MNIST 示例程序代码

根据MNIST 初学者指导手册 https://www.tensorflow.org/get_started/mnist/beginners ，把代码拼接完成，如下：

Python 2.7.10 (default, Jul 15 2017, 17:16:57) 
[GCC 4.2.1 Compatible Apple LLVM 9.0.0 (clang-900.0.31)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> mnist = input_data.read_data_sets(".", one_hot=True)
Extracting ./train-images-idx3-ubyte.gz
Extracting ./train-labels-idx1-ubyte.gz
Extracting ./t10k-images-idx3-ubyte.gz
Extracting ./t10k-labels-idx1-ubyte.gz
>>> x = tf.placeholder(tf.float32, [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(tf.float32, [None, 10])
>>> cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
>>> train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
>>> sess = tf.InteractiveSession()
2017-11-16 14:04:42.216851: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
>>> tf.global_variables_initializer().run()
>>> 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})
... 

>>> 
>>> 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}))
0.9193
>>> 

Tensorflow 深度学习，图形验证码识别成功率91.93%，继续研究。