"""Train and Eval the MNIST network. This version is like fully_connected_feed.py but uses data converted to a TFRecords file containing tf.train.Example protocol buffers. See tensorflow/g3doc/how_tos/reading_data.md#reading-from-files for context. YOU MUST run convert_to_records before running this (but you only need to run it once). """ import os.path import time import tensorflow.python.platform import numpy import tensorflow as tf from tensorflow.g3doc.tutorials.mnist import mnist # Basic model parameters as external flags. flags = tf.app.flags FLAGS = flags.FLAGS flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.') flags.DEFINE_integer('num_epochs', 2, 'Number of epochs to run trainer.') flags.DEFINE_integer('hidden1', 128, 'Number of units in hidden layer 1.') flags.DEFINE_integer('hidden2', 32, 'Number of units in hidden layer 2.') flags.DEFINE_integer('batch_size', 100, 'Batch size.') flags.DEFINE_string('train_dir', 'data', 'Directory with the training data.') # Constants used for dealing with the files, matches convert_to_records. TRAIN_FILE = 'train.tfrecords' VALIDATION_FILE = 'validation.tfrecords' def read_and_decode(filename_queue): reader = tf.TFRecordReader() _, serialized_example = reader.read(filename_queue) features = tf.parse_single_example( serialized_example, dense_keys=['image_raw', 'label'], # Defaults are not specified since both keys are required. dense_types=[tf.string, tf.int64]) # Convert from a scalar string tensor (whose single string has # length mnist.IMAGE_PIXELS) to a uint8 tensor with shape # [mnist.IMAGE_PIXELS]. image = tf.decode_raw(features['image_raw'], tf.uint8) image.set_shape([mnist.IMAGE_PIXELS]) # OPTIONAL: Could reshape into a 28x28 image and apply distortions # here. Since we are not applying any distortions in this # example, and the next step expects the image to be flattened # into a vector, we don't bother. # Convert from [0, 255] -> [-0.5, 0.5] floats. image = tf.cast(image, tf.float32) * (1. / 255) - 0.5 # Convert label from a scalar uint8 tensor to an int32 scalar. label = tf.cast(features['label'], tf.int32) return image, label def inputs(train, batch_size, num_epochs): """Reads input data num_epochs times. Args: train: Selects between the training (True) and validation (False) data. batch_size: Number of examples per returned batch. num_epochs: Number of times to read the input data, or 0/None to train forever. Returns: A tuple (images, labels), where: * images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS] in the range [-0.5, 0.5]. * labels is an int32 tensor with shape [batch_size] with the true label, a number in the range [0, mnist.NUM_CLASSES). Note that an tf.train.QueueRunner is added to the graph, which must be run using e.g. tf.train.start_queue_runners(). """ if not num_epochs: num_epochs = None filename = os.path.join(FLAGS.train_dir, TRAIN_FILE if train else VALIDATION_FILE) with tf.name_scope('input'): filename_queue = tf.train.string_input_producer( [filename], num_epochs=num_epochs) # Even when reading in multiple threads, share the filename # queue. image, label = read_and_decode(filename_queue) # Shuffle the examples and collect them into batch_size batches. # (Internally uses a RandomShuffleQueue.) # We run this in two threads to avoid being a bottleneck. images, sparse_labels = tf.train.shuffle_batch( [image, label], batch_size=batch_size, num_threads=2, capacity=1000 + 3 * batch_size, # Ensures a minimum amount of shuffling of examples. min_after_dequeue=1000) return images, sparse_labels def run_training(): """Train MNIST for a number of steps.""" # Tell TensorFlow that the model will be built into the default Graph. with tf.Graph().as_default(): # Input images and labels. images, labels = inputs(train=True, batch_size=FLAGS.batch_size, num_epochs=FLAGS.num_epochs) # Build a Graph that computes predictions from the inference model. logits = mnist.inference(images, FLAGS.hidden1, FLAGS.hidden2) # Add to the Graph the loss calculation. loss = mnist.loss(logits, labels) # Add to the Graph operations that train the model. train_op = mnist.training(loss, FLAGS.learning_rate) # The op for initializing the variables. init_op = tf.initialize_all_variables(); # Create a session for running operations in the Graph. sess = tf.Session() # Initialize the variables (the trained variables and the # epoch counter). sess.run(init_op) # Start input enqueue threads. coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) try: step = 0 while not coord.should_stop(): start_time = time.time() # Run one step of the model. The return values are # the activations from the `train_op` (which is # discarded) and the `loss` op. To inspect the values # of your ops or variables, you may include them in # the list passed to sess.run() and the value tensors # will be returned in the tuple from the call. _, loss_value = sess.run([train_op, loss]) duration = time.time() - start_time # Print an overview fairly often. if step % 100 == 0: print 'Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration) step += 1 except tf.errors.OutOfRangeError: 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__': tf.app.run()