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如何看待英雄联盟S7小组赛EDGvsSKT领先一万经济被翻盘
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本帖最后由 一只菜鸟飞过来 于
21:06 编辑
import argparse
import json
import time
import datetime
import numpy as np
import code
import socket
import os
import sys
import cPickle as pickle
from imagernn.data_provider import getDataProvider
from imagernn.solver import Solver
from imagernn.imagernn_utils import decodeGenerator, eval_split
def preProBuildWordVocab(sentence_iterator, word_count_threshold):
&&# count up all word counts so that we can threshold
&&# this shouldnt be too expensive of an operation
&&print('preprocessing word counts and creating vocab based on word count threshold %d' % (word_count_threshold))
&&t0 = time.time()
&&word_counts = {}
&&nsents = 0
&&for sent in sentence_iterator:
& & nsents += 1
& & for w in sent['tokens']:
& && &word_counts[w] = word_counts.get(w, 0) + 1
&&vocab = [w for w in word_counts if word_counts[w] &= word_count_threshold]
&&print('filtered words from %d to %d in %.2fs' % (len(word_counts),len(vocab),time.time() - t0))
&&# with K distinct words:
&&# - there are K+1 possible inputs (START token and all the words)
&&# - there are K+1 possible outputs (END token and all the words)
&&# we use ixtoword to take predicted indeces and map them to words for output visualization
&&# we use wordtoix to take raw words and get their index in word vector matrix
&&ixtoword = {}
&&ixtoword[0] = '.'&&# period at the end of the sentence. make first dimension be end token
&&wordtoix = {}
&&wordtoix['#START#'] = 0 # make first vector be the start token
&&ix = 1
&&for w in vocab:
& & wordtoix[w] = ix
& & ixtoword[ix] = w
& & ix += 1
&&# compute bias vector, which is related to the log probability of the distribution
&&# of the labels (words) and how often they occur. We will use this vector to initialize
&&# the decoder weights, so that the loss function doesnt show a huge increase in performance
&&# very quickly (which is just the network learning this anyway, for the most part). This makes
&&# the visualizations of the cost function nicer because it doesn't look like a hockey stick.
&&# for example on Flickr8K, doing this brings down initial perplexity from ~2500 to ~170.
&&word_counts['.'] = nsents
&&bias_init_vector = np.array([1.0*word_counts[ixtoword[i]] for i in ixtoword])
&&bias_init_vector /= np.sum(bias_init_vector) # normalize to frequencies
&&bias_init_vector = np.log(bias_init_vector)
&&bias_init_vector -= np.max(bias_init_vector) # shift to nice numeric range
&&return wordtoix, ixtoword, bias_init_vector
def RNNGenCost(batch, model, params, misc):
&&&&& cost function, returns cost and gradients for model &&&
&&regc = params['regc'] # regularization cost
&&BatchGenerator = decodeGenerator(params)
&&wordtoix = misc['wordtoix']
&&# forward the RNN on each image sentence pair
&&# the generator returns a list of matrices that have word probabilities
&&# and a list of cache objects that will be needed for backprop
&&Ys, gen_caches = BatchGenerator.forward(batch, model, params, misc, predict_mode = False)
&&# compute softmax costs for all generated sentences, and the gradients on top
&&loss_cost = 0.0
&&dYs = []
&&logppl = 0.0
&&logppln = 0
&&for i,pair in enumerate(batch):
& & img = pair['image']
& & # ground truth indeces for this sentence we expect to see
& & gtix = [ wordtoix[w] for w in pair['sentence']['tokens'] if w in wordtoix ]
& & gtix.append(0) # don't forget END token must be predicted in the end!
& & # fetch the predicted probabilities, as rows
& & Y = Ys[i]
& & maxes = np.amax(Y, axis=1, keepdims=True)
& & e = np.exp(Y - maxes) # for numerical stability shift into good numerical range
& & P = e / np.sum(e, axis=1, keepdims=True)
& & loss_cost += - np.sum(np.log(1e-20 + P[range(len(gtix)),gtix])) # note: add smoothing to not get infs
& & logppl += - np.sum(np.log2(1e-20 + P[range(len(gtix)),gtix])) # also accumulate log2 perplexities
& & logppln += len(gtix)
& & # lets be clever and optimize for speed here to derive the gradient in place quickly
& & for iy,y in enumerate(gtix):
& && &P[iy,y] -= 1 # softmax derivatives are pretty simple
& & dYs.append(P)
&&# backprop the RNN
&&grads = BatchGenerator.backward(dYs, gen_caches)
&&# add L2 regularization cost and gradients
&&reg_cost = 0.0
&&if regc & 0:& &
& & for p in misc['regularize']:
& && &mat = model[p]
& && &reg_cost += 0.5 * regc * np.sum(mat * mat)
& && &grads[p] += regc * mat
&&# normalize the cost and gradient by the batch size
&&batch_size = len(batch)
&&reg_cost /= batch_size
&&loss_cost /= batch_size
&&for k in grads: grads[k] /= batch_size
&&# return output in json
&&out = {}
&&out['cost'] = {'reg_cost' : reg_cost, 'loss_cost' : loss_cost, 'total_cost' : loss_cost + reg_cost}
&&out['grad'] = grads
&&out['stats'] = { 'ppl2' : 2 ** (logppl / logppln)}
&&return out
def main(params):
&&batch_size = params['batch_size']
&&dataset = params['dataset']
&&word_count_threshold = params['word_count_threshold']
&&do_grad_check = params['do_grad_check']
&&max_epochs = params['max_epochs']
&&host = socket.gethostname() # get computer hostname
&&# fetch the data provider
&&dp = getDataProvider(dataset)
&&misc = {} # stores various misc items that need to be passed around the framework
&&# go over all training sentences and find the vocabulary we want to use, i.e. the words that occur
&&# at least word_count_threshold number of times
&&misc['wordtoix'], misc['ixtoword'], bias_init_vector = preProBuildWordVocab(dp.iterSentences('train'), word_count_threshold)
&&# delegate the initialization of the model to the Generator class
&&BatchGenerator = decodeGenerator(params)
&&init_struct = BatchGenerator.init(params, misc)
&&model, misc['update'], misc['regularize'] = (init_struct['model'], init_struct['update'], init_struct['regularize'])
&&# force overwrite here. This is a bit of a hack, not happy about it
&&model['bd'] = bias_init_vector.reshape(1, bias_init_vector.size)
&&print('model init done.')
&&print('model has keys: ' + ', '.join(model.keys()))
&&print('updating: ' + ', '.join( '%s [%dx%d]' % (k, model[k].shape[0], model[k].shape[1]) for k in misc['update']))
&&print('updating: ' + ', '.join( '%s [%dx%d]' % (k, model[k].shape[0], model[k].shape[1]) for k in misc['regularize']))
&&print('number of learnable parameters total: %d' % (sum(model[k].shape[0] * model[k].shape[1] for k in misc['update']))
&&if params.get('init_model_from',''):
& && &&&# load checkpoint
& && &&&checkpoint = pickle.load(open(params['init_model_from'], 'rb'))
& && &&&model = checkpoint['model'] # overwrite the model
& && &&&
&&# initialize the Solver and the cost function
&&solver = Solver()
&&def costfun(batch, model):
& & # wrap the cost function to abstract some things away from the Solver
& & return RNNGenCost(batch, model, params, misc)
&&# calculate how many iterations we need
&&num_sentences_total = dp.getSplitSize('train', ofwhat = 'sentences')
&&num_iters_one_epoch = num_sentences_total / batch_size
&&max_iters = max_epochs * num_iters_one_epoch
&&eval_period_in_epochs = params['eval_period']
&&eval_period_in_iters = max(1, int(num_iters_one_epoch * eval_period_in_epochs))
&&abort = False
&&top_val_ppl2 = -1
&&smooth_train_ppl2 = len(misc['ixtoword']) # initially size of dictionary of confusion
&&val_ppl2 = len(misc['ixtoword'])
&&last_status_write_time = 0 # for writing worker job status reports
&&json_worker_status = {}
&&json_worker_status['params'] = params
&&json_worker_status['history'] = []
&&for it in xrange(max_iters):
& & if abort: break
& & t0 = time.time()
& & # fetch a batch of data
& & batch = [dp.sampleImageSentencePair() for i in xrange(batch_size)]
& & # evaluate cost, gradient and perform parameter update
& & step_struct = solver.step(batch, model, costfun, **params)
& & cost = step_struct['cost']
& & dt = time.time() - t0
& & # print training statistics
& & train_ppl2 = step_struct['stats']['ppl2']
& & smooth_train_ppl2 = 0.99 * smooth_train_ppl2 + 0.01 * train_ppl2 # smooth exponentially decaying moving average
& & if it == 0: smooth_train_ppl2 = train_ppl2 # start out where we start out
& & epoch = it * 1.0 / num_iters_one_epoch
& & print '%d/%d batch done in %.3fs. at epoch %.2f. loss cost = %f, reg cost = %f, ppl2 = %.2f (smooth %.2f)' \
& && && & % (it, max_iters, dt, epoch, cost['loss_cost'], cost['reg_cost'], \
& && && && & train_ppl2, smooth_train_ppl2)
& & # perform gradient check if desired, with a bit of a burnin time (10 iterations)
& & if it == 10 and do_grad_check:
& && &print 'disabling dropout for gradient check...'
& && &params['drop_prob_encoder'] = 0
& && &params['drop_prob_decoder'] = 0
& && &solver.gradCheck(batch, model, costfun)
& && &print 'done gradcheck, exitting.'
& && &sys.exit() # hmmm. probably should exit here
& & # detect if loss is exploding and kill the job if so
& & total_cost = cost['total_cost']
& & if it == 0:
& && &total_cost0 = total_cost # store this initial cost
& & if total_cost & total_cost0 * 2:
& && &print 'Aboring, cost seems to be exploding. Run gradcheck? Lower the learning rate?'
& && &abort = True # set the abort flag, we'll break out
& & # logging: write JSON files for visual inspection of the training
& & tnow = time.time()
& & if tnow & last_status_write_time + 60*1: # every now and then lets write a report
& && &last_status_write_time = tnow
& && &jstatus = {}
& && &jstatus['time'] = datetime.datetime.now().isoformat()
& && &jstatus['iter'] = (it, max_iters)
& && &jstatus['epoch'] = (epoch, max_epochs)
& && &jstatus['time_per_batch'] = dt
& && &jstatus['smooth_train_ppl2'] = smooth_train_ppl2
& && &jstatus['val_ppl2'] = val_ppl2 # just write the last available one
& && &jstatus['train_ppl2'] = train_ppl2
& && &json_worker_status['history'].append(jstatus)
& && &status_file = os.path.join(params['worker_status_output_directory'], host + '_status.json')
& && &try:
& && &&&json.dump(json_worker_status, open(status_file, 'w'))
& && &except Exception, e: # todo be more clever here
& && &&&print 'tried to write worker status into %s but got error:' % (status_file, )
& && &&&print e
& & # perform perplexity evaluation on the validation set and save a model checkpoint if it's good
& & is_last_iter = (it+1) == max_iters
& & if (((it+1) % eval_period_in_iters) == 0 and it & max_iters - 5) or is_last_iter:
& && &val_ppl2 = eval_split('val', dp, model, params, misc) # perform the evaluation on VAL set
& && &print 'validation perplexity = %f' % (val_ppl2, )
& && &
& && &# abort training if the perplexity is no good
& && &min_ppl_or_abort = params['min_ppl_or_abort']
& && &if val_ppl2 & min_ppl_or_abort and min_ppl_or_abort & 0:
& && &&&print 'aborting job because validation perplexity %f & %f' % (val_ppl2, min_ppl_or_abort)
& && &&&abort = True # abort the job
& && &write_checkpoint_ppl_threshold = params['write_checkpoint_ppl_threshold']
& && &if val_ppl2 & top_val_ppl2 or top_val_ppl2 & 0:
& && &&&if val_ppl2 & write_checkpoint_ppl_threshold or write_checkpoint_ppl_threshold & 0:
& && && & # if we beat a previous record or if this is the first time
& && && & # AND we also beat the user-defined threshold or it doesnt exist
& && && & top_val_ppl2 = val_ppl2
& && && & filename = 'model_checkpoint_%s_%s_%s_%.2f.p' % (dataset, host, params['fappend'], val_ppl2)
& && && & filepath = os.path.join(params['checkpoint_output_directory'], filename)
& && && & checkpoint = {}
& && && & checkpoint['it'] = it
& && && & checkpoint['epoch'] = epoch
& && && & checkpoint['model'] = model
& && && & checkpoint['params'] = params
& && && & checkpoint['perplexity'] = val_ppl2
& && && & checkpoint['wordtoix'] = misc['wordtoix']
& && && & checkpoint['ixtoword'] = misc['ixtoword']
& && && & try:
& && && && &pickle.dump(checkpoint, open(filepath, &wb&))
& && && && &print 'saved checkpoint in %s' % (filepath, )
& && && & except Exception, e: # todo be more clever here
& && && && &print 'tried to write checkpoint into %s but got error: ' % (filepat, )
& && && && &print e
if __name__ == &__main__&:
&&parser = argparse.ArgumentParser()
&&# global setup settings, and checkpoints
&&parser.add_argument('-d', '--dataset', dest='dataset', default='flickr8k', help='dataset: flickr8k/flickr30k')
&&parser.add_argument('-a', '--do_grad_check', dest='do_grad_check', type=int, default=0, help='perform gradcheck? program will block for visual inspection and will need manual user input')
&&parser.add_argument('--fappend', dest='fappend', type=str, default='baseline', help='append this string to checkpoint filenames')
&&parser.add_argument('-o', '--checkpoint_output_directory', dest='checkpoint_output_directory', type=str, default='cv/', help='output directory to write checkpoints to')
&&parser.add_argument('--worker_status_output_directory', dest='worker_status_output_directory', type=str, default='status/', help='directory to write worker status JSON blobs to')
&&parser.add_argument('--write_checkpoint_ppl_threshold', dest='write_checkpoint_ppl_threshold', type=float, default=-1, help='ppl threshold above which we dont bother writing a checkpoint to save space')
&&parser.add_argument('--init_model_from', dest='init_model_from', type=str, default='', help='initialize the model parameters from some specific checkpoint?')
&&
&&# model parameters
&&parser.add_argument('--generator', dest='generator', type=str, default='lstm', help='generator to use')
&&parser.add_argument('--image_encoding_size', dest='image_encoding_size', type=int, default=256, help='size of the image encoding')
&&parser.add_argument('--word_encoding_size', dest='word_encoding_size', type=int, default=256, help='size of word encoding')
&&parser.add_argument('--hidden_size', dest='hidden_size', type=int, default=256, help='size of hidden layer in generator RNNs')
&&# lstm-specific params
&&parser.add_argument('--tanhC_version', dest='tanhC_version', type=int, default=0, help='use tanh version of LSTM?')
&&# rnn-specific params
&&parser.add_argument('--rnn_relu_encoders', dest='rnn_relu_encoders', type=int, default=0, help='relu encoders before going to RNN?')
&&parser.add_argument('--rnn_feed_once', dest='rnn_feed_once', type=int, default=0, help='feed image to the rnn only single time?')
&&# optimization parameters
&&parser.add_argument('-c', '--regc', dest='regc', type=float, default=1e-8, help='regularization strength')
&&parser.add_argument('-m', '--max_epochs', dest='max_epochs', type=int, default=50, help='number of epochs to train for')
&&parser.add_argument('--solver', dest='solver', type=str, default='rmsprop', help='solver type: vanilla/adagrad/adadelta/rmsprop')
&&parser.add_argument('--momentum', dest='momentum', type=float, default=0.0, help='momentum for vanilla sgd')
&&parser.add_argument('--decay_rate', dest='decay_rate', type=float, default=0.999, help='decay rate for adadelta/rmsprop')
&&parser.add_argument('--smooth_eps', dest='smooth_eps', type=float, default=1e-8, help='epsilon smoothing for rmsprop/adagrad/adadelta')
&&parser.add_argument('-l', '--learning_rate', dest='learning_rate', type=float, default=1e-3, help='solver learning rate')
&&parser.add_argument('-b', '--batch_size', dest='batch_size', type=int, default=100, help='batch size')
&&parser.add_argument('--grad_clip', dest='grad_clip', type=float, default=5, help='clip gradients (normalized by batch size)? elementwise. if positive, at what threshold?')
&&parser.add_argument('--drop_prob_encoder', dest='drop_prob_encoder', type=float, default=0.5, help='what dropout to apply right after the encoder to an RNN/LSTM')
&&parser.add_argument('--drop_prob_decoder', dest='drop_prob_decoder', type=float, default=0.5, help='what dropout to apply right before the decoder in an RNN/LSTM')
&&# data preprocessing parameters
&&parser.add_argument('--word_count_threshold', dest='word_count_threshold', type=int, default=5, help='if a word occurs less than this number of times in training data, it is discarded')
&&# evaluation parameters
&&parser.add_argument('-p', '--eval_period', dest='eval_period', type=float, default=1.0, help='in units of epochs, how often do we evaluate on val set?')
&&parser.add_argument('--eval_batch_size', dest='eval_batch_size', type=int, default=100, help='for faster validation performance evaluation, what batch size to use on val img/sentences?')
&&parser.add_argument('--eval_max_images', dest='eval_max_images', type=int, default=-1, help='for efficiency we can use a smaller number of images to get validation error')
&&parser.add_argument('--min_ppl_or_abort', dest='min_ppl_or_abort', type=float , default=-1, help='if validation perplexity is below this threshold the job will abort')
&&args = parser.parse_args()
&&params = vars(args) # convert to ordinary dict
&&print 'parsed parameters:'
&&print json.dumps(params, indent = 2)
&&main(params)
复制代码
程序运行到148行的if语句时出现错误：invalid syntax
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按钮，否则您的代码可能会被“吃掉”！
& 累计签到：231 天连续签到：1 天
前一句 括号没配对
1. 如果您的提问得到满意的答案，请务必选择【最佳答案】；2. 如果想鼓励一下楼主或帮助到您的朋友，可以给他们【评分】作为奖励；3. 善用【论坛搜索】功能，那里可能有您想要的答案；4. 粘贴代码请点击编辑框上的
按钮，否则您的代码可能会被“吃掉”！
& 累计签到：65 天连续签到：1 天
前一句 括号没配对
还真是，我一直盯着那个if语句看了，忽略了上面，你能看出来这个程序是干嘛用的吗？
1. 如果您的提问得到满意的答案，请务必选择【最佳答案】；2. 如果想鼓励一下楼主或帮助到您的朋友，可以给他们【评分】作为奖励；3. 善用【论坛搜索】功能，那里可能有您想要的答案；4. 粘贴代码请点击编辑框上的
按钮，否则您的代码可能会被“吃掉”！
& 累计签到：2 天连续签到：2 天
前一句 括号没配对
我就说为什么这头像这么熟悉，一个月才反应过来是猫梓酱
1. 如果您的提问得到满意的答案，请务必选择【最佳答案】；2. 如果想鼓励一下楼主或帮助到您的朋友，可以给他们【评分】作为奖励；3. 善用【论坛搜索】功能，那里可能有您想要的答案；4. 粘贴代码请点击编辑框上的
按钮，否则您的代码可能会被“吃掉”！
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GitHub上找到了一个自动合成徽章的脚本，请码神看看有
发表于 前天&14:08
玩GO-get好游戏官方认证
我刚刚在玩GO推荐游戏领取了10Q币
GitHub上找到了一个自动合成徽章的脚本，请码神看看有没有问题
全平台震撼公测 快来创造你的世界吧!
号召小伙伴一起加入 赢游戏限量周边!}