tensorflow-qnd-0.0.16.tar.gz

# tensorflow-qnd [](https://badge.fury.io/py/tensorflow-qnd) [](setup.py) [](https://app.wercker.com/project/byKey/28ed86fae4933770c92881a466e81929) [](https://unlicense.org) 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](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/learn/python/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](https://www.tensorflow.org/how_tos/distributed/) - 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](https://raviqqe.github.io/tensorflow-qnd/qnd). ## Examples `train.py` (command script): ```python 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(): logging.getLogger().setLevel(logging.INFO) train_and_evaluate(model, mnist.read_file) if __name__ == "__main__": main() ``` `mnist.py` (module): ```python 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) image.set_shape([28**2]) return tf.to_float(image) / 255 - 0.5, features["label"] def minimize(loss): return tf.train.AdamOptimizer().minimize( loss, tf.contrib.framework.get_global_step()) 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, qnd.FLAGS.hidden_layer_size) 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( tf.nn.sparse_softmax_cross_entropy_with_logits(h, number)) return predictions, loss, minimize(loss), { "accuracy": tf.contrib.metrics.streaming_accuracy(predictions, number)[1], } return model ``` With the code above, you can create a command with the following interface. ``` usage: train.py [-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) --log_device_placement 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 them. ## 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 step. ## Contributing Please send issues about any bugs, feature requests or questions, or pull requests. ## License [The Unlicense](https://unlicense.org)