實例描述：
從MNIST數據集中選擇一副圖，這幅圖上有一個手寫的數字，讓機器模擬人眼來區分這個手寫的數字到底是幾。
實現步驟：
（1）導入MNIST數據集；
（2）分析MNIST樣本特點定義變量；
（3）構建模型；
（4）訓練模型並輸出中間狀態參數；
（5）測試模型；
（6）保存模型；
（7）讀取模型；
使用工具：
操作系統win7， Spyder(Anaconda3),
準備工作：
若想實現實例功能，必須先導入MNIST數據集。MNIST數據集是一個入門級的計算機視覺數據集。其基礎程度，相當於學編程時第一件事往往是學習打印Hello World。數據集裏包含各種手寫數字圖片，可通過編寫以下代碼，自動下載數據集並解壓到自定義的目錄下。
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("D:/Anaconda3Project/MNIST_data/", one_hot=True)
# 路徑爲自定義，可根據實際情況選擇數據集存放路徑
# one_hot=True,表示將樣本標籤轉化爲one_hot編碼。例如：一共10類，0的one_hot爲1000000000，1的one_hot爲0100000000.......以此類推，只有一個位爲1，1所在的位置就代表着第幾類。
但在實際操作中，自動下載並不能成功，會出現下面的錯誤：
所以，採用第二種方法，手動導入。在MNIST數據集官網[http://yann.lecun.com/exdb/mnist/]手動下載數據集。如圖：
左下角四行紅色鏈接依次下載即可，切記，不要解壓！不要解壓！不要解壓！
將四個壓縮包放在和你代碼同級的目錄下，比如：我把“MNIST數據集測試.py”文件存放在“D:/Anaconda3Project/”下，那麼將四個壓縮包也放在“D:/Anaconda3Project/”下，如圖：
這還沒完，還需要將如下代碼命名爲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 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
if __name__ == '__main__':
path = "D:/Anaconda3Project/MNIST_data/"
read_data_sets(path)
這串代碼也是網上找的，只需要將倒數第二行的path路勁改爲自己想要解壓到的位置即可，我把它設置成和自動下載時自定義路徑相同。然後將這串命名爲input_data.py文件存放在自己安裝的Anaconda程序文件的Lib文件下，如圖：
此時，爲防止導入失敗，可以將input_data.py放在和自己編寫的.py文件同級目錄下，如圖：
現在，應該就不會出現導入錯誤了，可以進行導入並驗證，代碼如下：
import input_data # 此時的導入只需要這麼寫就可以
mnist = input_data.read_data_sets("D:/Anaconda3Project/MNIST_data/", one_hot=True)
print('輸入數據：',mnist.train.images) # 輸出圖片數據
print('訓練數據集shape：',mnist.train.images.shape) # 輸出訓練數據集裏圖片個數
import pylab
im = mnist.train.images[1] # 導出數據集中第一幅圖片
im = im.reshape(-1,28) # 將圖片的shape變爲計算機計算行數，自己定義列數爲28列
pylab.imshow(im)
pylab.show()
print('測試數據集shape：',mnist.test.images.shape) # 輸出測試數據集裏圖片個數
print('驗證數據集shape：',mnist.validation.images.shape) # 輸出驗證數據集裏圖片個數
結果爲下圖：
圖一：
圖二：
從圖一可以看到四個壓縮包被依次提取，而且可以看到訓練集打印出來的信息爲一個55000行，784列的矩陣。即，訓練集中有55000張圖片。並且測試數據集裏有10000條樣本圖片，驗證數據集裏有5000個圖片。圖二展示了訓練集中的第一幅圖片。全部顯示，說明導入成功。
路漫漫其修遠兮，吾將上下而求索。
這只是MNIST數據集訓練的第一步，深度學習纔剛剛開始！