Migration Examples: Canned Estimators

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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.

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.

Namely, this guide includes examples for migrating:

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.

Setup

Start with a couple of necessary TensorFlow imports,

pip install tensorflow_decision_forests
import keras
import pandas as pd
import tensorflow as tf
import tensorflow.compat.v1 as tf1
import tensorflow_decision_forests as tfdf
2022-09-15 01:21:10.996161: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2022-09-15 01:21:11.666587: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory
2022-09-15 01:21:11.666860: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory
2022-09-15 01:21:11.666872: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

prepare some simple data for demonstration from the standard Titanic dataset,

x_train = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')
x_eval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')
x_train['sex'].replace(('male', 'female'), (0, 1), inplace=True)
x_eval['sex'].replace(('male', 'female'), (0, 1), inplace=True)

x_train['alone'].replace(('n', 'y'), (0, 1), inplace=True)
x_eval['alone'].replace(('n', 'y'), (0, 1), inplace=True)

x_train['class'].replace(('First', 'Second', 'Third'), (1, 2, 3), inplace=True)
x_eval['class'].replace(('First', 'Second', 'Third'), (1, 2, 3), inplace=True)

x_train.drop(['embark_town', 'deck'], axis=1, inplace=True)
x_eval.drop(['embark_town', 'deck'], axis=1, inplace=True)

y_train = x_train.pop('survived')
y_eval = x_eval.pop('survived')
# Data setup for TensorFlow 1 with `tf.estimator`
def _input_fn():
  return tf1.data.Dataset.from_tensor_slices((dict(x_train), y_train)).batch(32)


def _eval_input_fn():
  return tf1.data.Dataset.from_tensor_slices((dict(x_eval), y_eval)).batch(32)


FEATURE_NAMES = [
    'age', 'fare', 'sex', 'n_siblings_spouses', 'parch', 'class', 'alone'
]

feature_columns = []
for fn in FEATURE_NAMES:
  feat_col = tf1.feature_column.numeric_column(fn, dtype=tf.float32)
  feature_columns.append(feat_col)

and create a method to instantiate a simplistic sample optimizer to use with our various TensorFlow 1 Estimator and TensorFlow 2 Keras models.

def create_sample_optimizer(tf_version):
  if tf_version == 'tf1':
    optimizer = lambda: tf.keras.optimizers.legacy.Ftrl(
        l1_regularization_strength=0.001,
        learning_rate=tf1.train.exponential_decay(
            learning_rate=0.1,
            global_step=tf1.train.get_global_step(),
            decay_steps=10000,
            decay_rate=0.9))
  elif tf_version == 'tf2':
    optimizer = tf.keras.optimizers.legacy.Ftrl(
        l1_regularization_strength=0.001,
        learning_rate=tf.keras.optimizers.schedules.ExponentialDecay(
            initial_learning_rate=0.1, decay_steps=10000, decay_rate=0.9))
  return optimizer

Example 1: Migrating from LinearEstimator

TF1: Using LinearEstimator

In TensorFlow 1, you can use tf.estimator.LinearEstimator to create a baseline linear model for regression and classification problems.

linear_estimator = tf.estimator.LinearEstimator(
    head=tf.estimator.BinaryClassHead(),
    feature_columns=feature_columns,
    optimizer=create_sample_optimizer('tf1'))
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmpbgnqi9sh
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpbgnqi9sh', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
linear_estimator.train(input_fn=_input_fn, steps=100)
linear_estimator.evaluate(input_fn=_eval_input_fn, steps=10)
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/training_util.py:396: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/optimizers/optimizer_v2/ftrl.py:170: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpbgnqi9sh/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 0.6931472, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20...
INFO:tensorflow:Saving checkpoints for 20 into /tmpfs/tmp/tmpbgnqi9sh/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20...
INFO:tensorflow:Loss for final step: 0.552688.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2022-09-15T01:21:19
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpbgnqi9sh/model.ckpt-20
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Inference Time : 0.49880s
INFO:tensorflow:Finished evaluation at 2022-09-15-01:21:20
INFO:tensorflow:Saving dict for global step 20: accuracy = 0.70075756, accuracy_baseline = 0.625, auc = 0.75472915, auc_precision_recall = 0.65362054, average_loss = 0.5759378, global_step = 20, label/mean = 0.375, loss = 0.5704811, precision = 0.6388889, prediction/mean = 0.41331065, recall = 0.46464646
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20: /tmpfs/tmp/tmpbgnqi9sh/model.ckpt-20
{'accuracy': 0.70075756,
 'accuracy_baseline': 0.625,
 'auc': 0.75472915,
 'auc_precision_recall': 0.65362054,
 'average_loss': 0.5759378,
 'label/mean': 0.375,
 'loss': 0.5704811,
 'precision': 0.6388889,
 'prediction/mean': 0.41331065,
 'recall': 0.46464646,
 'global_step': 20}

TF2: Using Keras LinearModel

In TensorFlow 2, you can create an instance of the Keras tf.compat.v1.keras.models.LinearModel which is the substitute to the tf.estimator.LinearEstimator. The tf.compat.v1.keras path is used to signify that the pre-made model exists for compatibility.

linear_model = tf.compat.v1.keras.experimental.LinearModel()
linear_model.compile(loss='mse', optimizer=create_sample_optimizer('tf2'), metrics=['accuracy'])
linear_model.fit(x_train, y_train, epochs=10)
linear_model.evaluate(x_eval, y_eval, return_dict=True)
Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 3.1806 - accuracy: 0.5024
Epoch 2/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2254 - accuracy: 0.6539
Epoch 3/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2569 - accuracy: 0.7145
Epoch 4/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2314 - accuracy: 0.6874
Epoch 5/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1760 - accuracy: 0.7592
Epoch 6/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1881 - accuracy: 0.7576
Epoch 7/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1755 - accuracy: 0.7799
Epoch 8/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1789 - accuracy: 0.7879
Epoch 9/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1560 - accuracy: 0.8022
Epoch 10/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1543 - accuracy: 0.8038
9/9 [==============================] - 0s 2ms/step - loss: 0.1873 - accuracy: 0.7538
{'loss': 0.18730314075946808, 'accuracy': 0.7537878751754761}

Example 2: Migrating from DNNEstimator

TF1: Using DNNEstimator

In TensorFlow 1, you can use tf.estimator.DNNEstimator to create a baseline DNN model for regression and classification problems.

dnn_estimator = tf.estimator.DNNEstimator(
    head=tf.estimator.BinaryClassHead(),
    feature_columns=feature_columns,
    hidden_units=[128],
    activation_fn=tf.nn.relu,
    optimizer=create_sample_optimizer('tf1'))
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmp5pfcd4w9
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmp5pfcd4w9', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
dnn_estimator.train(input_fn=_input_fn, steps=100)
dnn_estimator.evaluate(input_fn=_eval_input_fn, steps=10)
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
2022-09-15 01:21:21.986809: W tensorflow/core/common_runtime/forward_type_inference.cc:332] Type inference failed. This indicates an invalid graph that escaped type checking. Error message: INVALID_ARGUMENT: expected compatible input types, but input 1:
type_id: TFT_OPTIONAL
args {
  type_id: TFT_PRODUCT
  args {
    type_id: TFT_TENSOR
    args {
      type_id: TFT_INT64
    }
  }
}
 is neither a subtype nor a supertype of the combined inputs preceding it:
type_id: TFT_OPTIONAL
args {
  type_id: TFT_PRODUCT
  args {
    type_id: TFT_TENSOR
    args {
      type_id: TFT_INT32
    }
  }
}

    while inferring type of node 'dnn/zero_fraction/cond/output/_18'
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmp5pfcd4w9/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 2.1210577, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20...
INFO:tensorflow:Saving checkpoints for 20 into /tmpfs/tmp/tmp5pfcd4w9/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20...
INFO:tensorflow:Loss for final step: 0.5941131.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2022-09-15T01:21:23
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmp5pfcd4w9/model.ckpt-20
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Inference Time : 0.46603s
INFO:tensorflow:Finished evaluation at 2022-09-15-01:21:23
INFO:tensorflow:Saving dict for global step 20: accuracy = 0.70075756, accuracy_baseline = 0.625, auc = 0.7197429, auc_precision_recall = 0.6219531, average_loss = 0.6046755, global_step = 20, label/mean = 0.375, loss = 0.60079545, precision = 0.64705884, prediction/mean = 0.41633147, recall = 0.44444445
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20: /tmpfs/tmp/tmp5pfcd4w9/model.ckpt-20
{'accuracy': 0.70075756,
 'accuracy_baseline': 0.625,
 'auc': 0.7197429,
 'auc_precision_recall': 0.6219531,
 'average_loss': 0.6046755,
 'label/mean': 0.375,
 'loss': 0.60079545,
 'precision': 0.64705884,
 'prediction/mean': 0.41633147,
 'recall': 0.44444445,
 'global_step': 20}

TF2: Using Keras to Create a Custom DNN Model

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).

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 - see here for more details.

dnn_model = tf.keras.models.Sequential(
    [tf.keras.layers.Dense(128, activation='relu'),
     tf.keras.layers.Dense(1)])

dnn_model.compile(loss='mse', optimizer=create_sample_optimizer('tf2'), metrics=['accuracy'])
dnn_model.fit(x_train, y_train, epochs=10)
dnn_model.evaluate(x_eval, y_eval, return_dict=True)
Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 575.6970 - accuracy: 0.5582
Epoch 2/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2964 - accuracy: 0.6938
Epoch 3/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2364 - accuracy: 0.7352
Epoch 4/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1901 - accuracy: 0.7703
Epoch 5/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1896 - accuracy: 0.7464
Epoch 6/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1781 - accuracy: 0.7799
Epoch 7/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1718 - accuracy: 0.7815
Epoch 8/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1667 - accuracy: 0.7959
Epoch 9/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1719 - accuracy: 0.7863
Epoch 10/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1608 - accuracy: 0.8102
9/9 [==============================] - 0s 2ms/step - loss: 0.1825 - accuracy: 0.7462
{'loss': 0.18249517679214478, 'accuracy': 0.7462121248245239}

Example 3: Migrating from DNNLinearCombinedEstimator

TF1: Using DNNLinearCombinedEstimator

In TensorFlow 1, you can use tf.estimator.DNNLinearCombinedEstimator to create a baseline combined model for regression and classification problems with customization capacity for both its linear and DNN components.

optimizer = create_sample_optimizer('tf1')

combined_estimator = tf.estimator.DNNLinearCombinedEstimator(
    head=tf.estimator.BinaryClassHead(),
    # Wide settings
    linear_feature_columns=feature_columns,
    linear_optimizer=optimizer,
    # Deep settings
    dnn_feature_columns=feature_columns,
    dnn_hidden_units=[128],
    dnn_optimizer=optimizer)
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmpzsp_5pwt
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpzsp_5pwt', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
  rewrite_options {
    meta_optimizer_iterations: ONE
  }
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
combined_estimator.train(input_fn=_input_fn, steps=100)
combined_estimator.evaluate(input_fn=_eval_input_fn, steps=10)
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpzsp_5pwt/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 2.6094906, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20...
INFO:tensorflow:Saving checkpoints for 20 into /tmpfs/tmp/tmpzsp_5pwt/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20...
INFO:tensorflow:Loss for final step: 0.536165.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2022-09-15T01:21:27
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpzsp_5pwt/model.ckpt-20
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Inference Time : 0.58632s
INFO:tensorflow:Finished evaluation at 2022-09-15-01:21:28
INFO:tensorflow:Saving dict for global step 20: accuracy = 0.70075756, accuracy_baseline = 0.625, auc = 0.74854606, auc_precision_recall = 0.64865834, average_loss = 0.5756461, global_step = 20, label/mean = 0.375, loss = 0.56522435, precision = 0.6315789, prediction/mean = 0.40043274, recall = 0.4848485
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20: /tmpfs/tmp/tmpzsp_5pwt/model.ckpt-20
{'accuracy': 0.70075756,
 'accuracy_baseline': 0.625,
 'auc': 0.74854606,
 'auc_precision_recall': 0.64865834,
 'average_loss': 0.5756461,
 'label/mean': 0.375,
 'loss': 0.56522435,
 'precision': 0.6315789,
 'prediction/mean': 0.40043274,
 'recall': 0.4848485,
 'global_step': 20}

TF2: Using Keras WideDeepModel

In TensorFlow 2, you can create an instance of the Keras tf.compat.v1.keras.models.WideDeepModel to substitute for one generated by tf.estimator.DNNLinearCombinedEstimator, with similar levels of user-specified customization (for instance, as in the previous example, the ability to customize a chosen model optimizer).

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.

If you would like to build your own model instead of a canned estimator, check out how to build a keras.Sequential model. For more information on custom training and optimizers you can also checkout this guide.

# Create LinearModel and DNN Model as in Examples 1 and 2
optimizer = create_sample_optimizer('tf2')

linear_model = tf.compat.v1.keras.experimental.LinearModel()
linear_model.compile(loss='mse', optimizer=optimizer, metrics=['accuracy'])
linear_model.fit(x_train, y_train, epochs=10, verbose=0)

dnn_model = tf.keras.models.Sequential(
    [tf.keras.layers.Dense(128, activation='relu'),
     tf.keras.layers.Dense(1)])
dnn_model.compile(loss='mse', optimizer=optimizer, metrics=['accuracy'])
combined_model = tf.compat.v1.keras.experimental.WideDeepModel(linear_model,
                                                               dnn_model)
combined_model.compile(
    optimizer=[optimizer, optimizer], loss='mse', metrics=['accuracy'])
combined_model.fit([x_train, x_train], y_train, epochs=10)
combined_model.evaluate(x_eval, y_eval, return_dict=True)
Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 243.0148 - accuracy: 0.5726
Epoch 2/10
20/20 [==============================] - 0s 2ms/step - loss: 0.4183 - accuracy: 0.7576
Epoch 3/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2819 - accuracy: 0.7703
Epoch 4/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2296 - accuracy: 0.7735
Epoch 5/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1973 - accuracy: 0.8038
Epoch 6/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1823 - accuracy: 0.8070
Epoch 7/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1769 - accuracy: 0.7990
Epoch 8/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1663 - accuracy: 0.8038
Epoch 9/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1616 - accuracy: 0.8006
Epoch 10/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1580 - accuracy: 0.8070
9/9 [==============================] - 0s 2ms/step - loss: 0.1977 - accuracy: 0.7386
{'loss': 0.19771531224250793, 'accuracy': 0.7386363744735718}

Example 4: Migrating from BoostedTreesEstimator

TF1: Using BoostedTreesEstimator

In TensorFlow 1, you could use tf.estimator.BoostedTreesEstimator to create a baseline to create a baseline Gradient Boosting model using an ensemble of decision trees for regression and classification problems. This functionality is no longer included in TensorFlow 2.

bt_estimator = tf1.estimator.BoostedTreesEstimator(
    head=tf.estimator.BinaryClassHead(),
    n_batches_per_layer=1,
    max_depth=10,
    n_trees=1000,
    feature_columns=feature_columns)
bt_estimator.train(input_fn=_input_fn, steps=1000)
bt_estimator.evaluate(input_fn=_eval_input_fn, steps=100)

TF2: Using TensorFlow Decision Forests

In TensorFlow 2, tf.estimator.BoostedTreesEstimator is replaced by tfdf.keras.GradientBoostedTreesModel from the TensorFlow Decision Forests package.

TensorFlow Decision Forests provides various advantages over the tf.estimator.BoostedTreesEstimator, notably regarding quality, speed, ease of use and flexibility. To learn about TensorFlow Decision Forests, start with the beginner colab.

The following example shows how to train a Gradient Boosted Trees model using TensorFlow 2:

Install TensorFlow Decision Forests.

pip install tensorflow_decision_forests

Create a TensorFlow dataset. Note that Decision Forests support natively many types of features and do not need pre-processing.

train_dataframe = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')
eval_dataframe = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')

# Convert the Pandas Dataframes into TensorFlow datasets.
train_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(train_dataframe, label="survived")
eval_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(eval_dataframe, label="survived")
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_decision_forests/keras/core_inference.py:873: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.
  features_dataframe = dataframe.drop(label, 1)
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_decision_forests/keras/core_inference.py:873: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.
  features_dataframe = dataframe.drop(label, 1)

Train the model on the train_dataset dataset.

# Use the default hyper-parameters of the model.
gbt_model = tfdf.keras.GradientBoostedTreesModel()
gbt_model.fit(train_dataset)
Warning: The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus.
WARNING:absl:The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus.
Use /tmpfs/tmp/tmp2i1wfi68 as temporary training directory
Reading training dataset...
Training dataset read in 0:00:03.189758. Found 627 examples.
Training model...
Model trained in 0:00:00.207383
Compiling model...
[INFO kernel.cc:1176] Loading model from path /tmpfs/tmp/tmp2i1wfi68/model/ with prefix f0b7f68f3a6a4b9f
[INFO abstract_model.cc:1248] Engine "GradientBoostedTreesQuickScorerExtended" built
[INFO kernel.cc:1022] Use fast generic engine
WARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f732d454550> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f732d454550> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING: AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f732d454550> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
Model compiled.
<keras.callbacks.History at 0x7f72a412fac0>

Evaluate the quality of the model on the eval_dataset dataset.

gbt_model.compile(metrics=['accuracy'])
gbt_evaluation = gbt_model.evaluate(eval_dataset, return_dict=True)
print(gbt_evaluation)
1/1 [==============================] - 0s 276ms/step - loss: 0.0000e+00 - accuracy: 0.8295
{'loss': 0.0, 'accuracy': 0.8295454382896423}

Gradient Boosted Trees is just one of the many decision forests algorithms avaiable in TensorFlow Decision Forests. For example, Random Forests (available as tfdf.keras.GradientBoostedTreesModel is very resistant to overfitting) while CART (available as tfdf.keras.CartModel) is great for model interpretation.

In the next example, we train and plot a Random Forest model.

# Train a Random Forest model
rf_model = tfdf.keras.RandomForestModel()
rf_model.fit(train_dataset)

# Evaluate the Random Forest model
rf_model.compile(metrics=['accuracy'])
rf_evaluation = rf_model.evaluate(eval_dataset, return_dict=True)
print(rf_evaluation)
Warning: The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus.
WARNING:absl:The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus.
Use /tmpfs/tmp/tmp4kb0l934 as temporary training directory
Reading training dataset...
Training dataset read in 0:00:00.172558. Found 627 examples.
Training model...
Model trained in 0:00:00.160445
Compiling model...
[INFO kernel.cc:1176] Loading model from path /tmpfs/tmp/tmp4kb0l934/model/ with prefix 004864cebace4f46
[INFO kernel.cc:1022] Use fast generic engine
Model compiled.
1/1 [==============================] - 0s 122ms/step - loss: 0.0000e+00 - accuracy: 0.8333
{'loss': 0.0, 'accuracy': 0.8333333134651184}

Finally, in the next example, we train and evaluate a CART model.

# Train a CART model
cart_model = tfdf.keras.CartModel()
cart_model.fit(train_dataset)

# Plot the CART model
tfdf.model_plotter.plot_model_in_colab(cart_model, max_depth=2)
Warning: The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus.
WARNING:absl:The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus.
Use /tmpfs/tmp/tmpx58c_f2a as temporary training directory
Reading training dataset...
Training dataset read in 0:00:00.173530. Found 627 examples.
Training model...
Model trained in 0:00:00.016866
Compiling model...
[INFO kernel.cc:1176] Loading model from path /tmpfs/tmp/tmpx58c_f2a/model/ with prefix 546beb2773024243
[INFO kernel.cc:1022] Use fast generic engine
Model compiled.