Tensorflow入门笔记
Tensorflow原理
背景： Python和C++来回切换会造成巨大开销。
To do efficient numerical computing in Python, we typically use libraries like NumPythat do expensive operations such as matrix multiplication outside Python, using highly efficient code implemented in another language. Unfortunately, there can still be a lot of overhead from switching back to Python every operation. This overhead is especially bad if you want to run computations on GPUs or in a distributed manner, where there can be a high cost to transferring data.
解决方案： 利用Python基于Graph定义所有运算，然后让这些一次性在Python外完成这些运算。
TensorFlow also does its heavy lifting outside Python, but it tahttps://www.tensorflow.org/get_started/mnist/proskes things a step further to avoid this overhead. Instead of running a single expensive operation independently from Python, TensorFlow lets us describe a graph of interacting operations that run entirely outside Python. This approach is similar to that used in Theano or Torch.
The role of the Python code is therefore to build this external computation graph, and to dictate which parts of the computation graph should be run. See the Computation Graph section of Getting Started With TensorFlow for more detail.
Tensorflow常用API说明
Session
TensorFlow relies on a highly efficient C++ backend to do its computation. The connection to this backend is called a session.The common usage for TensorFlow programs is to first create a graph and then launch it in a session.
总结： 用于和C++高性能计算模块会话的类
在入门教程中，我们使用
import tensorflow as tf
sess = tf.InteractiveSession()
Tensor
中文名称张量，可以查看知乎上关于这个问题的解释：什么是张量。实际上可以将其理解为一个矩阵，Tensorflow中的基本单位
查看以下代码：
import tensorflow as tf
# What is Tensor?
ta = [0,0,0,0];
ta[0] = tf.placeholder(tf.float32,[None,784])
ta[1] = tf.zeros([5,5],tf.float32)
print (ta)
输出以下结果：
/usr/bin/python2.7 /home/maoyiwei/桌面/Tensorflow/playground/play.py
[<tf.Tensor 'Placeholder:0' shape=(?, 784) dtype=float32>, <tf.Tensor 'zeros:0' shape=(5, 5) dtype=float32>, 0, 0]
Placeholder
可以理解为用于存储输入数据（训练数据）的Tensor。格式如下：
placeholder( dtype, shape=None, name=None)
x = tf.placeholder(tf.float32, shape=(1024, 1024))
Variables
字面意思。在Tensorflow中意义如下：
A
Variableis a value that lives in TensorFlow's computation graph. It can be used and even modified by the computation. In machine learning applications, one generally has the model parameters beVariables.
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
Variable要进行初始化，步骤如下：
Before
Variables can be used within a session, they must be initialized using that session. This step takes the initial values (in this case tensors full of zeros) that have already been specified, and assigns them to eachVariable. This can be done for allVariablesat once:
sess.run(tf.global_variables_initializer())
tf.matmul(x,W)
矩阵相乘(x*W)：详细看文档：
Matmul(a,b) Return:
A Tensor of the same type as a and b where each inner-most matrix is the product of the corresponding matrices in a and b, e.g. if all transpose or adjoint attributes are False:
output[..., i, j] = sum_k (a[..., i, k] * b[..., k, j]), for all indices i, j.
tf.reduce_XXX
查看文档，解释如下：
Computes the XXXX of elements across dimensions of a tensor.
主要参数如下：
reduce_mean(
input_tensor, # 输入的tensor
axis=None, # 维度
# keep_dims=False,
# name=None,
# reduction_indices=None
)
- input_tensor: The tensor to reduce. Should have numeric type.
- axis: The dimensions to reduce. If
None(the default), reduces all dimensions.
举例说明：
# 'x' is [[1., 2.]
# [3., 4.]]
tf.reduce_mean(x) ==> 2.5 #如果不指定第二个参数，那么就在所有的元素中取平均值
tf.reduce_mean(x, 0) ==> [2., 3.] #指定第二个参数为0，则第一维的元素取平均值，即每一列求平均值
tf.reduce_mean(x, 1) ==> [1.5, 3.5] #指定第二个参数为1，则第二维的元素取平均值，即每一行求平均值
常用的API如下：
- reduce_mean 平均值
- reduce_max 最大值
- reduce_min 最小值
- reduce_sum 求和
为什么要命名Reduce呢？ Stackoverflow上对这个问题的解释为：
Reduce is just a name for a family of operations which are used to create a single object from the sequence of objects, repeatedly applying the same binary operation.
tf.nn
一些激活函数、卷积函数等，源代码中注释如下：
"""## Activation Functions
The activation ops provide different types of nonlinearities for use in neural
networks. These include smooth nonlinearities (`sigmoid`, `tanh`, `elu`,
`softplus`, and `softsign`), continuous but not everywhere differentiable
functions (`relu`, `relu6`, and `relu_x`), and random regularization
(`dropout`).
tf.train
训练方法（训练损失函数）。直接上代码理解会更好一点。
# define a math model
print('make model')
# 占位符（你的数据）
x = tf.placeholder(tf.float32,[None,784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
# train it
print('train it')
# 占位符（预测数据）
y_ = tf.placeholder(tf.float32,[None,10])
# 计算交叉熵
cross_entropy = tf.reduce_mean(-tf.reduce_sum( y_*tf.log(y),reduction_indices=[1]))
# 使用梯度下降法
train_step = tf.train.GradientDescentOptimizer(0.55).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
for _ in range(1000):
# print('抓取100个随机数据训练')
batch_xs, batch_ys = mnist.train.next_batch(100)
# print(x,y)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
这个feed_dict和placeholder相互对应。
记住这两句话：
train_step = tf.train.GradientDescentOptimizer(0.55).minimize(cross_entropy)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
额外说明一下sess.run。可以传入tf.train或者tensor，如下面评价模型就是输入tensor的例子，此时sess.run返回tensor的计算结果。
# Evaluating our Model
print('start to evaluate')
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
其中tf.cast用于数据转换。
