今天開始進(jìn)行第一次MNIST的入門調(diào)試。 教程是按照Tensorflow中文社區(qū)的MNIST入門教程來進(jìn)行的珍昨。?
本文包含兩點(diǎn)：MNIST數(shù)據(jù)集的下載與導(dǎo)入县耽；MNIST手寫數(shù)字的識(shí)別測(cè)試
1. MNIST數(shù)據(jù)集的下載與導(dǎo)入
由于某些不可名說的原因，教程中的MNIST數(shù)據(jù)集無法下載打開導(dǎo)致一直出錯(cuò)镣典，現(xiàn)在百度網(wǎng)盤放出下載資源：
百度網(wǎng)盤：鏈接: https://pan.baidu.com/s/1boOSDYJ 密碼: 58kw
有需要的同學(xué)可以下載使用兔毙。
提取和導(dǎo)入MNIST的代碼如下：
# 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
存為“input_data.py”文件即可。至此兄春，MNIST文件的獲取和導(dǎo)入即已完成澎剥。
2. MNIST手寫識(shí)別測(cè)試
整個(gè)導(dǎo)入，訓(xùn)練赶舆，驗(yàn)證和測(cè)試的代碼如下哑姚，詳細(xì)解釋可以在上述教程中得到。
import tensorflowas tf
import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder("float", [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W)+b)
y_ = tf.placeholder("float", [None,10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for iin range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})
執(zhí)行代碼后可得：
訓(xùn)練結(jié)果為0.9137芜茵，符合教程的結(jié)果叙量，測(cè)試成功。
