Theano学习笔记（一）――代数

import theano.tensor as T from theano import function x = T.dscalar('x') y = T.dscalar('y') z = x + y f = function([x, y], z)

import theano.tensor as T from theano import function x = T.dmatrix('x') y = T.dmatrix('y') z = x + y f = function([x, y], z)

import theano a = theano.tensor.vector() b = theano.tensor.vector() out = a ** 2 + b ** 2 + 2 * a * b f = theano.function([a,b],out) print f([0, 1],[1,2]) >>> [ 1. 9.]

import theano.tensor as T from theano import function a, b = T.dmatrices('a', 'b') diff = a - b abs_diff = abs(diff) diff_squared = diff**2 f = function([a, b], [diff, abs_diff,diff_squared]) print f([[1, 1], [1, 1]], [[0, 1], [2,3]]) >>> [array([[ 1., 0.], [-1., -2.]]), array([[ 1., 0.], [ 1., 2.]]), array([[ 1., 0.], [ 1., 4.]])]

import theano.tensor as T from theano import function from theano import Param x, y = T.dscalars('x', 'y') z = x + y f = function([x, Param(y, default=1,name='by_name')],z) print f(33) print f(33, 2) print f(33,by_name=3) >>> 34.0 35.0 36.0

import theano.tensor as T from theano import function from theano import shared state = shared(0) inc = T.iscalar('inc') accumulator = function([inc], state,updates=[(state, state+inc)]) print state.get_value() accumulator(1) print state.get_value() accumulator(300) print state.get_value() state.set_value(-1) print accumulator(3) print state.get_value() >>> 0 1 301 -1 2

decrementor = function([inc], state,updates=[(state, state-inc)]) print decrementor(2) print state.get_value() >>> 2 0

fn_of_state = state * 2 + inc foo = T.scalar(dtype=state.dtype) skip_shared = function([inc, foo],fn_of_state, givens=[(state,foo)]) print skip_shared(1, 3) print state.get_value() >>> 7 0

from theano import function from theano.tensor.shared_randomstreamsimport RandomStreams srng = RandomStreams(seed=234)#种子 rv_u = srng.uniform((2,2))#均匀分布 rv_n = srng.normal((2,2))#正态分布 f = function([], rv_u)#每次调用，每次都会更新 g = function([], rv_n,no_default_updates=True)#如果以后一直用这组随机数，就不再更新 nearly_zeros = function([], rv_u + rv_u- 2 * rv_u) print nearly_zeros()#函数每次执行只获得一个随机数，即使表达式里面有3个随机数

#分别设置，使用.rng.set_value()函数 rng_val =rv_u.rng.get_value(borrow=True) # Get the rng for rv_u rng_val.seed(89234) # seeds thegenerator rv_u.rng.set_value(rng_val,borrow=True) #全局设置，使用.seed()函数 srng.seed(902340)

state_after_v0 =rv_u.rng.get_value().get_state()#保存调用前的state nearly_zeros() # this affects rv_u's generator v1 = f()#第一个调用，之后state会变化 rng = rv_u.rng.get_value(borrow=True) rng.set_state(state_after_v0)#为其state还原 rv_u.rng.set_value(rng, borrow=True) v2 = f() # v2 != v1输出更新后state对应的随机数 v3 = f() # v3 == v1再次更新又还原成原来的state了

import theano import numpy import theano.tensor as T from theano.sandbox.rng_mrg importMRG_RandomStreams from theano.tensor.shared_randomstreamsimport RandomStreams class Graph(): def __init__(self, seed=123): self.rng = RandomStreams(seed) self.y = self.rng.uniform(size=(1,)) g1 = Graph(seed=123) f1 = theano.function([], g1.y) g2 = Graph(seed=987) f2 = theano.function([], g2.y) print 'By default, the two functionsare out of sync.' print 'f1() returns ', f1() print 'f2() returns ', f2() #输出不同的随机值 def copy_random_state(g1, g2): if isinstance(g1.rng, MRG_RandomStreams): #类型判断：其第一个参数为对象，第二个为类型名或类型名的一个列表。其返回值为布尔型。 g2.rng.rstate = g1.rng.rstate for (su1, su2) in zip(g1.rng.state_updates, g2.rng.state_updates):#打包 su2[0].set_value(su1[0].get_value())#赋值 print 'We now copy the state of thetheano random number generators.' copy_random_state(g1, g2) print 'f1() returns ', f1() print 'f2() returns ', f2() #输出相同的随机值 >>> By default, the two functions are outof sync. f1() returns [ 0.72803009] f2() returns [ 0.55056769] We now copy the state of the theanorandom number generators. f1() returns [ 0.59044123] f2() returns [ 0.59044123]