2017年10月16日 (一) 01:53的版本

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

import matplotlib.pyplot as plt
from wavReader import readWav

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

plt.plot(data)

plt.show()

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.pyplot 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.nn.sigmoid(R, name="last")))

print(sess.run(tf.nn.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])))

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

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

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

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

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

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

1. 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

1. 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)

参考文献

[x] python-numpy, http://cs231n.github.io/python-numpy-tutorial/.