# 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 tensorflow.python.platform

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

import input_data

mnist =  input_data.read_data_sets('MNIST_data/', one_hot=True)

#-*- coding:utf-8 -*-

#The MNIST database of handwritten digits

#Author:Kai Z

import tensorflow as tf

import numpy as np

import input_data

#创建MNIST数据，存储于/MNIST_data目录下

#mnist.train mnist.test

mnist = input_data.read_data_sets('MNIST_data/', one_hot=True)

#神经网络超参数

input_node = 784

output_node = 10

hide_node = 100

batch_size = 100

learning_rate = 1e-3

training_steps = 5000

x = tf.placeholder(tf.float32,[None,input_node])

y = tf.placeholder(tf.float32,[None,output_node])

hidden_weight = tf.Variable(tf.random_normal([input_node,hide_node],stddev = 1,seed = 1))

hidden_bias = tf.Variable(tf.zeros([1,hide_node],tf.float32))

output_weight = tf.Variable(tf.random_normal([hide_node,output_node],stddev = 1,seed = 1))

output_bias = tf.Variable(tf.zeros([1,output_node],tf.float32))

h = tf.nn.tanh(tf.matmul(x,hidden_weight)+hidden_bias)

y_pred = tf.nn.sigmoid(tf.matmul(h,output_weight)+output_bias)

correct_predict = tf.equal(tf.argmax(y_pred,1),tf.argmax(y,1))

accuracy = tf.reduce_mean(tf.cast(correct_predict,tf.float32))

cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y_pred,labels=tf.argmax(y,1))

cross_entropy_mean = tf.reduce_mean(cross_entropy)

train_op = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy_mean)

init_op = tf.global_variables_initializer()

with tf.Session() as sess:

    sess.run(init_op)

    for i in range(training_steps):

        input_batch,output_batch = mnist.train.next_batch(batch_size)

        sess.run(train_op,feed_dict={x:input_batch,y:output_batch})

        if i%100 == 0:

            right_rate = sess.run(accuracy,feed_dict = {x:mnist.validation.images,y:mnist.validation.labels})

            print('训练%d次后，训练正确率为百分之%f'%(i,right_rate*100))

    right_rate = sess.run(accuracy,feed_dict = {x:mnist.test.images,y:mnist.test.labels})

    print('训练%d次后，测试正确率为百分之%f'%(i,right_rate*100))