How can Tensorflow be used with Estimator to compile the model using Python?

TensorFlow Estimators provide a high-level API for building and training machine learning models. The train() method compiles and trains the estimator model using the specified input function and training steps.

Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?

What is TensorFlow Estimator?

An Estimator is TensorFlow's high-level representation of a complete model. It is designed for easy scaling and asynchronous training. Estimators provide methods to train, evaluate, predict, and export models for serving.

Training Model with Iris Dataset

The model is trained using the iris dataset with 4 features and one label ?

• sepal length
• sepal width
• petal length
• petal width

Example

Here's how to compile and train a model using TensorFlow Estimator ?

# Training the classifier model
print("The model is being trained")
classifier.train(
    input_fn=lambda: input_fn(train, train_y, training=True), 
    steps=5000
)

Code credit − https://www.tensorflow.org/tutorials/estimator/premade#first_things_first

Output

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/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: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:Saving checkpoints for 0 into /tmp/tmpbhg2uvbr/model.ckpt.
INFO:tensorflow:loss = 1.1140382, step = 0
INFO:tensorflow:global_step/sec: 312.415
INFO:tensorflow:loss = 0.8781501, step = 100 (0.321 sec)
INFO:tensorflow:global_step/sec: 375.535
INFO:tensorflow:loss = 0.80712265, step = 200 (0.266 sec)
...
INFO:tensorflow:loss = 0.36297452, step = 5000
INFO:tensorflow:Saving checkpoints for 5000 into /tmp/tmpbhg2uvbr/model.ckpt.
INFO:tensorflow:Loss for final step: 0.36297452.
<tensorflow_estimator.python.estimator.canned.dnn.DNNClassifierV2 at 0x7fc9983ed470>

Key Components

The training process involves several key components ?

• input_fn − Function that provides data to the model
• steps − Number of training steps to perform
• Checkpoints − Model state saved during training for recovery
• Loss monitoring − Track training progress through loss values

Training Process

Once an Estimator object is created, you can perform the following operations ?

• Train the model using train() method
• Evaluate the trained model using evaluate() method
• Make predictions using predict() method
• Continue training by calling train() again

Conclusion

TensorFlow Estimators simplify model training through the train() method. The training process automatically handles checkpointing, loss monitoring, and model optimization, making it easy to build scalable machine learning models.