import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

import matplotlib.pyplot as plt

import numpy as np

%matplotlib inline

mnist = input_data.read_data_sets('/home/ubuntu-mm/TensorFlow/Learning/MNIST_data', one_hot=True) #下载MINIST数据集

#表示输入任意数量的MNIST图像，每一张图展平成784维的向量

#placeholder是占位符，在训练时指定

x = tf.placeholder(tf.float32, [None, 784])

#初始化W，b矩阵

W = tf.Variable(tf.zeros([784,10]))

b = tf.Variable(tf.zeros([10]))

#tf.matmul(X，W)表示x乘以W

y = tf.nn.softmax(tf.matmul(x, W) + b)

#为了计算交叉熵，我们首先需要添加一个新的占位符用于输入正确值

y_ = tf.placeholder("float", [None,10])

#交叉熵损失函数

cross_entropy = -tf.reduce_sum(y_*tf.log(y))

#模型的训练，不断的降低成本函数

#要求TensorFlow用梯度下降算法（gradient descent algorithm）以0.01的学习速率最小化交叉熵

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

#在运行计算之前，需要添加一个操作来初始化我们创建的变量

init = tf.global_variables_initializer()

#在Session里面启动我模型，并且初始化变量

with tf.Session() as sess:

# sess = tf.Session()

# sess.run(init)

    sess.run(init)

    #开始训练模型,循环训练1000次

    for i in range(50):

        #随机抓取训练数据中的100个批处理数据点

      batch_xs, batch_ys = mnist.train.next_batch(100)

      #然后我们用这些数据点作为参数替换之前的占位符来运行train_step

      sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

    #检验真实标签与预测标签是否一致

    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))

    #计算精确度，将true和false转化成相应的浮点数，求和取平均

    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

    #计算所学习到的模型在测试数据集上面的正确率

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

    print 'W is:',W.eval()[10]

    print 'b is:',b.eval()

    batch_xs, batch_ys = mnist.train.next_batch(100)

#     print 'batch_xs[1]=',batch_xs[1]

    print 'batch_ys[1]=',batch_ys[1]

    x_in = tf.reshape(batch_xs[1],[1,784])

    Y_Predict = tf.nn.softmax(tf.matmul(x_in, W) + b)

    print 'Y_Predict is :',Y_Predict.eval()

Ori_Pic = np.zeros([28,28])

for m in range(784):

    i = m%28

    j = (m-i)/28

    Ori_Pic[j][i] = batch_xs[1][m]

plt.figure(1)

plt.imshow(Ori_Pic)