import tensorflow as tf
import numpy
import matplotlib.pyplot as plt
rng = numpy.random
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/usr/local/lib/python3.6/site-packages/tensorflow/examples/tutorials/mnist",one_hot=True)
#Parameters
learning_rate = 0.01
batch_size = 100
training_epochs = 25
display_step = 1
#Training Data
#tf Graph Input
X = tf.placeholder(tf.float32,[None,784])
#mnist data image if shape 28*28 = 784
y = tf.placeholder(tf.float32,[None,10])
#0-9 digits recognition => 10 classes
#Create Model
#Set model weights
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
#construct a model
pred = tf.nn.softmax(tf.matmul(X,W)+b)
#Minimize error using cross entropy
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred),reduction_indices=1)) #L2 loss
#Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
#Initialize the variables
init = tf.initialize_all_variables()
#Launch the graph
with tf.Session() as sess:
    sess.run(init)
#Training cycle
    for epoch in range(training_epochs):
        avg_cost = 0
        total_batch = int(mnist.train.num_examples/batch_size)
        #Loop over all batches
        for batch_idx in range(total_batch):
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            #Run optimization op (backprop) and cost op (to get loss value)
            _, c = sess.run([optimizer, cost], feed_dict={X:batch_xs, y:batch_ys})
#Compute average loss
            avg_cost += c/total_batch
        #Display logs per epoch step
        if (epoch+1) % display_step == 0:
            print("Epoch:",'%04d' %(epoch+1),"cost=",\
                  "{:.9f}".format(avg_cost))
print("Optimization Finished!")
    #Test model
    correct_prediction = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
    #Calculate accuracy
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    print("Accuracy",accuracy.eval({X:mnist.test.images,y:mnist.test.labels}))
#result:
#https://zhuanlan.zhihu.com/p/22410917

import tensorflow as tf
W=tf.Variable(tf.zeros([2,1]),name="weights")
b=tf.Variable(0.,name="bias")
def inference(X):
    return tf.matmul(X,W)+b
def loss(X,Y):
    Y_predicted=inference(X)
    return tf.reduce_sum(tf.squared_difference(Y,Y_predicted))
def inputs():
    weights_age=[[84,46],[73,20],[65,52],[70,30]]
    blood_fat_content=[354,190,405,263]
    return tf.to_float(weights_age),tf.to_float(blood_fat_content)
def train(total_loss):
    learning_rate=0.000001;
    return tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss)
def evaluate(sess,X,Y):
    print(sess.run(inference([[80.,25.]])))
with tf.Session() as sess:
    tf.global_variables_initializer().run()
    X,Y=inputs()
    total_loss=loss(X,Y)
    train_op=train(total_loss)
    coord=tf.train.Coordinator()
    threads=tf.train.start_queue_runners(sess=sess,coord=coord)
    training_iterations=1000
    for iter in range(training_iterations):
        sess.run([train_op])
        if iter%100 == 0:
            print("loss:",sess.run([total_loss]))
    evaluate(sess,X,Y)
    coord.request_stop()
    coord.join(threads)
    sess.close()



----------------------------
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets("/usr/local/lib/python3.6/site-packages/tensorflow/examples/tutorials/mnist",one_hot=True)
#不用网络下载(因为被墙了下载会失败),而是将下载好的数据放到对应文件夹。
sess=tf.InteractiveSession()
X=tf.placeholder(tf.float32,[None,784])
W=tf.Variable(tf.zeros([784,10]))
b=tf.Variable(tf.zeros([10]))
pred=tf.nn.softmax(tf.matmul(X,W)+b)
target=tf.placeholder(tf.float32,[None,10])
cross_entropy=tf.reduce_mean(-tf.reduce_sum(target*tf.log(pred),reduction_indices=[1]))

train_step=tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

tf.global_variables_initializer().run()
for i in range(1000):
    batch_xs,batch_ys=mnist.train.next_batch(100)
    train_step.run({X:batch_xs,target:batch_ys})

correct_prediction=tf.equal(tf.argmax(pred,1),tf.argmax(target,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
print(accuracy.eval({X:mnist.test.images,target:mnist.test.labels}))


-----------------------
import tensorflow as tf
from numpy.random import RandomState
batch_size = 8
x = tf.placeholder(tf.float32, shape=(None, 2), name="x-input")
y_label = tf.placeholder(tf.float32, shape=(None, 1), name='y-input')
w1= tf.Variable(tf.random_normal([2, 1], stddev=1, seed=1))
y = tf.matmul(x, w1)
#定义损失函数使得预测少了的损失大，于是模型应该偏向多的方向预测。
#换句话说,对于y>y_label的情况，惩罚较小,所以更倾向于y>y_label
loss_less = 10
loss_more = 1
loss = tf.reduce_sum(tf.where(tf.greater(y, y_label), (y - y_label) * loss_more, (y_label - y) * loss_less))
#如果y大,(y - y_label) * loss_more
#如果y_label大,(y_label - y) * loss_less

train_step = tf.train.AdamOptimizer(0.001).minimize(loss)
rdm = RandomState(1)
X = rdm.rand(128,2)
Y = [[x1+x2+(rdm.rand()/10.0-0.05)] for (x1, x2) in X]
with tf.Session() as sess:
    tf.global_variables_initializer().run()
    STEPS = 5000
    for i in range(STEPS):
        start = (i*batch_size) % 128
        end = (i*batch_size) % 128 + batch_size
        sess.run(train_step, feed_dict={x: X[start:end], y_label: Y[start:end]})
        if i % 1000 == 0:
            print("After %d training step(s), w1 is: " % (i))
            print(sess.run(w1), "\n")
    print("Final w1 is: \n", sess.run(w1))

-------------------
#保存model
import tensorflow as tf
v1=tf.Variable(tf.constant(1.0,shape=[1]),name="v1")
v2=tf.Variable(tf.constant(1.0,shape=[1]),name="v2")
result=v1+v2
init_op=tf.global_variables_initializer()
saver=tf.train.Saver()
with tf.Session()  as sess:
    sess.run(init_op)
    saver.save(sess,"model.ckpt")

------------
import tensorflow as tf
W=tf.Variable(tf.zeros([2,1]),name="weights")

print("-")
print(W)
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    W_val = sess.run(W)
    print("--")
    print(W_val)
    print("---")
    print(W)

"""
-
<tf.Variable 'weights:0' shape=(2, 1) dtype=float32_ref>
--
[[ 0.]
 [ 0.]]
---
<tf.Variable 'weights:0' shape=(2, 1) dtype=float32_ref>
"""

TensorFlow print variable 的值 是这样的： 
import tensorflow as tf 
W=tf.Variable(tf.zeros([2,1]),name="weights")
print("-") 
print(W) 
with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer()) 
    W_val = sess.run(W) 
    print("--") 
    print(W_val) 
    print("---") 
    print(W)