The central unit of data in TensorFlow is the tensor.
A tensor consists of a set of primitive values shaped into an array of any number of dimensions.
A tensor's rank is its number of dimensions.
Examples：
Tutorial
导入Tensorflow
import tensorflow as tf
Tensorflow程序包括两部分:
Building the computational graph
Running the computational graph
A computational graph is a series of TensorFlow operations arranged into a graph of nodes.
Example:
3 # a rank 0 tensor; this is a scalar with shape []
[1. ,2., 3.] # a rank 1 tensor; this is a vector with shape [3]
[[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3]
[[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3]
node1 = tf.constant(3.0, dtype=tf.float32)
node2 = tf.constant(4.0) # also tf.float32 implicitly
print(node1, node2)
# 输出
(tensorflow) bovenson@ThinkCentre:~/Git/Tensorflow/tutorial$ python3 t1.py
Tensor("Const:0", shape=(), dtype=float32) Tensor("Const_1:0", shape=(), dtype=float32)
# 创建Session并调用其run函数计算node1和node2的值
sess = tf.Session()
print(sess.run([node1, node2]))
# 输出
[3.0, 4.0]
# 把Tensor和运算结合，来完成更复杂的计算操作：
node3 = tf.add(node1, node2)
print("node3:", node3)
print("sess.run(node3):", sess.run(node3))
# 输出
node3: Tensor("Add:0", shape=(), dtype=float32)
sess.run(node3): 7.0
# 一个图可以被参数化以接受外部输入，被称作占位符。一个占位符用来声明稍后会被赋值。
a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
adder_node = a + b # + provides a shortcut for tf.add(a, b)
# 使用
print(sess.run(adder_node, {a: 3, b:4.5}))
print(sess.run(adder_node, {a: [1,3], b: [2, 4]}))
# 输出
7.5
[ 3. 7.]
# 还可以更复杂
add_and_triple = adder_node * 3.
print(sess.run(add_and_triple, {a: 3, b:4.5}))
# 输出
22.5
# 向图添加可训练的参数
W = tf.Variable([.3], dtype=tf.float32)
b = tf.Variable([-.3], dtype=tf.float32)
x = tf.placeholder(tf.float32)
linear_model = W * x + b
# 常量在调用tf.constant时被初始化,且不可改变. 相反, 变量在调用tf.Variable时不会被初始化, 必须显式地调用特定操作
init = tf.global_variables_initializer()
sess.run(init)
# It is important to realize init is a handle to the TensorFlow sub-graph that initializes all the global variables. Until we call sess.run, the variables are uninitialized.
# Since x is a placeholder, we can evaluate linear_model for several values of x simultaneously as follows:
print(sess.run(linear_model, {x:[1,2,3,4]}))
# 输出
[ 0. 0.30000001 0.60000002 0.90000004]
# We've created a model, but we don't know how good it is yet. To evaluate the model on training data, we need a y placeholder to provide the desired values, and we need to write a loss function.
# A loss function measures how far apart the current model is from the provided data. We'll use a standard loss model for linear regression, which sums the squares of the deltas between the current model and the provided data. linear_model - y creates a vector where each element is the corresponding example's error delta. We call tf.square to square that error. Then, we sum all the squared errors to create a single scalar that abstracts the error of all examples using tf.reduce_sum:
y = tf.placeholder(tf.float32)
squared_deltas = tf.square(linear_model - y)
loss = tf.reduce_sum(squared_deltas)
print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))
# 输出
23.66
# We could improve this manually by reassigning the values of W and b to the perfect values of -1 and 1. A variable is initialized to the value provided to tf.Variable but can be changed using operations like tf.assign. For example, W=-1 and b=1 are the optimal parameters for our model. We can change W and b accordingly:
fixW = tf.assign(W, [-1.])
fixb = tf.assign(b, [1.])
sess.run([fixW, fixb])
print(sess.run(loss, {x:[1,2,3,4], y:[0,-1,-2,-3]}))
# 输出
0.0
# We guessed the "perfect" values of W and b, but the whole point of machine learning is to find the correct model parameters automatically. We will show how to accomplish this in the next section.