YMatsu mpozpnd
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| import numpy as np | |
| import numpy.linalg | |
| from tqdm import tqdm | |
| class CTRModel: | |
| """ | |
| もとの論文: | |
| Chong Wang, David M. Blei, ''Collaborative Topic Modeling for Recommending Scientific Articles'', KDD2011 | |
| """ |
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| import torch as t | |
| import torch.nn | |
| from torch.nn import * | |
| import numpy as np | |
| class CIN(torch.nn.Module): | |
| def __init__(self, num_fields, embedding_dim, num_layers, Hk=None): | |
| super(CIN, self).__init__() | |
| self.num_fields = num_fields | |
| self.embedding_dim = embedding_dim |
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| #!/usr/bin/env python | |
| # vim: set fileencoding=utf-8 : | |
| # | |
| # Author: Graphium | |
| # URL: http://tehutehu.com | |
| # License: MIT License | |
| # Created: 2016-12-22 | |
| # | |
| import theano | |
| import theano.tensor as T |
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| function t2conda () { | |
| ssh -t t2a006170 "export https_proxy=proxy.noc.titech.ac.jp:3128;conda $@" | |
| } |
mpozpnd
/ sgd_momentum
Created
April 6, 2015 15:43
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| def sgd_momentum(lr,momentum,cost,params,L1_rate,L2_rate):↲ | |
| gparams = [T.grad(cost + L1_rate * param.norm(L=1) + L2_rate * param.norm(L=2), param) for param in params]↲ | |
| ex2=[theano.shared(np.zeros_like(param.get_value(borrow=True))) for param in params]↲ | |
| updates=OrderedDict()↲ | |
| for gparam,param,ex2_ in zip(gparams,params,ex2):↲ | |
| updates[ex2_]=T.cast(momentum * ex2_ - lr * gparam,theano.config.floatX)↲ | |
| updates[param]=T.cast(param + momentum * ex2_ - lr * gparam,theano.config.floatX)↲ | |
| return updates,ex2,gparams↲ |
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| def adam(a,b1,b2,la,t,cost,params,L1_rate,L2_rate,eps_ = 1e-08):↲ | |
| updates=OrderedDict()↲ | |
| gparams=[T.grad(cost+ L1_rate * param.norm(L=1) + L2_rate * param.norm(L=2),param) for param in params]↲ | |
| ↲ | |
| b1 = b1 * (la ** t)↲ | |
| t = t + 1↲ | |
| ↲ | |
| ↲ | |
| mt=[theano.shared(np.zeros_like(param.get_value(borrow=True))) for param in params]↲ | |
| vt=[theano.shared(np.zeros_like(param.get_value(borrow=True))) for param in params]↲ |
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| def adadelta(rho_,eps_,cost,params,L1_rate,L2_rate):↲ | |
| updates = OrderedDict()↲ | |
| gparams = [T.grad(cost + L1_rate * param.norm(L=1) + L2_rate * param.norm(L=2) , param) for param in params]↲ | |
| gparams_mean=[gparam.mean() for gparam in gparams]↲ | |
| eg2=[theano.shared(np.zeros_like(param.get_value(borrow=True))) for param in params]↲ | |
| dxt=[theano.shared(np.zeros_like(param.get_value(borrow=True))) for param in params]↲ | |
| ex2=[theano.shared(np.zeros_like(param.get_value(borrow=True))) for param in params]↲ | |
| for gparam, param, eg2_,ex2_ in zip(gparams,params,eg2,ex2):↲ | |
| updates[eg2_]=T.cast(rho_ * eg2_ +(1. -rho_)*(gparam **2) ,theano.config.floatX)↲ | |
| dparam = -T.sqrt((ex2_ + eps_)/(updates[eg2_] + eps_)) * gparam↲ |
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| def adagrad(lr,eps_,cost,params,L1_rate,L2_rate):↲ | |
| updates=OrderedDict()↲ | |
| gparams=[T.grad(cost+ L1_rate * param.norm(L=1) + L2_rate * param.norm(L=2),param) for param in params]↲ | |
| gsum=[theano.shared(np.zeros_like(param.get_value(borrow=True))) for param in params]↲ | |
| for gparam, param, gsum_ in zip(gparams,params,gsum):↲ | |
| updates[param]=T.cast(param - lr * gparam / T.sqrt(1 + gsum_),theano.config.floatX)↲ | |
| updates[gsum_]=T.cast(gsum_ + gparam ** 2,theano.config.floatX)↲ | |
| return updates,gsum ,gparams |