待更新

第一个例子

import tensorflow as tf
import numpy as np

# create data
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data*0.1 + 0.3

Weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
biases = tf.Variable(tf.zeros([1]))

y = Weights*x_data + biases
loss = tf.reduce_mean(tf.square(y-y_data))

optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

# init = tf.initialize_all_variables() # tf 马上就要废弃这种写法
init = tf.global_variables_initializer()  # 替换成这样就好

sess = tf.Session()
sess.run(init)          # Very important

for step in range(201):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(Weights), sess.run(biases))

Session 控制

import tensorflow as tf

matrix1 = tf.constant([[3, 3]])
matrix2 = tf.constant([[2],
                       [2]])
product = tf.matmul(matrix1,matrix2)  # matrix multipy np.dot(m1,m2)

#mehtod1
# sess = tf.Session()
# result = sess.run(product)
# print(result)
# sess.close()

# [[12]]

#method 2
with tf.Session() as sess:
    result2 = sess.run(product)
    print(result2)

# [[12]]

变量

import numpy as np
import tensorflow as tf
state = tf.Variable(0,name='counter')
one = tf.constant(1)

new_value = tf.add(state,one)
update = tf.assign(state,new_value)

init = tf.global_variables_initializer() # must have if define variable

with tf.Session() as sess:
    sess.run(init)
    for _ in range(3):
        sess.run(update)
        print(sess.run(state))