CNNSupervisedTrainer_mnist_mnistClassifier_net.py 19.8 KB
 Nicola Gatto committed Apr 25, 2019 1 2 3 4 5 6 ``````import mxnet as mx import logging import numpy as np import time import os import shutil `````` Sebastian N. committed Nov 11, 2019 7 8 9 ``````import pickle import math import sys `````` Nicola Gatto committed Apr 25, 2019 10 11 ``````from mxnet import gluon, autograd, nd `````` Sebastian N. committed Jul 09, 2019 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 ``````class CrossEntropyLoss(gluon.loss.Loss): def __init__(self, axis=-1, sparse_label=True, weight=None, batch_axis=0, **kwargs): super(CrossEntropyLoss, self).__init__(weight, batch_axis, **kwargs) self._axis = axis self._sparse_label = sparse_label def hybrid_forward(self, F, pred, label, sample_weight=None): pred = F.log(pred) if self._sparse_label: loss = -F.pick(pred, label, axis=self._axis, keepdims=True) else: label = gluon.loss._reshape_like(F, label, pred) loss = -F.sum(pred * label, axis=self._axis, keepdims=True) loss = gluon.loss._apply_weighting(F, loss, self._weight, sample_weight) return F.mean(loss, axis=self._batch_axis, exclude=True) class LogCoshLoss(gluon.loss.Loss): def __init__(self, weight=None, batch_axis=0, **kwargs): super(LogCoshLoss, self).__init__(weight, batch_axis, **kwargs) def hybrid_forward(self, F, pred, label, sample_weight=None): loss = F.log(F.cosh(pred - label)) loss = gluon.loss._apply_weighting(F, loss, self._weight, sample_weight) return F.mean(loss, axis=self._batch_axis, exclude=True) `````` Sebastian N. committed Nov 11, 2019 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 ``````class SoftmaxCrossEntropyLossIgnoreIndices(gluon.loss.Loss): def __init__(self, axis=-1, ignore_indices=[], sparse_label=True, from_logits=False, weight=None, batch_axis=0, **kwargs): super(SoftmaxCrossEntropyLossIgnoreIndices, self).__init__(weight, batch_axis, **kwargs) self._axis = axis self._ignore_indices = ignore_indices self._sparse_label = sparse_label self._from_logits = from_logits def hybrid_forward(self, F, pred, label, sample_weight=None): log_softmax = F.log_softmax pick = F.pick if not self._from_logits: pred = log_softmax(pred, self._axis) if self._sparse_label: loss = -pick(pred, label, axis=self._axis, keepdims=True) else: label = _reshape_like(F, label, pred) loss = -(pred * label).sum(axis=self._axis, keepdims=True) # ignore some indices for loss, e.g. tokens in NLP applications for i in self._ignore_indices: loss = loss * mx.nd.logical_not(mx.nd.equal(mx.nd.argmax(pred, axis=1), mx.nd.ones_like(mx.nd.argmax(pred, axis=1))*i)) return loss.mean(axis=self._batch_axis, exclude=True) @mx.metric.register class BLEU(mx.metric.EvalMetric): N = 4 def __init__(self, exclude=None, name='bleu', output_names=None, label_names=None): super(BLEU, self).__init__(name=name, output_names=output_names, label_names=label_names) self._exclude = exclude or [] self._match_counts = [0 for _ in range(self.N)] self._counts = [0 for _ in range(self.N)] self._size_ref = 0 self._size_hyp = 0 def update(self, labels, preds): labels, preds = mx.metric.check_label_shapes(labels, preds, True) new_labels = self._convert(labels) new_preds = self._convert(preds) for label, pred in zip(new_labels, new_preds): reference = [word for word in label if word not in self._exclude] hypothesis = [word for word in pred if word not in self._exclude] self._size_ref += len(reference) self._size_hyp += len(hypothesis) for n in range(self.N): reference_ngrams = self._get_ngrams(reference, n + 1) hypothesis_ngrams = self._get_ngrams(hypothesis, n + 1) match_count = 0 for ngram in hypothesis_ngrams: if ngram in reference_ngrams: reference_ngrams.remove(ngram) match_count += 1 self._match_counts[n] += match_count self._counts[n] += len(hypothesis_ngrams) def get(self): precisions = [sys.float_info.min for n in range(self.N)] i = 1 for n in range(self.N): match_counts = self._match_counts[n] counts = self._counts[n] if counts != 0: if match_counts == 0: i *= 2 match_counts = 1 / i `````` Christian Fuß committed Nov 12, 2019 117 118 `````` if (match_counts / counts) > 0: precisions[n] = match_counts / counts `````` Sebastian N. committed Nov 11, 2019 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 `````` bleu = self._get_brevity_penalty() * math.exp(sum(map(math.log, precisions)) / self.N) return (self.name, bleu) def calculate(self): precisions = [sys.float_info.min for n in range(self.N)] i = 1 for n in range(self.N): match_counts = self._match_counts[n] counts = self._counts[n] if counts != 0: if match_counts == 0: i *= 2 match_counts = 1 / i precisions[n] = match_counts / counts return self._get_brevity_penalty() * math.exp(sum(map(math.log, precisions)) / self.N) def _get_brevity_penalty(self): if self._size_hyp >= self._size_ref: return 1 else: return math.exp(1 - (self._size_ref / self._size_hyp)) @staticmethod def _get_ngrams(sentence, n): ngrams = [] if len(sentence) >= n: for i in range(len(sentence) - n + 1): ngrams.append(sentence[i:i+n]) return ngrams @staticmethod def _convert(nd_list): if len(nd_list) == 0: return [] new_list = [[] for _ in range(nd_list[0].shape[0])] for element in nd_list: for i in range(element.shape[0]): new_list[i].append(element[i].asscalar()) return new_list `````` Sebastian Nickels committed May 27, 2019 173 ``````class CNNSupervisedTrainer_mnist_mnistClassifier_net: `````` Sebastian N. committed Jun 21, 2019 174 `````` def __init__(self, data_loader, net_constructor): `````` Nicola Gatto committed Apr 25, 2019 175 176 `````` self._data_loader = data_loader self._net_creator = net_constructor `````` Sebastian N. committed Jun 21, 2019 177 `````` self._networks = {} `````` Nicola Gatto committed Apr 25, 2019 178 179 180 181 `````` def train(self, batch_size=64, num_epoch=10, eval_metric='acc', `````` Sebastian N. committed Oct 30, 2019 182 `````` eval_metric_params={}, `````` Sebastian N. committed Jul 09, 2019 183 184 `````` loss ='softmax_cross_entropy', loss_params={}, `````` Nicola Gatto committed Apr 25, 2019 185 186 187 188 189 `````` optimizer='adam', optimizer_params=(('learning_rate', 0.001),), load_checkpoint=True, context='gpu', checkpoint_period=5, `````` Sebastian N. committed Nov 11, 2019 190 `````` save_attention_image=False, `````` Sebastian N. committed Nov 22, 2019 191 `````` use_teacher_forcing=False, `````` Nicola Gatto committed Apr 25, 2019 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 `````` normalize=True): if context == 'gpu': mx_context = mx.gpu() elif context == 'cpu': mx_context = mx.cpu() else: logging.error("Context argument is '" + context + "'. Only 'cpu' and 'gpu are valid arguments'.") if 'weight_decay' in optimizer_params: optimizer_params['wd'] = optimizer_params['weight_decay'] del optimizer_params['weight_decay'] if 'learning_rate_decay' in optimizer_params: min_learning_rate = 1e-08 if 'learning_rate_minimum' in optimizer_params: min_learning_rate = optimizer_params['learning_rate_minimum'] del optimizer_params['learning_rate_minimum'] optimizer_params['lr_scheduler'] = mx.lr_scheduler.FactorScheduler( `````` Sebastian N. committed Nov 11, 2019 209 210 211 `````` optimizer_params['step_size'], factor=optimizer_params['learning_rate_decay'], stop_factor_lr=min_learning_rate) `````` Nicola Gatto committed Apr 25, 2019 212 213 214 `````` del optimizer_params['step_size'] del optimizer_params['learning_rate_decay'] `````` Sebastian N. committed Nov 11, 2019 215 216 `````` train_batch_size = batch_size test_batch_size = batch_size `````` Nicola Gatto committed Apr 25, 2019 217 `````` `````` Sebastian N. committed Nov 11, 2019 218 `````` train_iter, train_test_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_data(train_batch_size, test_batch_size) `````` Sebastian N. committed Jun 21, 2019 219 220 221 222 223 `````` if normalize: self._net_creator.construct(context=mx_context, data_mean=data_mean, data_std=data_std) else: self._net_creator.construct(context=mx_context) `````` Nicola Gatto committed Apr 25, 2019 224 225 226 227 228 229 230 231 `````` begin_epoch = 0 if load_checkpoint: begin_epoch = self._net_creator.load(mx_context) else: if os.path.isdir(self._net_creator._model_dir_): shutil.rmtree(self._net_creator._model_dir_) `````` Sebastian N. committed Jun 21, 2019 232 `````` self._networks = self._net_creator.networks `````` Nicola Gatto committed Apr 25, 2019 233 234 235 236 237 238 239 `````` try: os.makedirs(self._net_creator._model_dir_) except OSError: if not os.path.isdir(self._net_creator._model_dir_): raise `````` Sebastian N. committed Nov 11, 2019 240 `````` trainers = [mx.gluon.Trainer(network.collect_params(), optimizer, optimizer_params) for network in self._networks.values() if len(network.collect_params().values()) != 0] `````` Nicola Gatto committed Apr 25, 2019 241 `````` `````` Sebastian N. committed Jul 09, 2019 242 243 `````` margin = loss_params['margin'] if 'margin' in loss_params else 1.0 sparseLabel = loss_params['sparse_label'] if 'sparse_label' in loss_params else True `````` Sebastian N. committed Nov 22, 2019 244 `````` ignore_indices = [loss_params['ignore_indices']] if 'ignore_indices' in loss_params else [] `````` Sebastian N. committed Jul 09, 2019 245 246 `````` if loss == 'softmax_cross_entropy': fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False `````` Sebastian N. committed Nov 22, 2019 247 `````` loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel) `````` Christian Fuß committed Nov 26, 2019 248 `````` elif loss == 'softmax_cross_entropy_ignore_indices': `````` Sebastian N. committed Nov 22, 2019 249 `````` fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False `````` Sebastian N. committed Nov 11, 2019 250 `````` loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel) `````` Sebastian N. committed Jul 09, 2019 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 `````` elif loss == 'sigmoid_binary_cross_entropy': loss_function = mx.gluon.loss.SigmoidBinaryCrossEntropyLoss() elif loss == 'cross_entropy': loss_function = CrossEntropyLoss(sparse_label=sparseLabel) elif loss == 'l2': loss_function = mx.gluon.loss.L2Loss() elif loss == 'l1': loss_function = mx.gluon.loss.L2Loss() elif loss == 'huber': rho = loss_params['rho'] if 'rho' in loss_params else 1 loss_function = mx.gluon.loss.HuberLoss(rho=rho) elif loss == 'hinge': loss_function = mx.gluon.loss.HingeLoss(margin=margin) elif loss == 'squared_hinge': loss_function = mx.gluon.loss.SquaredHingeLoss(margin=margin) elif loss == 'logistic': labelFormat = loss_params['label_format'] if 'label_format' in loss_params else 'signed' loss_function = mx.gluon.loss.LogisticLoss(label_format=labelFormat) elif loss == 'kullback_leibler': fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else True loss_function = mx.gluon.loss.KLDivLoss(from_logits=fromLogits) elif loss == 'log_cosh': loss_function = LogCoshLoss() else: logging.error("Invalid loss parameter.") `````` Nicola Gatto committed Apr 25, 2019 276 277 278 279 280 281 282 283 `````` speed_period = 50 tic = None for epoch in range(begin_epoch, begin_epoch + num_epoch): train_iter.reset() for batch_i, batch in enumerate(train_iter): with autograd.record(): `````` Sebastian N. committed Nov 11, 2019 284 285 286 287 288 289 290 `````` labels = [batch.label[i].as_in_context(mx_context) for i in range(1)] image_ = batch.data[0].as_in_context(mx_context) predictions_ = mx.nd.zeros((train_batch_size, 10,), ctx=mx_context) `````` Sebastian N. committed Nov 22, 2019 291 292 `````` nd.waitall() `````` Sebastian N. committed Nov 11, 2019 293 `````` lossList = [] `````` Sebastian N. committed Aug 12, 2019 294 295 `````` predictions_ = self._networks[0](image_) `````` Sebastian Nickels committed Jun 06, 2019 296 `````` `````` Sebastian N. committed Nov 11, 2019 297 `````` lossList.append(loss_function(predictions_, labels[0])) `````` Christian Fuß committed Aug 28, 2019 298 `````` `````` Sebastian N. committed Nov 11, 2019 299 300 301 `````` loss = 0 for element in lossList: loss = loss + element `````` Nicola Gatto committed Apr 25, 2019 302 303 `````` loss.backward() `````` Sebastian N. committed Jun 21, 2019 304 305 306 `````` for trainer in trainers: trainer.step(batch_size) `````` Nicola Gatto committed Apr 25, 2019 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 `````` if tic is None: tic = time.time() else: if batch_i % speed_period == 0: try: speed = speed_period * batch_size / (time.time() - tic) except ZeroDivisionError: speed = float("inf") logging.info("Epoch[%d] Batch[%d] Speed: %.2f samples/sec" % (epoch, batch_i, speed)) tic = time.time() tic = None `````` Sebastian N. committed Nov 11, 2019 323 324 325 `````` train_test_iter.reset() metric = mx.metric.create(eval_metric, **eval_metric_params) for batch_i, batch in enumerate(train_test_iter): `````` Christian Fuß committed Nov 26, 2019 326 `````` if True: `````` Sebastian N. committed Nov 11, 2019 327 `````` labels = [batch.label[i].as_in_context(mx_context) for i in range(1)] `````` Sebastian Nickels committed Jun 06, 2019 328 `````` `````` Sebastian N. committed Nov 11, 2019 329 330 331 `````` image_ = batch.data[0].as_in_context(mx_context) predictions_ = mx.nd.zeros((test_batch_size, 10,), ctx=mx_context) `````` Sebastian Nickels committed Jun 06, 2019 332 `````` `````` Sebastian N. committed Aug 12, 2019 333 `````` `````` Sebastian N. committed Nov 22, 2019 334 335 `````` nd.waitall() `````` Sebastian N. committed Nov 11, 2019 336 337 `````` outputs = [] attentionList=[] `````` Sebastian N. committed Aug 12, 2019 338 `````` predictions_ = self._networks[0](image_) `````` Sebastian Nickels committed May 27, 2019 339 `````` `````` Sebastian N. committed Nov 11, 2019 340 341 342 343 344 345 `````` outputs.append(predictions_) if save_attention_image == "True": import matplotlib.pyplot as plt logging.getLogger('matplotlib').setLevel(logging.ERROR) `````` Christian Fuß committed Aug 28, 2019 346 `````` `````` Sebastian N. committed Nov 11, 2019 347 `````` plt.clf() `````` Sebastian N. committed Nov 22, 2019 348 `````` fig = plt.figure(figsize=(15,15)) `````` Sebastian N. committed Nov 11, 2019 349 `````` max_length = len(labels)-1 `````` Christian Fuß committed Aug 28, 2019 350 `````` `````` Sebastian N. committed Nov 11, 2019 351 352 353 `````` if(os.path.isfile('src/test/resources/training_data/Show_attend_tell/dict.pkl')): with open('src/test/resources/training_data/Show_attend_tell/dict.pkl', 'rb') as f: dict = pickle.load(f) `````` Christian Fuß committed Aug 28, 2019 354 `````` `````` Sebastian N. committed Nov 22, 2019 355 356 357 358 `````` ax = fig.add_subplot(max_length//3, max_length//4, 1) ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0)) `````` Sebastian N. committed Nov 11, 2019 359 360 361 362 363 `````` for l in range(max_length): attention = attentionList[l] attention = mx.nd.slice_axis(attention, axis=0, begin=0, end=1) attention = mx.nd.squeeze(attention) attention_resized = np.resize(attention.asnumpy(), (8, 8)) `````` Sebastian N. committed Nov 22, 2019 364 `````` ax = fig.add_subplot(max_length//3, max_length//4, l+2) `````` Christian Fuß committed Nov 26, 2019 365 366 367 368 369 370 `````` if int(labels[l+1][0].asscalar()) > len(dict): ax.set_title("") img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0)) ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent()) break elif dict[int(labels[l+1][0].asscalar())] == "": `````` Sebastian N. committed Nov 22, 2019 371 372 373 374 375 376 377 378 `````` ax.set_title(".") img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0)) ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent()) break else: ax.set_title(dict[int(labels[l+1][0].asscalar())]) img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0)) ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent()) `````` Sebastian N. committed Nov 11, 2019 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 `````` plt.tight_layout() target_dir = 'target/attention_images' if not os.path.exists(target_dir): os.makedirs(target_dir) plt.savefig(target_dir + '/attention_train.png') plt.close() predictions = [] for output_name in outputs: if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1: predictions.append(mx.nd.argmax(output_name, axis=1)) else: predictions.append(output_name) `````` Sebastian Nickels committed Jun 06, 2019 394 395 `````` metric.update(preds=predictions, labels=labels) `````` Nicola Gatto committed Apr 25, 2019 396 397 398 `````` train_metric_score = metric.get()[1] test_iter.reset() `````` Sebastian N. committed Nov 11, 2019 399 `````` metric = mx.metric.create(eval_metric, **eval_metric_params) `````` Nicola Gatto committed Apr 25, 2019 400 `````` for batch_i, batch in enumerate(test_iter): `````` Christian Fuß committed Nov 26, 2019 401 `````` if True: `````` Sebastian N. committed Nov 11, 2019 402 `````` labels = [batch.label[i].as_in_context(mx_context) for i in range(1)] `````` Sebastian Nickels committed Jun 06, 2019 403 `````` `````` Sebastian N. committed Nov 11, 2019 404 405 406 `````` image_ = batch.data[0].as_in_context(mx_context) predictions_ = mx.nd.zeros((test_batch_size, 10,), ctx=mx_context) `````` Sebastian Nickels committed Jun 06, 2019 407 `````` `````` Sebastian N. committed Aug 12, 2019 408 `````` `````` Sebastian N. committed Nov 22, 2019 409 410 `````` nd.waitall() `````` Sebastian N. committed Nov 11, 2019 411 412 `````` outputs = [] attentionList=[] `````` Sebastian N. committed Aug 12, 2019 413 `````` predictions_ = self._networks[0](image_) `````` Sebastian N. committed Jul 03, 2019 414 `````` `````` Sebastian N. committed Nov 11, 2019 415 416 417 418 419 `````` outputs.append(predictions_) if save_attention_image == "True": plt.clf() `````` Sebastian N. committed Nov 22, 2019 420 `````` fig = plt.figure(figsize=(15,15)) `````` Sebastian N. committed Nov 11, 2019 421 `````` max_length = len(labels)-1 `````` Christian Fuß committed Aug 28, 2019 422 `````` `````` Sebastian N. committed Nov 22, 2019 423 424 425 426 `````` ax = fig.add_subplot(max_length//3, max_length//4, 1) ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0)) `````` Sebastian N. committed Nov 11, 2019 427 428 429 430 431 `````` for l in range(max_length): attention = attentionList[l] attention = mx.nd.slice_axis(attention, axis=0, begin=0, end=1) attention = mx.nd.squeeze(attention) attention_resized = np.resize(attention.asnumpy(), (8, 8)) `````` Sebastian N. committed Nov 22, 2019 432 `````` ax = fig.add_subplot(max_length//3, max_length//4, l+2) `````` Christian Fuß committed Nov 26, 2019 433 434 435 436 437 438 `````` if int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar()) > len(dict): ax.set_title("") img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0)) ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent()) break elif dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())] == "": `````` Sebastian N. committed Nov 22, 2019 439 440 441 442 443 `````` ax.set_title(".") img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0)) ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent()) break else: `````` Christian Fuß committed Nov 26, 2019 444 `````` ax.set_title(dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())]) `````` Sebastian N. committed Nov 22, 2019 445 446 `````` img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0)) ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent()) `````` Sebastian N. committed Nov 11, 2019 447 448 449 450 451 452 453 454 455 456 457 458 459 `````` plt.tight_layout() plt.savefig(target_dir + '/attention_test.png') plt.close() predictions = [] for output_name in outputs: if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1: predictions.append(mx.nd.argmax(output_name, axis=1)) #ArgMax already applied else: predictions.append(output_name) `````` Sebastian Nickels committed May 27, 2019 460 `````` `````` Sebastian Nickels committed Jun 06, 2019 461 `````` metric.update(preds=predictions, labels=labels) `````` Nicola Gatto committed Apr 25, 2019 462 463 464 465 `````` test_metric_score = metric.get()[1] logging.info("Epoch[%d] Train: %f, Test: %f" % (epoch, train_metric_score, test_metric_score)) `````` Sebastian N. committed Nov 11, 2019 466 `````` `````` Nicola Gatto committed Apr 25, 2019 467 `````` if (epoch - begin_epoch) % checkpoint_period == 0: `````` Sebastian N. committed Jun 21, 2019 468 469 `````` for i, network in self._networks.items(): network.save_parameters(self.parameter_path(i) + '-' + str(epoch).zfill(4) + '.params') `````` Nicola Gatto committed Apr 25, 2019 470 `````` `````` Sebastian N. committed Jun 21, 2019 471 472 473 `````` for i, network in self._networks.items(): network.save_parameters(self.parameter_path(i) + '-' + str(num_epoch + begin_epoch).zfill(4) + '.params') network.export(self.parameter_path(i) + '_newest', epoch=0) `````` Nicola Gatto committed Apr 25, 2019 474 `````` `````` Sebastian N. committed Jun 21, 2019 475 `````` def parameter_path(self, index): `````` 476 `` return self._net_creator._model_dir_ + self._net_creator._model_prefix_ + '_' + str(index)``