Day #122 - Tensorflow Estimator API

• Manage data distribution for out of box
• Data Parallelism - Replicate your model on multiple workers

estimator = tf.estimator.LinearRegressor(...)
tf.estimator.train_and_evaluate(estimator,....)

Needed for running on multiple machines

#1. Estimator
#2. Run Config
#3. Training Spec
#4. Test Spec

estimator = tf.estimator.LinearRegressor(feature_columns=featcols,config=run_config)
..
tf.estimator.train_and_evaluate(estimator,train_spec,eval_spec)

#5. Checkpoints, Summary

run_config = tf.estimator.RunConfig(model_dir=output_dir,save_summary_steps=100,save_checkpoint_steps=2000)
estimator = tf.estimator.LinearRegressor(config=run_config,....)

#6. Using Data Sets

train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn,max_steps=5000)

#7. Eval Spec

tf.estimator.train_and_evaluate(estimator,train_spec,eval_spec)

#8. Evaluation Checkpoint

eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn,steps=100,throttle_secs=600,exporters=...)

#9. Measure for Test data

tf.estimator.train_and_evaluate(estimator,train_spec,eval_spec)

	run_config = tf.estimator.RunConfig(model_dir=output_dir,....)
	estimator = tf.estimator.LinearRegressor(featcols,config=run_config)
	train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn,max_steps=1000)
	export_latest = tf.estimator.LatestExporter(serving_input_receiver_fn=serving_input_fn)
	eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn,exporters=export_latest)
	tf.estimator.train_and_evaluate(estimator,train_spec,eval_spec)
	#Shuffling
	dataset = tf.data.Dataset.list_files("train.csv-*").shuffle(100).flat_map(tf.data.TextLineDataset).map(decode_csv)
	dataset = dataset.shuffle(1000).repeat(15).batch(128)

view raw tensor3.py hosted with ❤ by GitHub

Happy Learning!!!