import tensorflow as tf

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.reducen_sum(y_ * tf.log(y))

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

init = tf.initialize_all_variables()
with tf.Session() as sess:
  sess.run(init)

for i in xrange(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.agrmax(y_, 1))

accuracy = tf.reduce_mean(tf.cast(correc_prediction, tf.float32))
print (sess.run(accuracy, feed_dict = {x: mnist.test.images, y_: minst.test.labels}))

from tensorflow import Session, device, constant, matmul

'''构建一个只有两个constant做输入, 然后进行矩阵乘的简单图:'''

#如果不使用with session()语句, 需要手动执行session.close().
#with device设备指定了执行计算的设备:
#  "/cpu:0": 机器的 CPU.
#  "/gpu:0": 机器的第一个 GPU, 如果有的话.
#  "/gpu:1": 机器的第二个 GPU, 以此类推.

with Session() as session: # 创建执行图的上下文
  with device('/cpu:0'): # 指定运算设备
    mat1 = constant([[3, 3]]) # 创建源节点
    mat2 = constant([[2], [2]])
    product = matmul(mat1, mat2) # 指定节点的前置节点, 创建图
    result = session.run(product) # 执行计算
    print(result)