MNIST手写体识别--tensorflow
对于tensorflow给出的几个版本的手写体识别的代码进行分析。其中tensorflow的mnist代码在https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/tutorials/mnist1：softmax版本
# Copyright 2015 The TensorFlow Authors. 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.
# ==============================================================================
"""A very simple MNIST classifier.
See extensive documentation at
https://www.tensorflow.org/get_started/mnist/beginners
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
FLAGS = None
def main(_):
# Import data
mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
# Create the model
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x, W) + b
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
# The raw formulation of cross-entropy,
#
# tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
# reduction_indices=[1]))
#
# can be numerically unstable.
#
# So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
# outputs of 'y', and then average across the batch.
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# Train
for _ in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
# Test trained model
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images,
y_: mnist.test.labels}))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',
help='Directory for storing input data')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
上面就是完整的代码，下面是分析。
import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)# Create the model x = tf.placeholder(tf.float32, [None, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.matmul(x, W) + b然后定义输入，输出以及参数W，b
可以看到，W*x数组里面每一行其实就是对于每个样本的预测，x1w1的值代表结果是0的概率，当然仅仅进行到这里值不是概率，需要经过处理之后才是概率。 定义好模型之后，需要设置目标函数来进行优化。这里使用的是一种称为交叉熵的量进行优化。交叉熵是信息论里面的知识，主要用于度量两个概率分布间的差异性信息。
cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)定义好目标函数，使用梯度下降方法优化。定义好优化方法之后，只需要不断地优化直到收敛就可以停止迭代。因此，现在就可以运行了。
sess = tf.InteractiveSession() tf.global_variables_initializer().run()定义的变量必须要使用显示的初始化函数。
for _ in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
主要的优化过程在这里，外层循环代表的是梯度下降过程，每一次迭代相当于一步，使用的100个样本点的作用是小批梯度，因为批梯度算法每次更新使用的是全部的样本点，这样导致计算复杂度很高。
所以这里使用的是小样本来进行每次的迭代。需要说明的一点是y_其实就是标签。后面的代码用于评估模型，这里不做论述。还有一点，就是在定义x的时候使用的是[None,784],原因是后面再迭代的时候需要传入x，而使用None的话，传入任何大小都行。
查看原文： http://www.hahaszj.top/uncategorized/mnist%e6%89%8b%e5%86%99%e4%bd%93%e8%af%86%e5%88%ab-tensorflow/186