TensorFlow官方文档入门笔记[一]

Posted 2020-10-07

TensorFlow官方文档入门笔记[一]

张量

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]

 

 

计算图

TF由两个部分组成：构建计算图与运行计算图。

node1 = tf.constant(3.0, dtype=tf.float32) node2 = tf.constant(4.0) # also tf.float32 implicitly print(node1, node2) 输出： Tensor("Const:0", shape=(), dtype=float32) Tensor("Const_1:0", shape=(), dtype=float32)

必须创建一个Session对象，然后调用其run方法运行计算图。会话封装了TensorFlow运行时的控制和状态。

sess = tf.Session() print(sess.run([node1, node2])) 输出： [3.0, 4.0]

 

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

 

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

 

add_and_triple = adder_node * 3. print(sess.run(add_and_triple, {a: 3, b: 4.5})) 输出 22.5

 

变量允许我们向图中添加可训练的参数。它们的构造类型和初始值：

W = tf.Variable([.3], dtype=tf.float32) b = tf.Variable([-.3], dtype=tf.float32) x = tf.placeholder(tf.float32) linear_model = W * x + b

常数在调用时被初始化tf.constant，其值永远不会改变。相比之下，调用时，变量不会被初始化tf.Variable。要初始化TensorFlow程序中的所有变量：

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

x是占位符评估linear_model几个值

print(sess.run(linear_model, {x: [1, 2, 3, 4]})) 输出 [ 0.          0.30000001  0.60000002  0.90000004]

评估训练模型创建损失函数

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

tf.assign参数值

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

 

tf.train API

TensorFlow提供了优化器，可以缓慢地更改每个变量，以便最大限度地减少损失函数。最简单的优化器是梯度下降。它根据相对于该变量的损失导数的大小修改每个变量tf.gradients

例如：

optimizer = tf.train.GradientDescentOptimizer(0.01) train = optimizer.minimize(loss) sess.run(init) # reset values to incorrect defaults. for i in range(1000):   sess.run(train, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]}) print(sess.run([W, b]))    输出 [array([-0.9999969], dtype=float32), array([ 0.99999082], dtype=float32)]

 

完成程序

完成的可训练线性回归模型如下：

import tensorflow as tf # Model parameters W = tf.Variable([.3], dtype=tf.float32) b = tf.Variable([-.3], dtype=tf.float32) # Model input and output x = tf.placeholder(tf.float32) linear_model = W * x + b y = tf.placeholder(tf.float32) # loss loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares # optimizer optimizer = tf.train.GradientDescentOptimizer(0.01) train = optimizer.minimize(loss) # training data x_train = [1, 2, 3, 4] y_train = [0, -1, -2, -3] # training loop init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) # reset values to wrong for i in range(1000): sess.run(train, {x: x_train, y: y_train}) # evaluate training accuracy curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train}) print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss)) 输出 W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11