Deep Learning Frameworks

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《Improving Deep Neural Networks》第三周时，提及了一下框架

单独开一篇，待后续一个一个补啦～

choosing deep learning frameworks

  • Ease of programming(development and deployment)
  • Running speed
  • Truly open(open source with good governance)

kinds

以字母序为顺序

Caffe/Caffe2

CNTK

DL4J

Keras

Lasagne

mxnet

PaddlePaddle

TensorFlow

试体验：

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import numpy as np
import tensorflow as tf
w = tf.Variable(0, dtype=tf.float32)
"""
# w^2-10w+25
# cost =  tf.add(tf.add(w**2,tf.multiply(-10., w)), 25)
# tensorflow已经重载了运算符，这样也是可以的
cost = w**2 - 10* w + 25
"""
# 更一般的
coefficients = np.array([[1.], [-20.], [100.]])
x = tf.placeholder(tf.float32, [3,1])
cost = x[0][0]*w**2 + x[1][0]* w + x[2][0]
# 0.01是学习速率，目标是最小化代价函数cost
train=tf.train.GradientDescentOptimizer(0.01).minimize(cost)
# 初始化
init = tf.global_variables_initializer()
"""
session = tf.Session()
session.run(init)
# 迭代多次
for i in range(1000):
	# session.run(train)
    session.run(train, feed_dict={x:coefficients})
    
# output
print(session.run(w))
"""
# 当有内循环出现的时候，用with在清理的时候会更优秀一些
with tf.Session() as session:
    session.run(init)
    for i in range(1000):
    	session.run(train, feed_dict={x:coefficients})
	print(session.run(w))

Theano

Torch

后记

雾霾越来越大了呢

健身房里人也越来越多了

羡慕室友的小腹肌

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