查看“Python TensorFlow Basics”的源代码

=讲课=

斯坦福大学教程Lecture 1和Lecture 2

回归模型 regression model（Lecture 1）

线性回归 linear model（Lecture 2）

=IPython=

* IPython 

命令行输入:$ ipython notebook

或者使用：$jupyter notebook


=Python Math library=

https://docs.python.org/3.6/library/math.html


==matplotlib==
（matplotlib/pyplot）展示WaveForm  AudioPlot

<code>
 import matplotlib.pyplot as plt
 from wavReader import readWav

 rate, data =readWav('c:\\Users\\icenter\\Documents\\a.wav')

 plt.plot(data)

 plt.show()

</code>

== sigmoid==
（http://matplotlib.org/）

 import matplotlib.pyplot as plt
 import numpy as np

 x = np.linspace(-5,5,100)

 sigmoid = lambda x: 1 / (1 + np.exp(-x))

 plt.plot(x,sigmoid(x), color='red', lw=2)

 plt.show()

==softmax函数==

 import math
 import matplotlib.plot as plt

 w=[1,2,3,4,5,6,7,8,9]

 w_exp=[math.exp(i) for i in w]

 print(w_exp)

 sum_w_exp = sum(w_exp)

 softmax = [round(i / sum_w_exp, 3) for i in w_exp]

 print(softmax)

 print(sum(softmax))

 plt.plot(softmax)

 plt.show()

----

=Tensorflow=

TensorFlow：opensource machine intelligence libraries


 import tensorflow as tf
 import numpy

 A=tf.Variable([[1,2],[3,4]], dtype=tf.float32)
 A.get_shape()

TensorShape([Dimension(2), Dimension(2)])

 B=tf.Variable([[5,6],[7,8]],dtype=tf.float32)
 B.get_shape()

TensorShape([Dimension(2), Dimension(2)])

 C=tf.matmul(A,B)

 tf.global_variables()
[<tf.Variable 'Variable:0' shape=(2, 2) dtype=float32_ref>, <tf.Variable 'Variable_1:0' shape=(2, 2) dtype=float32_ref>]

 init = tf.global_variables_initializer()

 sess =tf.Session()

 sess.run(init)

 print(sess.run(C))
[[ 19.  22.]
[ 43.  50.]]

 print(sess.run(tf.reduce_sum(C, 0)))
[ 62.  72.]

 print(sess.run(tf.reduce_sum(C, 1)))
[ 41.  93.]

=TensorFlow 的表示= 

==Sigmoid 函数/SOFTMAX 函数/SOFTPLUS函数==

（https://www.tensorflow.org/api_guides/python/nn）

 R=tf.Variable([1.,.2,3],dtype=tf.float32)

 T=tf.Variable([True, True, False, False], dtype=tf.bool)

 U=tf.Variable([True, True, True, False], dtype=tf.bool)

 init=tf.global_variables_initializer()

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

 print(sess.run(tf.sigmoid(R, name="last")))

 print(sess.run(tf.softmax(R,dim=-1, name="last")))

 print(sess.run(tf.nn.softplus(R,name="last")))

 print(sess.run(tf.nn.relu(R,name="XXX")))

 print(sess.run(tf.cast(T, tf.int32)))

 print(sess.run(tf.cast(T, tf.float32)))

 print(sess.run(tf.reduce_mean(tf.cast(T, tf.float32))))

==Tensorflow的reshape函数==

 tf.reshape()

 Y=tf.Variable([[1,2,3],[4,5,6]],dtype=tf.float32)

 sess.run(tf.global_variables_initializer())

 print(sess.run(Y))
[[1 2 3]
[4 5 6]]

 print(sess.run(tf.reshape(Y,[6])))
[ 1.  2.  3.  4.  5.  6.]


 print(sess.run(tf.reshape(Y,[3,2])))

<nowiki>array([[1 2]
       [3 4]
       [5 6]], dtype=float32)
</nowiki>
 print(sess.run(tf.reshape(Y,[-1])))

array([1 2 3 4 5 6],dtype=float32)

 print(sess.run(tf.reshape(Y, [-1,1])))

<nowiki>
array([[ 1.],
       [ 2.],
       [ 3.],
       [ 4.],
       [ 5.],
       [ 6.]], dtype=float32)
</nowiki>

=tf.argmax=

tf.argmax is an extremely useful function which gives you the index of the highest entry in a tensor along some axis.

 print(sess.run(tf.argmax(Y,1)))

[2 2]

 print(sess.run(tf.argmax(Y,0)))

[1 1 1]

=TensorFlow Saver= 

# Add ops to save and restore all the variables.
 saver = tf.train.Saver()

  # Save the variables to disk.
  save_path = saver.save(sess, "/tmp/model.ckpt")
  print("Model saved in file: %s" % save_path)


  saver.restore(sess, "/tmp/model.ckpt")
  print("Model restored.")
  # Do some work with the model

# current file path

 import os
 os.path.realpath('.')



=线性代数 Linear Algebra=

matrix(matrices) and vector(vectors)

addition and scalar multiplication

Matrix and vector multiplication

matrix and matrix multiplication

matrix inverse/transpose

(https://www.tensorflow.org/api_docs/python/tf/matrix_inverse)

(https://www.tensorflow.org/api_guides/python/math_ops)


=Octave =

展示Sigmoid函数和Softplus函数

 x = -5:0.1:5

 plot(x, y=1 ./(1+e.^-x)

 plot(x, z = log(1+e.^x))