7 @@
         "id": "YKl6XZ7Bp1t5"
       },
       "source": [
-        "### TF1: Using DNNEstimator"
+        "### TensorFlow 1: Using DNNEstimator"
       ]
     },
     {
@@ -320,7 +320,7 @@
         "id": "J7wJUmgypln8"
       },
       "source": [
-        "In TensorFlow 1, you can use `tf.estimator.DNNEstimator` to create a baseline DNN model for regression and classification problems."
+        "In TensorFlow 1, you can use `tf.estimator.DNNEstimator` to create a baseline deep neural network (DNN) model for regression and classification problems."
       ]
     },
     {
@@ -357,7 +357,7 @@
         "id": "6xJz6px6pln-"
       },
       "source": [
-        "### TF2: Using Keras to create a custom DNN model"
+        "### TensorFlow 2: Using Keras to create a custom DNN model"
       ]
     },
     {
@@ -368,7 +368,7 @@
       "source": [
         "In TensorFlow 2, you can create a custom DNN model to substitute for one generated by `tf.estimator.DNNEstimator`, with similar levels of user-specified customization (for instance, as in the previous example, the ability to customize a chosen model optimizer).\n",
         "\n",
-        "A similar workflow can be used to replace `tf.estimator.experimental.RNNEstimator` with a Keras RNN Model. Keras provides a number of built-in, customizable choices by way of `tf.keras.layers.RNN`, `tf.keras.layers.LSTM`, and `tf.keras.layers.GRU`. Refer to _Built-in RNN layers: a simple example_ in the [RNN with Keras guide](https://www.tensorflow.org/guide/keras/rnn#built-in_rnn_layers_a_simple_example) for more details."
+        "A similar workflow can be used to replace `tf.estimator.experimental.RNNEstimator` with a Keras recurrent neural network (RNN) model. Keras provides a number of built-in, customizable choices by way of `tf.keras.layers.RNN`, `tf.keras.layers.LSTM`, and `tf.keras.layers.GRU`. To learn more, check out the _Built-in RNN layers: a simple example_ section of [RNN with Keras guide](https://www.tensorflow.org/guide/keras/rnn)."
       ]
     },
     {
@@ -413,7 +413,7 @@
         "id": "GfRaObf5g4TU"
       },
       "source": [
-        "### TF1: Using DNNLinearCombinedEstimator"
+        "### TensorFlow 1: Using DNNLinearCombinedEstimator"
       ]
     },
     {
@@ -464,7 +464,7 @@
         "id": "BeMikL5ug4TX"
       },
       "source": [
-        "### TF2: Using Keras WideDeepModel"
+        "### TensorFlow 2: Using Keras WideDeepModel"
       ]
     },
     {
@@ -477,7 +477,7 @@
         "\n",
         "This `WideDeepModel` is constructed on the basis of a constituent `LinearModel` and a custom DNN Model, both of which are discussed in the preceding two examples. A custom linear model can also be used in place of the built-in Keras `LinearModel` if desired.\n",
         "\n",
-        "If you would like to build your own model instead of a canned estimator, check out [how to build a `keras.Sequential` model](https://www.tensorflow.org/guide/keras/sequential_model). For more information on custom training and optimizers you can also checkout [this guide](https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough)."
+        "If you would like to build your own model instead of using a canned estimator, check out the [Keras Sequential model](https://www.tensorflow.org/guide/keras/sequential_model) guide. For more information on custom training and optimizers, check out the [Custom training: walkthrough](https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough) guide."
       ]
     },
     {
@@ -488,7 +488,7 @@
       },
       "outputs": [],
       "source": [
-        "# Create a LinearModel and a DNN model as in Examples 1 and 2\n",
+        "# Create LinearModel and DNN Model as in Examples 1 and 2\n",
         "optimizer = create_sample_optimizer('tf2')\n",
         "\n",
         "linear_model = tf.compat.v1.keras.experimental.LinearModel()\n",
@@ -532,7 +532,7 @@
         "id": "_3mCQVDSeOKD"
       },
       "source": [
-        "### TF1: Using BoostedTreesEstimator"
+        "### TensorFlow 1: Using BoostedTreesEstimator"
       ]
     },
     {
@@ -578,7 +578,7 @@
         "id": "eNuLP6BeeOKF"
       },
       "source": [
-        "### TF2: Using TensorFlow Decision Forests"
+        "### TensorFlow 2: Using TensorFlow Decision Forests"
       ]
     },
     {
@@ -634,7 +634,7 @@
         "train_dataframe = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')\n",
         "eval_dataframe = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')\n",
         "\n",
-        "# Convert the pandas Dataframes into TensorFlow datasets.\n",
+        "# Convert the Pandas Dataframes into TensorFlow datasets.\n",
         "train_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(train_dataframe, label=\"survived\")\n",
         "eval_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(eval_dataframe, label=\"survived\")"
       ]
@@ -691,7 +691,7 @@
       "source": [
         "Gradient Boosted Trees is just one of the many decision forest algorithms available in TensorFlow Decision Forests. For example, Random Forests (available as [tfdf.keras.GradientBoostedTreesModel](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/RandomForestModel) is very resistant to overfitting) while CART (available as [tfdf.keras.CartModel](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/CartModel)) is great for model interpretation.\n",
         "\n",
-        "In the next example, you'll train and plot a Random Forest model."
+        "In the next example, train and plot a Random Forest model."
       ]
     },
     {
@@ -718,7 +718,7 @@
         "id": "Z0QYolhoZb_k"
       },
       "source": [
-        "In the final example, you'll train and evaluate a CART model."
+        "In the final example, train and evaluate a CART model."
       ]
     },
     {

Original file line number	Diff line number	Diff line change
`@@ -67,15 +67,15 @@`
`67`	`67`	`"source": [`
`68`	`68`	`"Canned (or Premade) Estimators have traditionally been used in TensorFlow 1 as quick and easy ways to train models for a variety of typical use cases. TensorFlow 2 provides straightforward approximate substitutes for a number of them by way of Keras models. For those canned estimators that do not have built-in TensorFlow 2 substitutes, you can still build your own replacement fairly easily.\n",`
`69`	`69`	`"\n",`
`70`		- "This guide walks through a few examples of direct equivalents and custom substitutions to demonstrate how TensorFlow 1's `tf.estimator`-derived models can be migrated to TF2 with Keras.\n",
	`70`	+ "This guide will walk you through a few examples of direct equivalents and custom substitutions to demonstrate how TensorFlow 1's `tf.estimator`-derived models can be migrated to TensorFlow 2 with Keras.\n",
`71`	`71`	`"\n",`
`72`	`72`	`"Namely, this guide includes examples for migrating:\n",`
`73`	`73`	"* From `tf.estimator`'s `LinearEstimator`, `Classifier` or `Regressor` in TensorFlow 1 to Keras `tf.compat.v1.keras.models.LinearModel` in TensorFlow 2\n",
`74`	`74`	"* From `tf.estimator`'s `DNNEstimator`, `Classifier` or `Regressor` in TensorFlow 1 to a custom Keras DNN ModelKeras in TensorFlow 2\n",
`75`	`75`	"* From `tf.estimator`'s `DNNLinearCombinedEstimator`, `Classifier` or `Regressor` in TensorFlow 1 to `tf.compat.v1.keras.models.WideDeepModel` in TensorFlow 2\n",
`76`	`76`	"* From `tf.estimator`'s `BoostedTreesEstimator`, `Classifier` or `Regressor` in TensorFlow 1 to `tfdf.keras.GradientBoostedTreesModel` in TensorFlow 2\n",
`77`	`77`	`"\n",`
`78`		- "A common precursor to the training of a model is feature preprocessing, which is done for TensorFlow 1 Estimator models with `tf.feature_column`. For more information on feature preprocessing in TensorFlow 2, check out [this guide on migrating from feature columns to the Keras preprocessing layers API](migrating_feature_columns.ipynb)."
	`78`	+ "A common precursor to the training of a model is feature preprocessing, which is done for TensorFlow 1 Estimator models with `tf.feature_column`. For more information on feature preprocessing in TensorFlow 2, see [this guide on migrating from feature columns to the Keras preprocessing layers API](migrating_feature_columns.ipynb)."
`79`	`79`	`]`
`80`	`80`	`},`
`81`	`81`	`{`
`@@ -183,7 +183,7 @@`
`183`	`183`	`"id": "bYSgoezeMrpI"`
`184`	`184`	`},`
`185`	`185`	`"source": [`
`186`		`- "and create a method to instantiate a simplistic sample optimizer to use with our various TensorFlow 1 Estimator and TensorFlow 2 Keras models."`
	`186`	`+ "and create a method to instantiate a simplistic sample optimizer to use with various TensorFlow 1 Estimator and TensorFlow 2 Keras models."`
`187`	`187`	`]`
`188`	`188`	`},`
`189`	`189`	`{`
`@@ -226,7 +226,7 @@`
`226`	`226`	`"id": "_O7fyhCnpvED"`
`227`	`227`	`},`
`228`	`228`	`"source": [`
`229`		`- "### TF1: Using LinearEstimator"`
	`229`	`+ "### TensorFlow 1: Using LinearEstimator"`
`230`	`230`	`]`
`231`	`231`	`},`
`232`	`232`	`{`
`@@ -270,7 +270,7 @@`
`270`	`270`	`"id": "KEmzBjfnsxwT"`
`271`	`271`	`},`
`272`	`272`	`"source": [`
`273`		`- "### TF2: Using Keras LinearModel"`
	`273`	`+ "### TensorFlow 2: Using Keras LinearModel"`
`274`	`274`	`]`
`275`	`275`	`},`
`276`	`276`	`{`
`@@ -311,7 +311,7 @@`
`311`	`311`	`"id": "YKl6XZ7Bp1t5"`
`312`	`312`	`},`
`313`	`313`	`"source": [`
`314`		`- "### TF1: Using DNNEstimator"`
	`314`	`+ "### TensorFlow 1: Using DNNEstimator"`
`315`	`315`	`]`
`316`	`316`	`},`
`317`	`317`	`{`
`@@ -320,7 +320,7 @@`
`320`	`320`	`"id": "J7wJUmgypln8"`
`321`	`321`	`},`
`322`	`322`	`"source": [`
`323`		- "In TensorFlow 1, you can use `tf.estimator.DNNEstimator` to create a baseline DNN model for regression and classification problems."
	`323`	+ "In TensorFlow 1, you can use `tf.estimator.DNNEstimator` to create a baseline deep neural network (DNN) model for regression and classification problems."
`324`	`324`	`]`
`325`	`325`	`},`
`326`	`326`	`{`
`@@ -357,7 +357,7 @@`
`357`	`357`	`"id": "6xJz6px6pln-"`
`358`	`358`	`},`
`359`	`359`	`"source": [`
`360`		`- "### TF2: Using Keras to create a custom DNN model"`
	`360`	`+ "### TensorFlow 2: Using Keras to create a custom DNN model"`
`361`	`361`	`]`
`362`	`362`	`},`
`363`	`363`	`{`
`@@ -368,7 +368,7 @@`
`368`	`368`	`"source": [`
`369`	`369`	"In TensorFlow 2, you can create a custom DNN model to substitute for one generated by `tf.estimator.DNNEstimator`, with similar levels of user-specified customization (for instance, as in the previous example, the ability to customize a chosen model optimizer).\n",
`370`	`370`	`"\n",`
`371`		- "A similar workflow can be used to replace `tf.estimator.experimental.RNNEstimator` with a Keras RNN Model. Keras provides a number of built-in, customizable choices by way of `tf.keras.layers.RNN`, `tf.keras.layers.LSTM`, and `tf.keras.layers.GRU`. Refer to _Built-in RNN layers: a simple example_ in the [RNN with Keras guide](https://www.tensorflow.org/guide/keras/rnn#built-in_rnn_layers_a_simple_example) for more details."
	`371`	+ "A similar workflow can be used to replace `tf.estimator.experimental.RNNEstimator` with a Keras recurrent neural network (RNN) model. Keras provides a number of built-in, customizable choices by way of `tf.keras.layers.RNN`, `tf.keras.layers.LSTM`, and `tf.keras.layers.GRU`. To learn more, check out the _Built-in RNN layers: a simple example_ section of [RNN with Keras guide](https://www.tensorflow.org/guide/keras/rnn)."
`372`	`372`	`]`
`373`	`373`	`},`
`374`	`374`	`{`
`@@ -413,7 +413,7 @@`
`413`	`413`	`"id": "GfRaObf5g4TU"`
`414`	`414`	`},`
`415`	`415`	`"source": [`
`416`		`- "### TF1: Using DNNLinearCombinedEstimator"`
	`416`	`+ "### TensorFlow 1: Using DNNLinearCombinedEstimator"`
`417`	`417`	`]`
`418`	`418`	`},`
`419`	`419`	`{`
`@@ -464,7 +464,7 @@`
`464`	`464`	`"id": "BeMikL5ug4TX"`
`465`	`465`	`},`
`466`	`466`	`"source": [`
`467`		`- "### TF2: Using Keras WideDeepModel"`
	`467`	`+ "### TensorFlow 2: Using Keras WideDeepModel"`
`468`	`468`	`]`
`469`	`469`	`},`
`470`	`470`	`{`
`@@ -477,7 +477,7 @@`
`477`	`477`	`"\n",`
`478`	`478`	"This `WideDeepModel` is constructed on the basis of a constituent `LinearModel` and a custom DNN Model, both of which are discussed in the preceding two examples. A custom linear model can also be used in place of the built-in Keras `LinearModel` if desired.\n",
`479`	`479`	`"\n",`
`480`		- "If you would like to build your own model instead of a canned estimator, check out [how to build a `keras.Sequential` model](https://www.tensorflow.org/guide/keras/sequential_model). For more information on custom training and optimizers you can also checkout [this guide](https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough)."
	`480`	`+ "If you would like to build your own model instead of using a canned estimator, check out the [Keras Sequential model](https://www.tensorflow.org/guide/keras/sequential_model) guide. For more information on custom training and optimizers, check out the [Custom training: walkthrough](https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough) guide."`
`481`	`481`	`]`
`482`	`482`	`},`
`483`	`483`	`{`
`@@ -488,7 +488,7 @@`
`488`	`488`	`},`
`489`	`489`	`"outputs": [],`
`490`	`490`	`"source": [`
`491`		`- "# Create a LinearModel and a DNN model as in Examples 1 and 2\n",`
	`491`	`+ "# Create LinearModel and DNN Model as in Examples 1 and 2\n",`
`492`	`492`	`"optimizer = create_sample_optimizer('tf2')\n",`
`493`	`493`	`"\n",`
`494`	`494`	`"linear_model = tf.compat.v1.keras.experimental.LinearModel()\n",`
`@@ -532,7 +532,7 @@`
`532`	`532`	`"id": "_3mCQVDSeOKD"`
`533`	`533`	`},`
`534`	`534`	`"source": [`
`535`		`- "### TF1: Using BoostedTreesEstimator"`
	`535`	`+ "### TensorFlow 1: Using BoostedTreesEstimator"`
`536`	`536`	`]`
`537`	`537`	`},`
`538`	`538`	`{`
`@@ -578,7 +578,7 @@`
`578`	`578`	`"id": "eNuLP6BeeOKF"`
`579`	`579`	`},`
`580`	`580`	`"source": [`
`581`		`- "### TF2: Using TensorFlow Decision Forests"`
	`581`	`+ "### TensorFlow 2: Using TensorFlow Decision Forests"`
`582`	`582`	`]`
`583`	`583`	`},`
`584`	`584`	`{`
`@@ -634,7 +634,7 @@`
`634`	`634`	`"train_dataframe = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')\n",`
`635`	`635`	`"eval_dataframe = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')\n",`
`636`	`636`	`"\n",`
`637`		`- "# Convert the pandas Dataframes into TensorFlow datasets.\n",`
	`637`	`+ "# Convert the Pandas Dataframes into TensorFlow datasets.\n",`
`638`	`638`	`"train_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(train_dataframe, label=\"survived\")\n",`
`639`	`639`	`"eval_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(eval_dataframe, label=\"survived\")"`
`640`	`640`	`]`
`@@ -691,7 +691,7 @@`
`691`	`691`	`"source": [`
`692`	`692`	`"Gradient Boosted Trees is just one of the many decision forest algorithms available in TensorFlow Decision Forests. For example, Random Forests (available as [tfdf.keras.GradientBoostedTreesModel](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/RandomForestModel) is very resistant to overfitting) while CART (available as [tfdf.keras.CartModel](https://www.tensorflow.org/decision_forests/api_docs/python/tfdf/keras/CartModel)) is great for model interpretation.\n",`
`693`	`693`	`"\n",`
`694`		`- "In the next example, you'll train and plot a Random Forest model."`
	`694`	`+ "In the next example, train and plot a Random Forest model."`
`695`	`695`	`]`
`696`	`696`	`},`
`697`	`697`	`{`
`@@ -718,7 +718,7 @@`
`718`	`718`	`"id": "Z0QYolhoZb_k"`
`719`	`719`	`},`
`720`	`720`	`"source": [`
`721`		`- "In the final example, you'll train and evaluate a CART model."`
	`721`	`+ "In the final example, train and evaluate a CART model."`
`722`	`722`	`]`
`723`	`723`	`},`
`724`	`724`	`{`