Define instance key (index number) for Cloud machine learning prediction - tensorflow

Question

I followed the 'Getting Started' tutorial for Cloud Machine Learning Engine and deployed it. I can pass an input file containing JSON instances to Batch Prediction service and it returns a file containing the predictions. How can I pass an instance key (index number) through the application graph unaltered so that the prediction contain the key and I know which JSON prediction belongs to which JSON input? It probably can be done by adding/changing a few lines in the original tutorial code (also copy pasted below). Can someone help me with that? I am relatively new to Tensorflow so a detailed description will be greatly appreciated. A sample code or tutorial will be very helpful, too... The 'Getting Started' sample code contains two files copy pasted below:
model.py
# Copyright 2016 Google Inc. All Rights Reserved. Licensed under the Apache
# License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations under
# the License.
"""Define a Wide + Deep model for classification on structured data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import multiprocessing
import six
import tensorflow as tf
# Define the format of your input data including unused columns
CSV_COLUMNS = ['age', 'workclass', 'fnlwgt', 'education', 'education_num',
'marital_status', 'occupation', 'relationship', 'race', 'gender',
'capital_gain', 'capital_loss', 'hours_per_week',
'native_country', 'income_bracket']
CSV_COLUMN_DEFAULTS = [[0], [''], [0], [''], [0], [''], [''], [''], [''], [''],
[0], [0], [0], [''], ['']]
LABEL_COLUMN = 'income_bracket'
LABELS = [' <=50K', ' >50K']
# Define the initial ingestion of each feature used by your model.
# Additionally, provide metadata about the feature.
INPUT_COLUMNS = [
# Categorical base columns
# For categorical columns with known values we can provide lists
# of values ahead of time.
tf.feature_column.categorical_column_with_vocabulary_list(
'gender', [' Female', ' Male']),
tf.feature_column.categorical_column_with_vocabulary_list(
'race',
[' Amer-Indian-Eskimo', ' Asian-Pac-Islander',
' Black', ' Other', ' White']
),
tf.feature_column.categorical_column_with_vocabulary_list(
'education',
[' Bachelors', ' HS-grad', ' 11th', ' Masters', ' 9th',
' Some-college', ' Assoc-acdm', ' Assoc-voc', ' 7th-8th',
' Doctorate', ' Prof-school', ' 5th-6th', ' 10th',
' 1st-4th', ' Preschool', ' 12th']),
tf.feature_column.categorical_column_with_vocabulary_list(
'marital_status',
[' Married-civ-spouse', ' Divorced', ' Married-spouse-absent',
' Never-married', ' Separated', ' Married-AF-spouse', ' Widowed']),
tf.feature_column.categorical_column_with_vocabulary_list(
'relationship',
[' Husband', ' Not-in-family', ' Wife', ' Own-child', ' Unmarried',
' Other-relative']),
tf.feature_column.categorical_column_with_vocabulary_list(
'workclass',
[' Self-emp-not-inc', ' Private', ' State-gov',
' Federal-gov', ' Local-gov', ' ?', ' Self-emp-inc',
' Without-pay', ' Never-worked']
),
# For columns with a large number of values, or unknown values
# We can use a hash function to convert to categories.
tf.feature_column.categorical_column_with_hash_bucket(
'occupation', hash_bucket_size=100, dtype=tf.string),
tf.feature_column.categorical_column_with_hash_bucket(
'native_country', hash_bucket_size=100, dtype=tf.string),
# Continuous base columns.
tf.feature_column.numeric_column('age'),
tf.feature_column.numeric_column('education_num'),
tf.feature_column.numeric_column('capital_gain'),
tf.feature_column.numeric_column('capital_loss'),
tf.feature_column.numeric_column('hours_per_week'),
]
UNUSED_COLUMNS = set(CSV_COLUMNS) - {col.name for col in INPUT_COLUMNS} - \
{LABEL_COLUMN}
def build_estimator(config, embedding_size=8, hidden_units=None):
"""Build a wide and deep model for predicting income category.
Wide and deep models use deep neural nets to learn high level abstractions
about complex features or interactions between such features.
These models then combined the outputs from the DNN with a linear regression
performed on simpler features. This provides a balance between power and
speed that is effective on many structured data problems.
You can read more about wide and deep models here:
https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html
To define model we can use the prebuilt DNNCombinedLinearClassifier class,
and need only define the data transformations particular to our dataset, and
then
assign these (potentially) transformed features to either the DNN, or linear
regression portion of the model.
Args:
config: tf.contrib.learn.RunConfig defining the runtime environment for the
estimator (including model_dir).
embedding_size: int, the number of dimensions used to represent categorical
features when providing them as inputs to the DNN.
hidden_units: [int], the layer sizes of the DNN (input layer first)
learning_rate: float, the learning rate for the optimizer.
Returns:
A DNNCombinedLinearClassifier
"""
(gender, race, education, marital_status, relationship,
workclass, occupation, native_country, age,
education_num, capital_gain, capital_loss, hours_per_week) = INPUT_COLUMNS
# Build an estimator.
# Reused Transformations.
# Continuous columns can be converted to categorical via bucketization
age_buckets = tf.feature_column.bucketized_column(
age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
# Wide columns and deep columns.
wide_columns = [
# Interactions between different categorical features can also
# be added as new virtual features.
tf.feature_column.crossed_column(
['education', 'occupation'], hash_bucket_size=int(1e4)),
tf.feature_column.crossed_column(
[age_buckets, race, 'occupation'], hash_bucket_size=int(1e6)),
tf.feature_column.crossed_column(
['native_country', 'occupation'], hash_bucket_size=int(1e4)),
gender,
native_country,
education,
occupation,
workclass,
marital_status,
relationship,
age_buckets,
]
deep_columns = [
# Use indicator columns for low dimensional vocabularies
tf.feature_column.indicator_column(workclass),
tf.feature_column.indicator_column(education),
tf.feature_column.indicator_column(marital_status),
tf.feature_column.indicator_column(gender),
tf.feature_column.indicator_column(relationship),
tf.feature_column.indicator_column(race),
# Use embedding columns for high dimensional vocabularies
tf.feature_column.embedding_column(
native_country, dimension=embedding_size),
tf.feature_column.embedding_column(occupation, dimension=embedding_size),
age,
education_num,
capital_gain,
capital_loss,
hours_per_week,
]
return tf.estimator.DNNLinearCombinedClassifier(
config=config,
linear_feature_columns=wide_columns,
dnn_feature_columns=deep_columns,
dnn_hidden_units=hidden_units or [100, 70, 50, 25]
)
def parse_label_column(label_string_tensor):
"""Parses a string tensor into the label tensor
Args:
label_string_tensor: Tensor of dtype string. Result of parsing the
CSV column specified by LABEL_COLUMN
Returns:
A Tensor of the same shape as label_string_tensor, should return
an int64 Tensor representing the label index for classification tasks,
and a float32 Tensor representing the value for a regression task.
"""
# Build a Hash Table inside the graph
table = tf.contrib.lookup.index_table_from_tensor(tf.constant(LABELS))
# Use the hash table to convert string labels to ints and one-hot encode
return table.lookup(label_string_tensor)
# ************************************************************************
# YOU NEED NOT MODIFY ANYTHING BELOW HERE TO ADAPT THIS MODEL TO YOUR DATA
# ************************************************************************
def csv_serving_input_fn():
"""Build the serving inputs."""
csv_row = tf.placeholder(
shape=[None],
dtype=tf.string
)
features = parse_csv(csv_row)
features.pop(LABEL_COLUMN)
return tf.estimator.export.ServingInputReceiver(features, {'csv_row': csv_row})
def example_serving_input_fn():
"""Build the serving inputs."""
example_bytestring = tf.placeholder(
shape=[None],
dtype=tf.string,
)
feature_scalars = tf.parse_example(
example_bytestring,
tf.feature_column.make_parse_example_spec(INPUT_COLUMNS)
)
return tf.estimator.export.ServingInputReceiver(
features,
{'example_proto': example_bytestring}
)
# [START serving-function]
def json_serving_input_fn():
"""Build the serving inputs."""
inputs = {}
for feat in INPUT_COLUMNS:
inputs[feat.name] = tf.placeholder(shape=[None], dtype=feat.dtype)
return tf.estimator.export.ServingInputReceiver(inputs, inputs)
# [END serving-function]
SERVING_FUNCTIONS = {
'JSON': json_serving_input_fn,
'EXAMPLE': example_serving_input_fn,
'CSV': csv_serving_input_fn
}
def parse_csv(rows_string_tensor):
"""Takes the string input tensor and returns a dict of rank-2 tensors."""
# Takes a rank-1 tensor and converts it into rank-2 tensor
# Example if the data is ['csv,line,1', 'csv,line,2', ..] to
# [['csv,line,1'], ['csv,line,2']] which after parsing will result in a
# tuple of tensors: [['csv'], ['csv']], [['line'], ['line']], [[1], [2]]
row_columns = tf.expand_dims(rows_string_tensor, -1)
columns = tf.decode_csv(row_columns, record_defaults=CSV_COLUMN_DEFAULTS)
features = dict(zip(CSV_COLUMNS, columns))
# Remove unused columns
for col in UNUSED_COLUMNS:
features.pop(col)
return features
def input_fn(filenames,
num_epochs=None,
shuffle=True,
skip_header_lines=0,
batch_size=200):
"""Generates features and labels for training or evaluation.
This uses the input pipeline based approach using file name queue
to read data so that entire data is not loaded in memory.
Args:
filenames: [str] list of CSV files to read data from.
num_epochs: int how many times through to read the data.
If None will loop through data indefinitely
shuffle: bool, whether or not to randomize the order of data.
Controls randomization of both file order and line order within
files.
skip_header_lines: int set to non-zero in order to skip header lines
in CSV files.
batch_size: int First dimension size of the Tensors returned by
input_fn
Returns:
A (features, indices) tuple where features is a dictionary of
Tensors, and indices is a single Tensor of label indices.
"""
filename_dataset = tf.data.Dataset.from_tensor_slices(filenames)
if shuffle:
# Process the files in a random order.
filename_dataset = filename_dataset.shuffle(len(filenames))
# For each filename, parse it into one element per line, and skip the header
# if necessary.
dataset = filename_dataset.flat_map(
lambda filename: tf.data.TextLineDataset(filename).skip(skip_header_lines))
dataset = dataset.map(parse_csv)
if shuffle:
dataset = dataset.shuffle(buffer_size=batch_size * 10)
dataset = dataset.repeat(num_epochs)
dataset = dataset.batch(batch_size)
iterator = dataset.make_one_shot_iterator()
features = iterator.get_next()
return features, parse_label_column(features.pop(LABEL_COLUMN))
task.py
import argparse
import os
import trainer.model as model
import tensorflow as tf
from tensorflow.contrib.learn.python.learn.utils import (
saved_model_export_utils)
from tensorflow.contrib.training.python.training import hparam
def run_experiment(hparams):
"""Run the training and evaluate using the high level API"""
train_input = lambda: model.input_fn(
hparams.train_files,
num_epochs=hparams.num_epochs,
batch_size=hparams.train_batch_size
)
# Don't shuffle evaluation data
eval_input = lambda: model.input_fn(
hparams.eval_files,
batch_size=hparams.eval_batch_size,
shuffle=False
)
train_spec = tf.estimator.TrainSpec(train_input,
max_steps=hparams.train_steps
)
exporter = tf.estimator.FinalExporter('census',
model.SERVING_FUNCTIONS[hparams.export_format])
eval_spec = tf.estimator.EvalSpec(eval_input,
steps=hparams.eval_steps,
exporters=[exporter],
name='census-eval'
)
run_config = tf.estimator.RunConfig()
run_config = run_config.replace(model_dir=hparams.job_dir)
print('model dir {}'.format(run_config.model_dir))
estimator = model.build_estimator(
embedding_size=hparams.embedding_size,
# Construct layers sizes with exponetial decay
hidden_units=[
max(2, int(hparams.first_layer_size *
hparams.scale_factor**i))
for i in range(hparams.num_layers)
],
config=run_config
)
tf.estimator.train_and_evaluate(estimator,
train_spec,
eval_spec)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Input Arguments
parser.add_argument(
'--train-files',
help='GCS or local paths to training data',
nargs='+',
required=True
)
parser.add_argument(
'--num-epochs',
help="""\
Maximum number of training data epochs on which to train.
If both --max-steps and --num-epochs are specified,
the training job will run for --max-steps or --num-epochs,
whichever occurs first. If unspecified will run for --max-steps.\
""",
type=int,
)
parser.add_argument(
'--train-batch-size',
help='Batch size for training steps',
type=int,
default=40
)
parser.add_argument(
'--eval-batch-size',
help='Batch size for evaluation steps',
type=int,
default=40
)
parser.add_argument(
'--eval-files',
help='GCS or local paths to evaluation data',
nargs='+',
required=True
)
# Training arguments
parser.add_argument(
'--embedding-size',
help='Number of embedding dimensions for categorical columns',
default=8,
type=int
)
parser.add_argument(
'--first-layer-size',
help='Number of nodes in the first layer of the DNN',
default=100,
type=int
)
parser.add_argument(
'--num-layers',
help='Number of layers in the DNN',
default=4,
type=int
)
parser.add_argument(
'--scale-factor',
help='How quickly should the size of the layers in the DNN decay',
default=0.7,
type=float
)
parser.add_argument(
'--job-dir',
help='GCS location to write checkpoints and export models',
required=True
)
# Argument to turn on all logging
parser.add_argument(
'--verbosity',
choices=[
'DEBUG',
'ERROR',
'FATAL',
'INFO',
'WARN'
],
default='INFO',
)
# Experiment arguments
parser.add_argument(
'--train-steps',
help="""\
Steps to run the training job for. If --num-epochs is not specified,
this must be. Otherwise the training job will run indefinitely.\
""",
type=int
)
parser.add_argument(
'--eval-steps',
help='Number of steps to run evalution for at each checkpoint',
default=100,
type=int
)
parser.add_argument(
'--export-format',
help='The input format of the exported SavedModel binary',
choices=['JSON', 'CSV', 'EXAMPLE'],
default='JSON'
)
args = parser.parse_args()
# Set python level verbosity
tf.logging.set_verbosity(args.verbosity)
# Set C++ Graph Execution level verbosity
os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(
tf.logging.__dict__[args.verbosity] / 10)
# Run the training job
hparams=hparam.HParams(**args.__dict__)
run_experiment(hparams)

Answer 1

In Tensorflow 2.x use Keras to write a new export signature that takes the original inputs plus the keys. Note that you have to define the shape(s) of your original input appropriately
#tf.function(input_signature=[tf.TensorSpec([None, 1], dtype=tf.float32), tf.TensorSpec([None, 1], dtype=tf.int32)])
def keyed_prediction(originput, key):
pred = model(originput, training=False)
return {
'price': pred,
'key': key
}
model.save(EXPORT_PATH, signatures={'serving_default': keyed_prediction})
In Tensorflow 1.x modify the export signature:
config = estimator.config
def model_fn2(features, labels, mode):
estimatorSpec = estimator._call_model_fn(features, labels, mode, config=config)
if estimatorSpec.export_outputs:
for ekey in ['predict', 'serving_default']:
estimatorSpec.export_outputs[ekey] = \
tf.estimator.export.PredictOutput(estimatorSpec.predictions)
return estimatorSpec
return tf.estimator.Estimator(model_fn=model_fn2, config=config)
See:
https://towardsdatascience.com/how-to-extend-a-canned-tensorflow-estimator-to-add-more-evaluation-metrics-and-to-pass-through-ddf66cd3047d

Answer 2

Currently, passing through keys requires exporting a model capable of handling keys. Unfortunately, in the current state of affairs, this also requires that the training data contain a column for keys.
The function tf.contrib.estimators.forward_features() is intended for this purpose, but it assumes the key is also present in your training data. In that case, you would simply add this line to model.py
estimator = model.build_estimator(...)
estimator = tf.contrib.estimators.forward_features(estimator, "key")
Where 'key' is the name of the column containing the key. (So you would also have to add another column to the CSV input files and modify the CSV_COLUMNS and CSV_DEFAULTS as appropriate).