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Migrate from Estimator to Keras APIs

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This guide demonstrates how to migrate from TensorFlow 1's tf.estimator.Estimator APIs to TensorFlow 2's tf.keras APIs. First, you will set up and run a basic model for training and evaluation with tf.estimator.Estimator. Then, you will perform the equivalent steps in TensorFlow 2 with the tf.keras APIs. You will also learn how to customize the training step by subclassing tf.keras.Model and using tf.GradientTape.

  • In TensorFlow 1, the high-level tf.estimator.Estimator APIs let you train and evaluate a model, as well as perform inference and save your model (for serving).
  • In TensorFlow 2, use the Keras APIs to perform the aforementioned tasks, such as model building, gradient application, training, evaluation, and prediction.

(For migrating model/checkpoint saving workflows to TensorFlow 2, check out the SavedModel and Checkpoint migration guides.)

Setup

Start with imports and a simple dataset:

import tensorflow as tf
import tensorflow.compat.v1 as tf1
features = [[1., 1.5], [2., 2.5], [3., 3.5]]
labels = [[0.3], [0.5], [0.7]]
eval_features = [[4., 4.5], [5., 5.5], [6., 6.5]]
eval_labels = [[0.8], [0.9], [1.]]

TensorFlow 1: Train and evaluate with tf.estimator.Estimator

This example shows how to perform training and evaluation with tf.estimator.Estimator in TensorFlow 1.

Start by defining a few functions: an input function for the training data, an evaluation input function for the evaluation data, and a model function that tells the Estimator how the training op is defined with the features and labels:

def _input_fn():
  return tf1.data.Dataset.from_tensor_slices((features, labels)).batch(1)

def _eval_input_fn():
  return tf1.data.Dataset.from_tensor_slices(
      (eval_features, eval_labels)).batch(1)

def _model_fn(features, labels, mode):
  logits = tf1.layers.Dense(1)(features)
  loss = tf1.losses.mean_squared_error(labels=labels, predictions=logits)
  optimizer = tf1.train.AdagradOptimizer(0.05)
  train_op = optimizer.minimize(loss, global_step=tf1.train.get_global_step())
  return tf1.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)

Instantiate your Estimator, and train the model:

estimator = tf1.estimator.Estimator(model_fn=_model_fn)
estimator.train(_input_fn)
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpeovq622_
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpeovq622_', '_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}
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:236: 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/python3.7/site-packages/tensorflow/python/training/adagrad.py:77: 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 /tmp/tmpeovq622_/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 2.0834494, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3...
INFO:tensorflow:Saving checkpoints for 3 into /tmp/tmpeovq622_/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3...
INFO:tensorflow:Loss for final step: 9.88002.
<tensorflow_estimator.python.estimator.estimator.Estimator at 0x7fbd06673350>

Evaluate the program with the evaluation set:

estimator.evaluate(_eval_input_fn)
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-10-26T01:32:58
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpeovq622_/model.ckpt-3
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Inference Time : 0.10194s
INFO:tensorflow:Finished evaluation at 2021-10-26-01:32:58
INFO:tensorflow:Saving dict for global step 3: global_step = 3, loss = 20.543152
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3: /tmp/tmpeovq622_/model.ckpt-3
{'loss': 20.543152, 'global_step': 3}

TensorFlow 2: Train and evaluate with the built-in Keras methods

This example demonstrates how to perform training and evaluation with Keras Model.fit and Model.evaluate in TensorFlow 2. (You can learn more in the Training and evaluation with the built-in methods guide.)

dataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(1)
eval_dataset = tf.data.Dataset.from_tensor_slices(
      (eval_features, eval_labels)).batch(1)

model = tf.keras.models.Sequential([tf.keras.layers.Dense(1)])
optimizer = tf.keras.optimizers.Adagrad(learning_rate=0.05)

model.compile(optimizer=optimizer, loss="mse")

With that, you are ready to train the model by calling Model.fit:

model.fit(dataset)
3/3 [==============================] - 0s 2ms/step - loss: 0.2785
<keras.callbacks.History at 0x7fbc4b320350>

Finally, evaluate the model with Model.evaluate:

model.evaluate(eval_dataset, return_dict=True)
3/3 [==============================] - 0s 1ms/step - loss: 0.0451
{'loss': 0.04510306194424629}

TensorFlow 2: Train and evaluate with a custom training step and built-in Keras methods

In TensorFlow 2, you can also write your own custom training step function with tf.GradientTape to perform forward and backward passes, while still taking advantage of the built-in training support, such as tf.keras.callbacks.Callback and tf.distribute.Strategy. (Learn more in Customizing what happens in Model.fit and Writing custom training loops from scratch.)

In this example, start by creating a custom tf.keras.Model by subclassing tf.keras.Sequential that overrides Model.train_step. (Learn more about subclassing tf.keras.Model). Inside that class, define a custom train_step function that for each batch of data performs a forward pass and backward pass during one training step.

class CustomModel(tf.keras.Sequential):
  """A custom sequential model that overrides `Model.train_step`."""

  def train_step(self, data):
    batch_data, labels = data

    with tf.GradientTape() as tape:
      predictions = self(batch_data, training=True)
      # Compute the loss value (the loss function is configured
      # in `Model.compile`).
      loss = self.compiled_loss(labels, predictions)

    # Compute the gradients of the parameters with respect to the loss.
    gradients = tape.gradient(loss, self.trainable_variables)
    # Perform gradient descent by updating the weights/parameters.
    self.optimizer.apply_gradients(zip(gradients, self.trainable_variables))
    # Update the metrics (includes the metric that tracks the loss).
    self.compiled_metrics.update_state(labels, predictions)
    # Return a dict mapping metric names to the current values.
    return {m.name: m.result() for m in self.metrics}

Next, as before:

dataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(1)
eval_dataset = tf.data.Dataset.from_tensor_slices(
      (eval_features, eval_labels)).batch(1)

model = CustomModel([tf.keras.layers.Dense(1)])
optimizer = tf.keras.optimizers.Adagrad(learning_rate=0.05)

model.compile(optimizer=optimizer, loss="mse")

Call Model.fit to train the model:

model.fit(dataset)
3/3 [==============================] - 0s 2ms/step - loss: 0.0587
<keras.callbacks.History at 0x7fbc3873f1d0>

And, finally, evaluate the program with Model.evaluate:

model.evaluate(eval_dataset, return_dict=True)
3/3 [==============================] - 0s 2ms/step - loss: 0.0197
{'loss': 0.019738242030143738}

Next steps

Additional Keras resources you may find useful:

The following guides can assist with migrating distribution strategy workflows from tf.estimator APIs: