[Coursera] Intro to TensorFlow (2)

Machine Learning with TensorFlow on GCP

Posted by cyc1am3n on October 23, 2018

Coursera 강의 “Machine Learning with TensorFlow on Google Cloud Platform” 중 세 번째 코스인 Intro to TensorFlow의 2주차 강의노트입니다.



Estimator API


  • Estimators wrap up a large amount of boilerplate code, on top of the model itself.


  • From small to big to prod with the Estimator API
    • Quick model
    • Checkpointing
    • Out-of-memory datasets
    • Train / eval / monitor
    • Distributed training
    • Hyper-parameter tuning on ML-Engine
    • Production: serving predictions from a trained model
  • Pre-made estimators that can all be used in the same way.

tf.estimator.Estimator
tf.estimator.Estimator


Pre-made Estimators


  • Feature columns tell the model what inputs to expect
import tensorflow as tf

featcols = [ tf.feature_column.numeric_column("sq_footage"), tf.feature_column.categorical_column_with_vocabulary_list("type", ["house", "apt"]) ]

model = tf.estimator.LinearRegressor(featcols)
  • Under the hood: feature columns take care of packing the inputs into the input vector of the model
    • tf.feature_column.bucketized_column
    • tf.feature_column.embedding_column
    • tf.feature_column.crossed_column
    • tf.feature_column.categorical_column_with_hash_bucket
  • Training: feed in training input data and train for 100 epochs
def train_input_fn():
	features = {"sq_footage": [1000,   2000,   3000,   1000,   2000,   3000],
			  "type":       ["house", "house", "house", "apt", "apt", "apt"]}
       labels =                              [500,    1000,   1500,   700,    1300,   1900]
       return features, labels

model.train(train_input_fn, steps=100)
  • Predictions: once trained, the model can be used for prediction
def predict_input_fn():
	features = {"sq_footage": [1500, 1800],
				"type":       ["house", "apt"]}
	return features

predictions = model.predict(predict_input_fn)
  • To use a different pre-made estimator, just change the class name and supply appropriate parameters
model = tf.estimator.DNNRegressor(featcols, hidden_units=[3, 2])


Checkpointing


  • Model checkpoints
    1. Continue training
    2. Resume on failure
    3. Predict from trained model
  • Estimators automatically checkpoint training
model = tf.estimator.LinearRegressor(featcols, './model_trained') # Where to put the checkpoints
model.train(train_input_fn, steps=100)
%ls model_trained

checkpoint                                                     model.ckpt-100.meta
graph.pbtxt                                                    model.ckpt-1.data-00000-of-00001
model.ckpt-100.data-00000-of-00001       model.ckpt-1.index
model.ckpt-100.index                                   model.ckpt-1.meta
  • We can now restore and predict with the model
trained_model = tf.estimator.LinearRegressor(featcols, './model_trained')
predictions = trained_model.predict(pred_input_fn)
INFO:tensorflow:Restoring parameters from
model_trained/model.ckpt-100

{'predictions': array([855.93], dtype=float32)}
{'predictions': array([859.07], dtype=float32)}
  • Training also resumes from the last checkpoint


Training on in-memory datasets


  • In memory data: usually numpy arrays or Pandas dataframes
    • tf.estimator.inputs.numpy_input_fn
    • tf.estimator.inputs.pandas_input_fn
  • Training happens until input is exhausted or number of steps is reached
def pandas_train_input_fn(df): # a Pandas dataframe
	return tf.estimator.inputs.pandas_input_fn(
			x = df,
			y = df['price'],
			batch_size=128,
			num_epochs=10,
			shuffle=True
	)

# Trains until input exhausted (10 epochs) starting from checkpoint
model.train(pandas_train_input_fn(df))
# 1000 additional steps from checkpoint
model.train(pandas_train_fn(df), steps=1000)
# 1000 steps - might be nothing if checkpoint already there
model.train(pandas_train_input_fn(df), max_steps=1000)
  • To add a new feature, add it to the list of feature columns and make sure it is present in data frame


Train on large datasets with Dataset API


  • Real World ML Models

Reak World ML Models
Reak World ML Models

  • Out-of memory datasets tend to be sharded into multiple files
  • Datasets can be created from different file formats. They generate input functions for Estimators
  • Read one CSV file using TextLineDataset
  • Datasets handle shuffling, epochs, batching, …
  • They support arbitrary transformations with map()
  • Datasets help create input_fn’s for Estimators
def decode_line(row):
	cols = tf.decode_csv(row, recode_defaults=[[0],['house'],[0]])
	features = {'sq_footage': cols[0], 'type': cols[1]}
	label = cols[2] # price
	return features, label

dataset = tf.data.TextLineDataset("train_1.csv").map(decode_line)

dataset = dataset.shuffle(1000).repeat(15).batch(128)

def input_fn():
	features, label = dataset.make_one_shot_iterator().get_next()
	return features, label

model.train(input_fn)
  • All the tf.commands that you write in Python do not actually process any data, they just build graphs.
  • Common Misconceptions about input_fn
    1. Input functions called only once
    2. Input functions return tf nodes (not data)
  • The real benefit of Dataset is that you can do more than just ingest data
dataset = tf.data.TextLineDataset(filename)\
							.skip(num_header_lines)\
							.map(add_key)\
							.map(lambda feats, labels: preproc(feats), labels)
							.filter(is_valid)\
							.cache()


Big jobs, Distributed training


  • estimator.train_and_evaluate is the preferred method for training real-world models.
  • data parallelism = replicate your model on multiple workers

Distributed training using dataparallelism
Distributed training using dataparallelism

  • estimator.train_and_evaluate is the preferred method for training real-world models
estimator = tf.estimator.LineRegressor(
						feature_colimns=featcols,
						config=run_config)
...
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
  • RunConfig tells the estimator where and how often to write Checkpoints and Tensorboard logs (“summaries”)
run_config = tf.estimator.RunConfig(
						model_dir=output_dir,
						save_summary_steps=100,
						save_checkpoints_steps=2000)

estimator = tf.estimator.LineRegressor(config=run_config, ...)
  • The TrainSpec tells the estimator how to get training data
train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn, max_steps=50000)
...
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
  • The EvalSpec controls the evaluation and the checkpointing of the model since they happen at the same time
eval_spec = tf.estimator.EvalSpec(
					input_fn=eval_input_fn,
					steps=100, # evals on 100 batches
					throttle_secs=600, # eval no more than every 10 min
					exporters=...)

tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
  • Shuffling is even more important in distributed training
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)


Monitoring with TensorBoard


  • Point Tensorboard to your output directory and the dashboards appear in your browser at localhost:6006
  • Pre-made Estimators export relevant metrics, embedding, histograms, etc. for TensorBoard, so there is nothing more to do

The dashboard for the graph
The dashboard for the graph

  • If you are writing a custom Estimator model, you can add summaries for Tensorboard with a single line.
    • Sprinkle appropriate summary ops throughout your code:
    • tf.summary.scalar
    • tf.summary.image
    • tf.summary.audio
    • tf.summary.text
    • tf.summary.histogram
tf.summary.scalar('meanVarl', tf.reduce_mean(varl))
...
tf.summary.text('outClass', stringvar)


Serving Input Function


  • Recap with all the code
run_config = tf.estimator.RunConfig(model_dir=output_dir, ...)

estimator = tf.estimator.LineRegressor(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)
  • Serving and training-time inputs are often very different


  • Serving input function transforms from parsed JSON data to the data your model expects


  • The exported model is ready to deploy
  • Example serving input function that decodes JPEGs
def serving_input_fn():
	json = {'jpeg_bytes': tf.placeholder(tf.string, [None])}

def decode(jpeg):
	pixels = tf.image.decode_jpeg(jpeg, channels=3)
	return pixels

pics = tf.map_fn(decode, json['jpeg_bytes'], dtype=tf.unit8)

features = {'pics': pics}
return tf.estimator.export.ServingInputReceiver(features, json)