Theano编程细节

• Theano中的theano.shared()与C++中的std::shared_ptr<>()

面向对象深度神经网络

构造函数（也即\__init__）的使命是搭建舞台（该神经网络一共多少层，这些层的输入与输出，激励函数，每一层的属性），构造整个Architecture，它不负责运算，优化事宜。

theano_rng vs numpy_rng

import numpy
import theano
from theano.tensor.shared_randomstreams import RandomStreams

numpy_rng = numpy.random.RandomState(23455)
theano_rng = RandomStreams(numpy_rng.randint(2**30))

嵌入梯度下降

T.grad(cost, wrt)

第一个参数表示代价函数（是一个符号型函数，而非一个标量），第二个参数（with respect to）表示函数所关于的参数。

def get_cost_update(self, corruption_level, eta):
    tilde_x = self.get_corrupted_input(self.x, corruption_level)
    y = self.get_hidden_values(tilde_x)
    z = self.get_reconstructed_input(y)
    L = -T.sum(self.x*T.log(z)+(1-self.x)*T.log(1-z), axis=1)
    cost = T.mean(L)
    gparams = T.grad(cost, self.params)
            # self.params = [self.W, self.b, self.b_prime]
    updates = [(param, param-eta*gparam) for param, gparam in zip(self.params, gparams)] 
    return (cost, updates)

mnist数据的加载

def load_data(dataset):
    with gzip.open(dataset, 'rb') as fp:
        train_set, valid_set, test_set = pickle(fp, encoding='latin1')
    def shared_datd(data_xy, borrow=True):
        data_x, data_y = data_xy
        shared_x = theano.shared(
            numpy.asarray(data_x, dtype=theano.config.floatX), borrow=borrow
        )
        shared_y = theano.shared(
            numpy.asarray(data_y, dtype=theano.config.floatX), borrow=borrow
        )
        return  shared_x, T.cast(shared_y, 'int32')
    train_set_x, train_set_y = shared_data(train_set)
    valid_set_x, valid_set_y = shared_data(valid_set)
    test_set_x, test_set_y = shared_data(test_set)
    rval = [(train_set_x, train_set_y), (valid_set_x, valid_set_y), (test_set_x, test_set_y)]
    return rval

每一个layer的输入

for i in range(self.nlayers):
    if i == 0:
        input_size = nins
    else:
        input_size = hidden_layers_size[i-1]

简单的二分支的判断，自然可以使用python下的三目运算符进行简化，也即：

for i in range(self.nlayers):
    input_size = nins if i == 0 else hidden_layers_size[i-1]

层间的输入输出关系

这段代码见于SdA

self.sigmoid_layers = []
self.params = []
self.dA_layers = []

for i in range(self.nlayers):
    input_size = nins if i == 0 else hidden_layers_size[i-1]
    layer_input = self.x if i == 0 else self.sigmoid_layers[-1].output
    sigmoid_layer = HiddenLayer(...)
    self.sigmoid_layers.append(sigmoid_layer)
                    # 一段精彩的逻辑
    self.params.extend(sigmoid_layer.params)
                    # self.params += sigmoid_layer.params
    dA_layer = dA(...)
    self.dA_layers.append(dA_layer)