Create an Estimator from a Keras model

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Overview

TensorFlow Estimators are supported in TensorFlow, and can be created from new and existing tf.keras models. This tutorial contains a complete, minimal example of that process.

Setup

import tensorflow as tf

import numpy as np
import tensorflow_datasets as tfds

Create a simple Keras model.

In Keras, you assemble layers to build models. A model is (usually) a graph of layers. The most common type of model is a stack of layers: the tf.keras.Sequential model.

To build a simple, fully-connected network (i.e. multi-layer perceptron):

model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(16, activation='relu', input_shape=(4,)),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(3)
])

Compile the model and get a summary.

model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              optimizer='adam')
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 16)                80        
_________________________________________________________________
dropout (Dropout)            (None, 16)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 3)                 51        
=================================================================
Total params: 131
Trainable params: 131
Non-trainable params: 0
_________________________________________________________________

Create an input function

Use the Datasets API to scale to large datasets or multi-device training.

Estimators need control of when and how their input pipeline is built. To allow this, they require an "Input function" or input_fn. The Estimator will call this function with no arguments. The input_fn must return a tf.data.Dataset.

def input_fn():
  split = tfds.Split.TRAIN
  dataset = tfds.load('iris', split=split, as_supervised=True)
  dataset = dataset.map(lambda features, labels: ({'dense_input':features}, labels))
  dataset = dataset.batch(32).repeat()
  return dataset

Test out your input_fn

for features_batch, labels_batch in input_fn().take(1):
  print(features_batch)
  print(labels_batch)
{'dense_input': <tf.Tensor: shape=(32, 4), dtype=float32, numpy=
array([[5.1, 3.4, 1.5, 0.2],
       [7.7, 3. , 6.1, 2.3],
       [5.7, 2.8, 4.5, 1.3],
       [6.8, 3.2, 5.9, 2.3],
       [5.2, 3.4, 1.4, 0.2],
       [5.6, 2.9, 3.6, 1.3],
       [5.5, 2.6, 4.4, 1.2],
       [5.5, 2.4, 3.7, 1. ],
       [4.6, 3.4, 1.4, 0.3],
       [7.7, 2.8, 6.7, 2. ],
       [7. , 3.2, 4.7, 1.4],
       [4.6, 3.2, 1.4, 0.2],
       [6.5, 3. , 5.2, 2. ],
       [5.5, 4.2, 1.4, 0.2],
       [5.4, 3.9, 1.3, 0.4],
       [5. , 3.5, 1.3, 0.3],
       [5.1, 3.8, 1.5, 0.3],
       [4.8, 3. , 1.4, 0.1],
       [6.5, 3. , 5.8, 2.2],
       [7.6, 3. , 6.6, 2.1],
       [6.7, 3.3, 5.7, 2.1],
       [7.9, 3.8, 6.4, 2. ],
       [6.7, 3. , 5.2, 2.3],
       [5.8, 4. , 1.2, 0.2],
       [6.3, 2.5, 5. , 1.9],
       [5. , 3. , 1.6, 0.2],
       [6.9, 3.1, 5.1, 2.3],
       [6.1, 3. , 4.6, 1.4],
       [5.8, 2.7, 4.1, 1. ],
       [5.2, 2.7, 3.9, 1.4],
       [6.7, 3. , 5. , 1.7],
       [5.7, 2.6, 3.5, 1. ]], dtype=float32)>}
tf.Tensor([0 2 1 2 0 1 1 1 0 2 1 0 2 0 0 0 0 0 2 2 2 2 2 0 2 0 2 1 1 1 1 1], shape=(32,), dtype=int64)

Create an Estimator from the tf.keras model.

A tf.keras.Model can be trained with the tf.estimator API by converting the model to an tf.estimator.Estimator object with tf.keras.estimator.model_to_estimator.

import tempfile
model_dir = tempfile.mkdtemp()
keras_estimator = tf.keras.estimator.model_to_estimator(
    keras_model=model, model_dir=model_dir)
INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using the Keras model provided.
INFO:tensorflow:Using the Keras model provided.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/keras/backend.py:435: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp4c7ajrxs', '_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}
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp4c7ajrxs', '_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}

Train and evaluate the estimator.

keras_estimator.train(input_fn=input_fn, steps=500)
eval_result = keras_estimator.evaluate(input_fn=input_fn, steps=10)
print('Eval result: {}'.format(eval_result))
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.
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.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmp/tmp4c7ajrxs/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmp/tmp4c7ajrxs/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting from: /tmp/tmp4c7ajrxs/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting from: /tmp/tmp4c7ajrxs/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-started 4 variables.
INFO:tensorflow:Warm-started 4 variables.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp4c7ajrxs/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp4c7ajrxs/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 1.7573407, step = 0
INFO:tensorflow:loss = 1.7573407, step = 0
INFO:tensorflow:global_step/sec: 18.463
INFO:tensorflow:global_step/sec: 18.463
INFO:tensorflow:loss = 1.0847657, step = 100 (5.418 sec)
INFO:tensorflow:loss = 1.0847657, step = 100 (5.418 sec)
INFO:tensorflow:global_step/sec: 19.8378
INFO:tensorflow:global_step/sec: 19.8378
INFO:tensorflow:loss = 0.8159908, step = 200 (5.041 sec)
INFO:tensorflow:loss = 0.8159908, step = 200 (5.041 sec)
INFO:tensorflow:global_step/sec: 19.4022
INFO:tensorflow:global_step/sec: 19.4022
INFO:tensorflow:loss = 0.5876056, step = 300 (5.154 sec)
INFO:tensorflow:loss = 0.5876056, step = 300 (5.154 sec)
INFO:tensorflow:global_step/sec: 20.0704
INFO:tensorflow:global_step/sec: 20.0704
INFO:tensorflow:loss = 0.47736764, step = 400 (4.982 sec)
INFO:tensorflow:loss = 0.47736764, step = 400 (4.982 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 500...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 500...
INFO:tensorflow:Saving checkpoints for 500 into /tmp/tmp4c7ajrxs/model.ckpt.
INFO:tensorflow:Saving checkpoints for 500 into /tmp/tmp4c7ajrxs/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 500...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 500...
INFO:tensorflow:Loss for final step: 0.5872932.
INFO:tensorflow:Loss for final step: 0.5872932.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:2426: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
  warnings.warn('`Model.state_updates` will be removed in a future version. '
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2021-06-19T01:28:28
INFO:tensorflow:Starting evaluation at 2021-06-19T01:28:28
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp4c7ajrxs/model.ckpt-500
INFO:tensorflow:Restoring parameters from /tmp/tmp4c7ajrxs/model.ckpt-500
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.68750s
INFO:tensorflow:Inference Time : 0.68750s
INFO:tensorflow:Finished evaluation at 2021-06-19-01:28:29
INFO:tensorflow:Finished evaluation at 2021-06-19-01:28:29
INFO:tensorflow:Saving dict for global step 500: global_step = 500, loss = 0.38869256
INFO:tensorflow:Saving dict for global step 500: global_step = 500, loss = 0.38869256
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 500: /tmp/tmp4c7ajrxs/model.ckpt-500
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 500: /tmp/tmp4c7ajrxs/model.ckpt-500
Eval result: {'loss': 0.38869256, 'global_step': 500}