TensorFlow与Python代码学习（1）：Tutorial与Tensor的基本操作

安装TensorFlow的话，这里就不多说了，参照官网即可，其中各有曲折，自己搜索查阅就好。

接着就是跟着官方的tutorial入门了https://tensorflow.google.cn/get_started/get_started 这里的网站结尾后缀是cn的，如果是com的，那个注意需要翻墙。

先跑tutorial，我这里实战了四个，到TensorFlow Mechanics 101。然后有些不懂的可以查阅网站和书籍，我的参考书，手边买了本周志华的《机器学习》和《面向机器智能的TensorFlow实践》，一本理论，一本实战，都很好读，对回归、分类、前馈神经网络、sigmod、softmax等术语、发展历史也有个大概了解。

官网一上来就是Computation Graph然后就是线性回归中的参数拟合，求解y = wx + b。这里我结合自己的操作和参看的书籍有些学习记录和整理。

启动时需要source ～/tensorflow/bin/activate，添加一个别名alias就行，在你的.bashrc文件加入一行：alias tensorflow='source /home/yake/tensorflow/bin/activate'

关闭的时候需要用指令deactive

查看TensorFlow的版本

import tensorflow as tf

print(tf.__version__)

它的那个Computation Graph很好看，是怎么保存的

1. Computation Graph的保存

import tensorflow as tf 

a = tf.constant(5, name = "input_a")
b = tf.constant(3, name = "input_b")
c = tf.multiply(a,b, name = "mul_c")
d = tf.add(a,b, name = "add_d")
e = tf.add(c,d, name = "add_e")

sess = tf.Session()
output = sess.run(e)
writer = tf.summary.FileWriter('./2tfbasic_save', sess.graph)
writer.close()
sess.close()

运行之后可以在本地目录下找到一个名为2tfbasic_save的文件夹，里面就可以调用TensorBoard可视化工具看到我们的的Computation Graph
1.开启TensorFlow环境，source ～/tensorflow/bin/activate或者命令别名tensorflow

2. 输入tensorboard --logdir="2tfbasic_save"

2. Tensor的基本操作

接触到的时候一堆术语，Constant，Variable，PlaceHolder，不过好在某些网友和书籍讲的还算可以。我这里直接贴上我的jupyter notebook的结果（这里是将ipynb导出为Python源码然后插入到博客里）

# coding: utf-8

# In[1]:


# https://www.tensorflow.org/programmers_guide/tensors
# P59 shape, value, dtype, cast
# numpy array
import tensorflow as tf 
import numpy as np

a = tf.constant(4.0)
print(a)
print(" a \n{}".format(a))

sess = tf.InteractiveSession()
print("a value:\n{}\n".format(a.eval()))

a = tf.zeros(2)
print("a value before expand:\n{}".format(a.eval()))
print(a.shape)

a = tf.expand_dims(a, 1)
print("a value after expand:\n{}".format(a.eval()))
print(a.shape)


# caution with the double ()
a = tf.zeros((2, 2))
print("\na zeros 2x2 \n{}".format(a))
print("a value:\n{}".format(a.eval()))

sess.close()


# In[2]:


b = tf.constant([[1,2,3],
                 [4,5,6]])
print(b)
print("b \n{}".format(b))

print(b.shape)
print("\nb shape: \n{}".format(b.get_shape()))
print(b.get_shape())

print(b.dtype)
b = tf.cast(b, tf.float32)
print(b.dtype)

# np.array
c = tf.constant(np.array([
                            [[1,2,3],
                             [4,5,6]],
                            [[1,1,1],
                             [2,2,2]]
                        ]))
print("\nc numpy array shape: {}".format(c.get_shape()))
sess = tf.InteractiveSession()
print("c numpy array value:\n{}".format(c.eval()))

d = tf.constant([
                    [[1,2,3],
                     [4,5,6]],
                    [[7,8,9],
                     [10,11,12]]
                ], tf.int32, name='matrix')

print("\nd tf array shape: {}".format(d.shape))
print("d tf array value:\n{}".format(d.eval()))
sess.close()


# In[3]:


import tensorflow as tf 
import numpy as np

a = np.array([2,3],dtype = np.int32)

b = tf.constant(5.0, tf.float32)
c = tf.constant([5.0,6.0], tf.float32)
d = tf.constant([[5.0,6.0, 7.0],[7.0,8.0,9.0]], tf.float32, name='matrix')


e = tf.placeholder(tf.int32, name='yake_input')

# 利用shape函数引入numpy.array
shape = tf.shape(a, name='a_shape')

sess = tf.Session()
#out = sess.run(e)
out = sess.run(shape)
sess.close()
print ("shape of a: " ,out)
print "a value: ",a
print("b shape: ",b)
print("c shape: ",c)
print("d shape: ",d)
print("e shape: ",e)

关于打印

for step in range(1001):
    curr_W, curr_b, curr_loss, _ = sess.run([W, b, cost, train], {x: x_train, y: y_train})
    
    if step % 200 == 0:
        print("step: %s W: %s b: %s loss: %s"%(step, curr_W, curr_b, curr_loss))

print("b value: {}".format(b.eval()))

print("step: %s loss: %s" %(step loss))

print "Step: ", step, "Loss: ", loss

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