skip to navigation
skip to content

tensorflow-qnd 0.1.0

Quick and Dirty TensorFlow command framework

# tensorflow-qnd

[![PyPI version](](
[![Python versions](](
[![wercker status]( "wercker status")](

Quick and Dirty TensorFlow command framework

tensorflow-qnd is a TensorFlow framework to create commands to train and
evaluate models and make inference with them.
The framework is built on top of
[TF Learn](
Especially if you are working on research projects using TensorFlow, you can
remove most of boilerplate code with this framework.
All you need to do is to define a model constructor `model_fn` and input
producer(s) `input_fn` to feed a dataset to the model.

## Features

- Creation of commands
- To train and evaluate models
- To infer labels or regression values with trained models
- Configuration of command line arguments to set hyperparameters of models etc.
- [Distributed TensorFlow](
- Just set an optional argument `distributed ` of `def_train_and_evaluate()`
as `True` (i.e. `def_train_and_evaluate(distributed=True)`) to enable it.
- Supports only data parallel training
- Only for training and evaluation but not for inference

## Installation

Python 3.5+ and TensorFlow 0.12+ are required.

pip3 install --user --upgrade tensorflow-qnd

## Usage

1. Add command line arguments with `add_flag` and `add_required_flag` functions.
2. Define a `train_and_evaluate` or `infer` function with
`def_train_and_evaluate` or `def_infer` function
3. Pass `model_fn` (model constructor) and `input_fn` (input producer) functions
to that defined function.
4. Run the script with appropriate command line arguments.

For more information, see [documentation](

## Examples

`` (command script):

import logging
import os

import qnd

import mnist

train_and_evaluate = qnd.def_train_and_evaluate(
distributed=("distributed" in os.environ))

model = mnist.def_model()

def main():
train_and_evaluate(model, mnist.read_file)

if __name__ == "__main__":

`` (module):

import qnd
import tensorflow as tf

def read_file(filename_queue):
_, serialized = tf.TFRecordReader().read(filename_queue)

scalar_feature = lambda dtype: tf.FixedLenFeature([], dtype)

features = tf.parse_single_example(serialized, {
"image_raw": scalar_feature(tf.string),
"label": scalar_feature(tf.int64),

image = tf.decode_raw(features["image_raw"], tf.uint8)

return tf.to_float(image) / 255 - 0.5, features["label"]

def minimize(loss):
return tf.train.AdamOptimizer().minimize(

def def_model():
qnd.add_flag("hidden_layer_size", type=int, default=64,
help="Hidden layer size")

def model(image, number=None, mode=None):
h = tf.contrib.layers.fully_connected(image,
h = tf.contrib.layers.fully_connected(h, 10, activation_fn=None)

predictions = tf.argmax(h, axis=1)

if mode == tf.contrib.learn.ModeKeys.INFER:
return predictions

loss = tf.reduce_mean(

return predictions, loss, minimize(loss), {
"accuracy": tf.contrib.metrics.streaming_accuracy(predictions,

return model

With the code above, you can create a command with the following interface.

usage: [-h] [--output_dir OUTPUT_DIR] [--train_steps TRAIN_STEPS]
[--eval_steps EVAL_STEPS]
[--min_eval_frequency MIN_EVAL_FREQUENCY]
[--num_cores NUM_CORES] [--log_device_placement]
[--save_summary_steps SAVE_SUMMARY_STEPS]
[--save_checkpoints_steps SAVE_CHECKPOINTS_STEPS]
[--keep_checkpoint_max KEEP_CHECKPOINT_MAX]
[--batch_size BATCH_SIZE]
[--batch_queue_capacity BATCH_QUEUE_CAPACITY]
[--num_batch_threads NUM_BATCH_THREADS] --train_file
TRAIN_FILE [--filename_queue_capacity FILENAME_QUEUE_CAPACITY]
--eval_file EVAL_FILE [--hidden_layer_size HIDDEN_LAYER_SIZE]

optional arguments:
-h, --help show this help message and exit
--output_dir OUTPUT_DIR
Directory where checkpoint and event files are stored
(default: output)
--train_steps TRAIN_STEPS
Maximum number of train steps (default: None)
--eval_steps EVAL_STEPS
Maximum number of eval steps (default: None)
--min_eval_frequency MIN_EVAL_FREQUENCY
Minimum evaluation frequency in number of train steps
(default: 1)
--num_cores NUM_CORES
Number of CPU cores used. 0 means use of a default
value. (default: 0)
If specified, log device placement information
(default: False)
--save_summary_steps SAVE_SUMMARY_STEPS
Number of steps every time of which summary is saved
(default: 100)
--save_checkpoints_steps SAVE_CHECKPOINTS_STEPS
Number of steps every time of which a model is saved
(default: None)
--keep_checkpoint_max KEEP_CHECKPOINT_MAX
Max number of kept checkpoint files (default: 86058)
--batch_size BATCH_SIZE
Mini-batch size (default: 64)
--batch_queue_capacity BATCH_QUEUE_CAPACITY
Batch queue capacity (default: 1024)
--num_batch_threads NUM_BATCH_THREADS
Number of threads used to create batches (default: 16)
--train_file TRAIN_FILE
File path of train data file(s). A glob is available.
(e.g. train/*.tfrecords) (default: None)
--filename_queue_capacity FILENAME_QUEUE_CAPACITY
Capacity of filename queues of train, eval and infer
data (default: 32)
--eval_file EVAL_FILE
File path of eval data file(s). A glob is available.
(e.g. eval/*.tfrecords) (default: None)
--hidden_layer_size HIDDEN_LAYER_SIZE
Hidden layer size (default: 64)

Explore [examples](examples) directory for more information and see how to run

## Caveats

### Necessary update of a global step variable

As done in [examples](examples), you must get a global step variable with
`tf.contrib.framework.get_global_step()` and update (increment) it in each
training step.

### Use streaming metrics for `eval_metric_ops`

When non-streaming ones such as `tf.contrib.metrics.accuracy` are used in a
return value `eval_metric_ops` of your `model_fn` or as arguments of
`ModelFnOps`, their values will be ones of the last batch in every evaluation

## Contributing

Please send issues about any bugs, feature requests or questions, or pull

## License

[The Unlicense](

File Type Py Version Uploaded on Size
tensorflow-qnd-0.1.0.tar.gz (md5) Source 2017-04-21 13KB
tensorflow_qnd-0.1.0-py3-none-any.whl (md5) Python Wheel py3 2017-04-21 18KB
tensorflow_qnd-0.1.0-py3.6.egg (md5) Python Egg 3.6 2017-04-21 32KB