Tensor

Knowing TensorFlow Core principles will give you a great mental model of how things are working internally when you use the more compact higher level API.
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tensor是tensorflow的核心数据单元

tensor的rank是其维度

3 # a rank 0 tensor; a scalar with shape [][1., 2., 3.] # a rank 1 tensor; 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]

The Computational Graph

可以把tensorflow的运作理解为两部分：

• Building the computational graph.
• Running the computational graph.

computational graph是一系列operation产生的图，每个节点得到n个输入并产生输出

import tensorflow as tf

node1 = tf.constant(3.0, dtype=tf.float32)node2 = tf.constant(4.0)print node1, node2

Tensor("Const:0", shape=(), dtype=float32) Tensor("Const_1:0", shape=(), dtype=float32)

• 这时候并不会直接把值输出，而是输出在evaluate的时候会产生3.0, 4.0的数值的nodes(节点)信息
• 为了直接evaluate这些节点，需要run整个session

with tf.Session() as sess:    print sess.run([node1, node2])

[3.0, 4.0]

from __future__ import print_function

node3 = tf.add(node1, node2)print("node3:", node3)with tf.Session() as sess:    print("sess.run(node3):", sess.run(node3))

node3: Tensor("Add:0", shape=(), dtype=float32)sess.run(node3): 7.0

一个图可以被参数化来accept external input

a = tf.placeholder(tf.float32)b = tf.placeholder(tf.float32)adder_node = a + b   # + provides a shortcut for tf.add(a, b)

with tf.Session() as sess:    print(sess.run(adder_node, {a : 3, b : 4.5}))    print(sess.run(adder_node, {a : [1, 2, 3], b : [2, 3, 4]}))

7.5[ 3.  5.  7.]

mul_node = adder_node * 3.with tf.Session() as sess:    print(sess.run(mul_node, {a : 3, b : 4.5}))

22.5

使用Variables来允许任意的输入，添加trainable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

Variables的初始化不像constant，需要如下初始化：

init = tf.global_variables_initializer()sess = tf.Session()sess.run(init)

print(sess.run(linear_model, {x : [1, 2, 3, 4]}))

[ 0.          0.30000001  0.60000002  0.90000004]

ok, 这时候我们创建了一个模型，是时候来评估这个模型了
* 真实值
* loss function

y = tf.placeholder(tf.float32)diff = linear_model - ysquared_deltas = tf.square(diff)loss = tf.reduce_sum(squared_deltas)   #sumprint(sess.run(diff, {x:[1,2,3,4], y:[2,3,4,5]}))print(sess.run(squared_deltas, {x:[1,2,3,4], y:[2,3,4,5]}))print(sess.run(loss, {x:[1,2,3,4], y:[2,3,4,5]}))

[-2.         -2.70000005 -3.4000001  -4.0999999 ][  4.           7.29000044  11.56000042  16.80999947]39.66

我们可以修改变量的值，通过tf.assign

fixW = tf.assign(W, [-1.])fixb = tf.assign(b, [1.])print(sess.run([fixW, fixb]))print(sess.run(linear_model, {x : [1, 2, 3, 4]}))print(sess.run(diff, {x:[1,2,3,4], y:[2,3,4,5]}))print(sess.run(squared_deltas, {x:[1,2,3,4], y:[2,3,4,5]}))print(sess.run(loss, {x:[1,2,3,4], y:[2,3,4,5]}))

[array([-1.], dtype=float32), array([ 1.], dtype=float32)][ 0. -1. -2. -3.][-2. -4. -6. -8.][  4.  16.  36.  64.]120.0

tf.train

tf提供梯度api来简化运算

TensorFlow can automatically produce derivatives given only a description of the model using the function tf.gradients

optimizer = tf.train.GradientDescentOptimizer(0.01)train = optimizer.minimize(loss)

sess.run(init)

for i in range(1000):    sess.run(train, {x:[1,2,3,4], y:[2,3,4,5]})

print(sess.run([W, b]))

[array([ 1.00000191], dtype=float32), array([ 0.9999944], dtype=float32)]

print(sess.run(loss, {x:[1,2,3,4], y:[2,3,4,5]}))

2.09326e-11

print(sess.run(linear_model, {x:[1,2,3,4], y:[2,3,4,5]}))

[ 1.9999963   2.99999809  4.          5.00000191]