slice_input_producer, allowing passing in input data for inference

fully_connected_preloaded_var.py

# Copyright 2015 The TensorFlow Authors. 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.
# ==============================================================================
"""Trains the MNIST network using preloaded data stored in a variable.

Run using bazel:

bazel run --config opt \
    <...>/tensorflow/examples/how_tos/reading_data:fully_connected_preloaded_var

or, if installed via pip:

cd tensorflow/examples/how_tos/reading_data
python fully_connected_preloaded_var.py
"""
from __future__ import absolute_import, division, print_function

import argparse
import sys
import time

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data, mnist

# Basic model parameters as external flags.
FLAGS = None


def run_training():
    """Train MNIST for a number of epochs."""
    # Get the sets of images and labels for training, validation, and
    # test on MNIST.
    data_sets = input_data.read_data_sets(FLAGS.train_dir, FLAGS.fake_data)

    # Tell TensorFlow that the model will be built into the default Graph.
    with tf.Graph().as_default():
        with tf.name_scope('input'):
            # Input data
            images_initializer = tf.placeholder(
                dtype=data_sets.train.images.dtype, shape=data_sets.train.images.shape)
            labels_initializer = tf.placeholder(
                dtype=data_sets.train.labels.dtype, shape=data_sets.train.labels.shape)
            input_images = tf.Variable(images_initializer, trainable=False, collections=[])
            input_labels = tf.Variable(labels_initializer, trainable=False, collections=[])

            image, label = tf.train.slice_input_producer(
                [input_images, input_labels], num_epochs=FLAGS.num_epochs)
            label = tf.cast(label, tf.int32)
            images, labels = tf.train.batch([image, label], batch_size=FLAGS.batch_size)

            images = tf.placeholder_with_default(images, [None, 784])

        # Build a Graph that computes predictions from the inference model.
        logits = mnist.inference(images, FLAGS.hidden1, FLAGS.hidden2)

        # Add to the Graph the Ops for loss calculation.
        loss = mnist.loss(logits, labels)

        # Add to the Graph the Ops that calculate and apply gradients.
        train_op = mnist.training(loss, FLAGS.learning_rate)

        # Add the Op to compare the logits to the labels during evaluation.
        eval_correct = mnist.evaluation(logits, labels)

        predictions = tf.nn.softmax(logits)

        # Build the summary operation based on the TF collection of Summaries.
        summary_op = tf.summary.merge_all()

        # Create a saver for writing training checkpoints.
        saver = tf.train.Saver()

        # Create the op for initializing variables.
        init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())

        # Create a session for running Ops on the Graph.
        sess = tf.Session()

        # Run the Op to initialize the variables.
        sess.run(init_op)
        sess.run(input_images.initializer, feed_dict={images_initializer: data_sets.train.images})
        sess.run(input_labels.initializer, feed_dict={labels_initializer: data_sets.train.labels})

        # Instantiate a SummaryWriter to output summaries and the Graph.
        summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)

        # Start input enqueue threads.
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess=sess, coord=coord)

        # And then after everything is built, start the training loop.
        try:
            step = 0
            while not coord.should_stop():
                start_time = time.time()

                # Run one step of the model.
                _, loss_value = sess.run([train_op, loss])

                print(sess.run([predictions], feed_dict={images: [[0] * 784]}))

                duration = time.time() - start_time

                # Write the summaries and print an overview fairly often.
                if step % 100 == 0:
                    # Print status to stdout.
                    print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
                    # Update the events file.
                    summary_str = sess.run(summary_op)
                    summary_writer.add_summary(summary_str, step)
                    step += 1

                # Save a checkpoint periodically.
                if (step + 1) % 1000 == 0:
                    print('Saving')
                    saver.save(sess, FLAGS.train_dir, global_step=step)

                step += 1
        except tf.errors.OutOfRangeError:
            print('Saving')
            saver.save(sess, FLAGS.train_dir, global_step=step)
            print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))
        finally:
            # When done, ask the threads to stop.
            coord.request_stop()

        # Wait for threads to finish.
        coord.join(threads)
        sess.close()


def main(_):
    run_training()


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--learning_rate', type=float, default=0.01, help='Initial learning rate.')
    parser.add_argument(
        '--num_epochs', type=int, default=2, help='Number of epochs to run trainer.')
    parser.add_argument(
        '--hidden1', type=int, default=128, help='Number of units in hidden layer 1.')
    parser.add_argument(
        '--hidden2', type=int, default=32, help='Number of units in hidden layer 2.')
    parser.add_argument(
        '--batch_size',
        type=int,
        default=100,
        help='Batch size.  Must divide evenly into the dataset sizes.')
    parser.add_argument(
        '--train_dir', type=str, default='/tmp/data', help='Directory to put the training data.')
    parser.add_argument(
        '--fake_data',
        default=False,
        help='If true, uses fake data for unit testing.',
        action='store_true')
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)