My first implementation of logistic regression model for mnist, based on the code on Fundamentals of Deep Learning

input_data.py

# Copyright 2015 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.
# ==============================================================================
"""Functions for downloading and reading MNIST data."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import gzip
import os
import numpy
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf
SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'
def maybe_download(filename, work_directory):
  """Download the data from Yann's website, unless it's already here."""
  if not os.path.exists(work_directory):
    os.mkdir(work_directory)
  filepath = os.path.join(work_directory, filename)
  if not os.path.exists(filepath):
    filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
    statinfo = os.stat(filepath)
    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
  return filepath
def _read32(bytestream):
  dt = numpy.dtype(numpy.uint32).newbyteorder('>')
  return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
def extract_images(filename):
  """Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
  print('Extracting', filename)
  with gzip.open(filename) as bytestream:
    magic = _read32(bytestream)
    if magic != 2051:
      raise ValueError(
          'Invalid magic number %d in MNIST image file: %s' %
          (magic, filename))
    num_images = _read32(bytestream)
    rows = _read32(bytestream)
    cols = _read32(bytestream)
    buf = bytestream.read(rows * cols * num_images)
    data = numpy.frombuffer(buf, dtype=numpy.uint8)
    data = data.reshape(num_images, rows, cols, 1)
    return data
def dense_to_one_hot(labels_dense, num_classes=10):
  """Convert class labels from scalars to one-hot vectors."""
  num_labels = labels_dense.shape[0]
  index_offset = numpy.arange(num_labels) * num_classes
  labels_one_hot = numpy.zeros((num_labels, num_classes))
  labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
  return labels_one_hot
def extract_labels(filename, one_hot=False):
  """Extract the labels into a 1D uint8 numpy array [index]."""
  print('Extracting', filename)
  with gzip.open(filename) as bytestream:
    magic = _read32(bytestream)
    if magic != 2049:
      raise ValueError(
          'Invalid magic number %d in MNIST label file: %s' %
          (magic, filename))
    num_items = _read32(bytestream)
    buf = bytestream.read(num_items)
    labels = numpy.frombuffer(buf, dtype=numpy.uint8)
    if one_hot:
      return dense_to_one_hot(labels)
    return labels
class DataSet(object):
  def __init__(self, images, labels, fake_data=False, one_hot=False,
               dtype=tf.float32):
    """Construct a DataSet.
    one_hot arg is used only if fake_data is true.  `dtype` can be either
    `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
    `[0, 1]`.
    """
    dtype = tf.as_dtype(dtype).base_dtype
    if dtype not in (tf.uint8, tf.float32):
      raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
                      dtype)
    if fake_data:
      self._num_examples = 10000
      self.one_hot = one_hot
    else:
      assert images.shape[0] == labels.shape[0], (
          'images.shape: %s labels.shape: %s' % (images.shape,
                                                 labels.shape))
      self._num_examples = images.shape[0]
      # Convert shape from [num examples, rows, columns, depth]
      # to [num examples, rows*columns] (assuming depth == 1)
      assert images.shape[3] == 1
      images = images.reshape(images.shape[0],
                              images.shape[1] * images.shape[2])
      if dtype == tf.float32:
        # Convert from [0, 255] -> [0.0, 1.0].
        images = images.astype(numpy.float32)
        images = numpy.multiply(images, 1.0 / 255.0)
    self._images = images
    self._labels = labels
    self._epochs_completed = 0
    self._index_in_epoch = 0
  @property
  def images(self):
    return self._images
  @property
  def labels(self):
    return self._labels
  @property
  def num_examples(self):
    return self._num_examples
  @property
  def epochs_completed(self):
    return self._epochs_completed
  def next_batch(self, batch_size, fake_data=False):
    """Return the next `batch_size` examples from this data set."""
    if fake_data:
      fake_image = [1] * 784
      if self.one_hot:
        fake_label = [1] + [0] * 9
      else:
        fake_label = 0
      return [fake_image for _ in xrange(batch_size)], [
          fake_label for _ in xrange(batch_size)]
    start = self._index_in_epoch
    self._index_in_epoch += batch_size
    if self._index_in_epoch > self._num_examples:
      # Finished epoch
      self._epochs_completed += 1
      # Shuffle the data
      perm = numpy.arange(self._num_examples)
      numpy.random.shuffle(perm)
      self._images = self._images[perm]
      self._labels = self._labels[perm]
      # Start next epoch
      start = 0
      self._index_in_epoch = batch_size
      assert batch_size <= self._num_examples
    end = self._index_in_epoch
    return self._images[start:end], self._labels[start:end]
def read_data_sets(train_dir, fake_data=False, one_hot=False, dtype=tf.float32):
  class DataSets(object):
    pass
  data_sets = DataSets()
  if fake_data:
    def fake():
      return DataSet([], [], fake_data=True, one_hot=one_hot, dtype=dtype)
    data_sets.train = fake()
    data_sets.validation = fake()
    data_sets.test = fake()
    return data_sets
  TRAIN_IMAGES = 'train-images-idx3-ubyte.gz'
  TRAIN_LABELS = 'train-labels-idx1-ubyte.gz'
  TEST_IMAGES = 't10k-images-idx3-ubyte.gz'
  TEST_LABELS = 't10k-labels-idx1-ubyte.gz'
  VALIDATION_SIZE = 5000
  local_file = maybe_download(TRAIN_IMAGES, train_dir)
  train_images = extract_images(local_file)
  local_file = maybe_download(TRAIN_LABELS, train_dir)
  train_labels = extract_labels(local_file, one_hot=one_hot)
  local_file = maybe_download(TEST_IMAGES, train_dir)
  test_images = extract_images(local_file)
  local_file = maybe_download(TEST_LABELS, train_dir)
  test_labels = extract_labels(local_file, one_hot=one_hot)
  validation_images = train_images[:VALIDATION_SIZE]
  validation_labels = train_labels[:VALIDATION_SIZE]
  train_images = train_images[VALIDATION_SIZE:]
  train_labels = train_labels[VALIDATION_SIZE:]
  data_sets.train = DataSet(train_images, train_labels, dtype=dtype)
  data_sets.validation = DataSet(validation_images, validation_labels,
                                 dtype=dtype)
  data_sets.test = DataSet(test_images, test_labels, dtype=dtype)
  return data_sets

logistic_regression.py

import tensorflow as tf
import input_data
import os
import time, shutil

mnist = input_data.read_data_sets("data/", one_hot=True)

learning_rate = 0.01
training_epochs = 1000
batch_size = 100
display_step = 1

def inference(x):
    init = tf.constant_initializer(value=0) # tf.zero? 
    W = tf.get_variable("W", [784, 10], initializer=init)
    b = tf.get_variable("b", [10], initializer=init)

    # y = Wx + b
    output = tf.nn.softmax(tf.matmul(x, W) + b)

    w_hist = tf.summary.histogram("weights", W)
    b_hist = tf.summary.histogram("biases", b)
    y_hist = tf.summary.histogram("output", output)
    
    return output

def loss(output, y):
    dot_product = y * tf.log(output)

    xentropy = -tf.reduce_sum(dot_product, axis=1)
    loss = tf.reduce_mean(xentropy)

    return loss

def training(cost, global_step):
    tf.summary.scalar("cost", cost)
    optimizer = tf.train.GradientDescentOptimizer(learning_rate)

    train_op = optimizer.minimize(cost, global_step=global_step)
    return train_op

def evaluate(output, y):
    correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    return accuracy

def main():
    if os.path.exists("logistic_logs/"):
        shutil.rmtree("logistic_logs/")

    graph = tf.Graph()
    
    with graph.as_default():
        # mnist have is in 28 * 28
        x = tf.placeholder("float", [None, 784])

        # mnist have 10 answer
        y = tf.placeholder("float", [None, 10])

        output = inference(x)

        cost = loss(output, y)

        global_step = tf.Variable(0, name="global_step", trainable=False)

        train_op = training(cost, global_step)

        eval_op = evaluate(output, y)

        summary_op = tf.summary.merge_all()

        saver = tf.train.Saver()

        session = tf.Session()

        summary_writer = tf.summary.FileWriter('logistic_logs/', graph=session.graph)

        init_op = tf.global_variables_initializer()

        session.run(init_op)

        for epoch in range(training_epochs):
            avg_cost = 0
            total_batch = int(mnist.train.num_examples / batch_size)

            for i in range(total_batch):
                mbatch_x, mbatch_y = mnist.train.next_batch(batch_size)

                feed_dict = { x: mbatch_x, y: mbatch_y }
                session.run(train_op, feed_dict=feed_dict)
                minibatch_cost = session.run(cost, feed_dict=feed_dict)
                
                avg_cost += session.run(cost, feed_dict={x: mbatch_x, y: mbatch_y}) / total_batch

            if epoch % display_step == 0:
                val_feed_dict = {
                    x: mnist.validation.images,
                    y: mnist.validation.labels
                }

                accuracy = session.run(eval_op, feed_dict=val_feed_dict)

                print("validation Error:", (1 - accuracy))
                summary_str = session.run(summary_op, feed_dict=feed_dict)
                summary_writer.add_summary(summary_str, session.run(global_step))

                saver.save(session, "logistic_logs/model_checkpoint", global_step=global_step)
        
        print("Optimization Down")
        test_feed_dict = {
            x: mnist.test.images,
            y: mnist.test.labels
        }

        accuracy = session.run(eval_op, feed_dict=test_feed_dict)

        print("Test Accuracy:", accuracy)
    
if __name__ == "__main__":
    main()