mirror of
https://github.com/wassname/keras-language-modeling.git
synced 2026-09-10 12:15:18 +08:00
added command line argument for serving to a port
This commit is contained in:
Ben Bolte
+42
-114
@@ -9,107 +9,14 @@ from time import strftime, gmtime, time
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import pickle
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import json
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from keras.callbacks import ProgbarLogger
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from keras.utils.generic_utils import Progbar
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import thread
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from scipy.stats import rankdata
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random.seed(42)
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class ProgbarError(Progbar):
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def update(self, current, values=[], force=False):
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'''
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@param current: index of current step
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@param values: list of tuples (name, value_for_last_step).
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The progress bar will display averages for these values.
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@param force: force visual progress update
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'''
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for k, v in values:
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if k not in self.sum_values:
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self.sum_values[k] = [v * (current - self.seen_so_far), current - self.seen_so_far]
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self.unique_values.append(k)
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else:
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self.sum_values[k][0] += v * (current - self.seen_so_far)
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self.sum_values[k][1] += (current - self.seen_so_far)
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self.seen_so_far = current
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now = time()
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if self.verbose == 1:
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if not force and (now - self.last_update) < self.interval:
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return
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prev_total_width = self.total_width
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sys.stderr.write("\b" * prev_total_width)
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sys.stderr.write("\r")
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numdigits = int(np.floor(np.log10(self.target))) + 1
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barstr = '%%%dd/%%%dd [' % (numdigits, numdigits)
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bar = barstr % (current, self.target)
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prog = float(current) / self.target
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prog_width = int(self.width * prog)
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if prog_width > 0:
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bar += ('=' * (prog_width-1))
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if current < self.target:
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bar += '>'
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else:
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bar += '='
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bar += ('.' * (self.width - prog_width))
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bar += ']'
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sys.stderr.write(bar)
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self.total_width = len(bar)
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if current:
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time_per_unit = (now - self.start) / current
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else:
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time_per_unit = 0
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eta = time_per_unit * (self.target - current)
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info = ''
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if current < self.target:
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info += ' - ETA: %ds' % eta
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else:
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info += ' - %ds' % (now - self.start)
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for k in self.unique_values:
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info += ' - %s:' % k
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if type(self.sum_values[k]) is list:
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avg = self.sum_values[k][0] / max(1, self.sum_values[k][1])
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if abs(avg) > 1e-3:
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info += ' %.4f' % avg
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else:
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info += ' %.4e' % avg
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else:
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info += ' %s' % self.sum_values[k]
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self.total_width += len(info)
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if prev_total_width > self.total_width:
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info += ((prev_total_width - self.total_width) * " ")
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sys.stderr.write(info)
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sys.stderr.flush()
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if current >= self.target:
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sys.stderr.write("\n")
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if self.verbose == 2:
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if current >= self.target:
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info = '%ds' % (now - self.start)
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for k in self.unique_values:
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info += ' - %s:' % k
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avg = self.sum_values[k][0] / max(1, self.sum_values[k][1])
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if avg > 1e-3:
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info += ' %.4f' % avg
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else:
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info += ' %.4e' % avg
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sys.stderr.write(info + "\n")
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self.last_update = now
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class ProgbarErrorLogger(ProgbarLogger):
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def on_epoch_begin(self, epoch, logs={}):
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if self.verbose:
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print('Epoch %d/%d' % (epoch + 1, self.nb_epoch), file=sys.stderr)
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self.progbar = ProgbarError(target=self.params['nb_sample'])
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self.seen = 0
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def log(x):
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print(x)
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class Evaluator:
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@@ -189,7 +96,6 @@ class Evaluator:
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return strftime('%Y-%m-%d %H:%M:%S', gmtime())
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def train(self):
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print('Began training at %s' % self.get_time())
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batch_size = self.params['batch_size']
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nb_epoch = self.params['nb_epoch']
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validation_split = self.params['validation_split']
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@@ -205,16 +111,17 @@ class Evaluator:
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questions += [q['question']] * len(q['answers'])
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good_answers += [self.answers[i] for i in q['answers']]
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indices += [j] * len(q['answers'])
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log('Began training at %s on %d samples' % (self.get_time(), len(questions)))
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questions = self.padq(questions)
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good_answers = self.pada(good_answers)
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val_loss = {'loss': 1., 'epoch': 0}
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def get_bad_samples(indices, top_50):
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return [self.answers[random.choice(top_50[i])] for i in indices]
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# def get_bad_samples(indices, top_50):
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# return [self.answers[random.choice(top_50[i])] for i in indices]
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for i in range(1, nb_epoch):
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for i in range(1, nb_epoch+1):
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# sample from all answers to get bad answers
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# if i % 2 == 0:
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# bad_answers = self.pada(random.sample(self.answers.values(), len(good_answers)))
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@@ -222,14 +129,15 @@ class Evaluator:
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# bad_answers = self.pada(get_bad_samples(indices, top_50))
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bad_answers = self.pada(random.sample(self.answers.values(), len(good_answers)))
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print('Fitting epoch %d' % i, file=sys.stderr)
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hist = self.model.fit([questions, good_answers, bad_answers], nb_epoch=1, batch_size=batch_size,
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validation_split=validation_split, verbose=0, callbacks=[ProgbarErrorLogger()])
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print('%s -- Epoch %d' % (self.get_time(), i), end='')
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print(' Loss = {}'.format(hist.history['val_loss'][0]))
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validation_split=validation_split, verbose=0)
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if hist.history['val_loss'][0] < val_loss['loss']:
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val_loss = {'loss': hist.history['val_loss'][0], 'epoch': i}
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print('Best: Loss = {}, Epoch = {}'.format(val_loss['loss'], val_loss['epoch']))
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log('%s -- Epoch %d ' % (self.get_time(), i) +
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'Loss = %.4f ' % hist.history['val_loss'][0] +
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'(Best: Loss = %.4f, Epoch = %.4f)' % (val_loss['loss'], val_loss['epoch']))
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self.save_epoch(i)
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@@ -240,10 +148,10 @@ class Evaluator:
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def prog_bar(self, so_far, total, n_bars=20):
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n_complete = int(so_far * n_bars / total)
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if n_complete >= n_bars - 1:
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print('\r[' + '=' * n_bars + ']', end='')
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print('\r[' + '=' * n_bars + ']', end='', file=sys.stderr)
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else:
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s = '\r[' + '=' * (n_complete - 1) + '>' + '.' * (n_bars - n_complete) + ']'
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print(s, end='')
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print(s, end='', file=sys.stderr)
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def eval_sets(self):
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if self._eval_sets is None:
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@@ -322,6 +230,26 @@ if __name__ == '__main__':
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}
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}
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if len(sys.argv) >= 2 and sys.argv[1] == 'serve':
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from flask import Flask
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app = Flask(__name__)
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port = 5000
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lines = list()
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def log(x):
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lines.append(x)
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@app.route('/')
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def home():
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return ('<html><body><h1>Training Log</h1>' +
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''.join(['<code>{}</code><br/>'.format(line) for line in lines]) +
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'</body></html>')
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def start_server():
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app.run(debug=False, use_evalex=False, port=port)
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thread.start_new_thread(start_server, tuple())
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print('Serving to port %d' % port, file=sys.stderr)
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from keras_models import AttentionModel
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evaluator = Evaluator(conf, model=AttentionModel, optimizer='rmsprop')
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@@ -331,11 +259,11 @@ if __name__ == '__main__':
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# evaluate mrr for a particular epoch
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evaluator.load_epoch(best_loss['epoch'])
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top1, mrr = evaluator.get_score(verbose=False)
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print(' - Top-1 Precision:')
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print(' - %.3f on test 1' % top1[0])
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print(' - %.3f on test 2' % top1[1])
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print(' - %.3f on dev' % top1[2])
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print(' - MRR:')
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print(' - %.3f on test 1' % mrr[0])
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print(' - %.3f on test 2' % mrr[1])
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print(' - %.3f on dev' % mrr[2])
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log(' - Top-1 Precision:')
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log(' - %.3f on test 1' % top1[0])
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log(' - %.3f on test 2' % top1[1])
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log(' - %.3f on dev' % top1[2])
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log(' - MRR:')
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log(' - %.3f on test 1' % mrr[0])
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log(' - %.3f on test 2' % mrr[1])
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log(' - %.3f on dev' % mrr[2])
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