pdfcoffee

Recommendations

Info

[ 297 ]Chapter 8The logic follows a predictable pattern. We convert the sequence of characters inour prefix_string into a sequence of integers, then expand_dims to add a batchdimension so the input can be passed into our model. We then reset the state ofthe model. This is needed because our model is stateful, and we don't want thehidden state for the first timestep in our prediction run to be carried over from theone computed during training. We then run the input through our model and getback a prediction. This is the vector of shape (90,) representing the probabilities ofeach character in the vocabulary appearing at the next time step. We then reshapethe prediction by removing the batch dimension and dividing by the temperature,then randomly sample from the vector. We then set our prediction as the input tothe next time step. We repeat this for the number of characters we need to generate,converting each prediction back to character form and accumulating in a list, andreturning the list at the end of the loop:def generate_text(model, prefix_string, char2idx, idx2char,num_chars_to_generate=1000, temperature=1.0):input = [char2idx[s] for s in prefix_string]input = tf.expand_dims(input, 0)text_generated = []model.reset_states()for i in range(num_chars_to_generate):preds = model(input)preds = tf.squeeze(preds, 0) / temperature# predict char returned by modelpred_id = tf.random.categorical(preds, num_samples=1)[-1, 0].numpy()text_generated.append(idx2char[pred_id])# pass the prediction as the next input to the modelinput = tf.expand_dims([pred_id], 0)return prefix_string + "".join(text_generated)Finally, we are ready to run our training and evaluation loop. As mentioned earlier,we will train our network for 50 epochs, and at every 10 epoch intervals, we will tryto generate some text with the model trained so far. Our prefix at each stage is thestring "Alice." Notice that in order to accommodate a single string prefix, we savethe weights after every 10 epochs and build a separate generative model with theseweights but with an input shape with a batch size of 1. Here is the code to do this:num_epochs = 50for i in range(num_epochs // 10):model.fit(dataset.repeat(),epochs=10,steps_per_epoch=steps_per_epoch
Recurrent Neural Networks# callbacks=[checkpoint_callback, tensorboard_callback])checkpoint_file = os.path.join(CHECKPOINT_DIR, "model_epoch_{:d}".format(i+1))model.save_weights(checkpoint_file)# create generative model using the trained model so fargen_model = CharGenModel(vocab_size, seq_length, embedding_dim,rnn_output_dim)gen_model.load_weights(checkpoint_file)gen_model.build(input_shape=(1, seq_length))print("after epoch: {:d}".format(i+1)*10)print(generate_text(gen_model, "Alice ", char2idx, idx2char))print("---")The output after the very first epoch of training contains words that are completelyundecipherable:Alice nIPJtce otaishein r. henipt il nn tu t hen mlPde hc efahdtioDDeteeybeaewI teu"t e9B ce nd ageiw eai rdoCr ohrSI eyPmtte:vh ndte taudhor0-gu s5'ria,tr gn inoo luwomg Omke dee sdoohdnggtdhiAoyaphotd t- kta e c t- taLurtn hiisd tl'lpei od y' tpacoe dnlhroG mGhod ut hlhoy .i, sseodli., ekngnhe idlue'aa' ndti-rla nt d'eiAieradwe ai'otteniAidee hy-ouasq"plhgs tuutandhptiw oohe.Rastnint:e,oodwsir"omGoeuall1*g taetphhitoge ds wr li,raa, h$jeuorsu h cidmdg'tku..n,HnbMAsn nsaathaa,' ase woe ehf re ig"hTr ddloese eod,aed toe rh k.nalf bte seyr udG n,ug lei hn icuimty"onw Qee ivtsae zdrye g eut rthrer nsd,Zhqehd' sr caseruhel are fd yse e kgeiiday odW-ldmkhNw endeM[harlhroah Wydrygslsh EnilDnt e "lue "en wHeslhglidrth"ylds rln n iiato taue flitlnnyg ittlno re 'el yOkao itswnadoli'.dnd Akib-ehn hftwinh yd ee tosetftonne.;egren t wf, ota nfsr, t&he desnre e" oo fnrvnse aid na tesd isioneetIf ·itrn tttpakihc s nih'bheY ilenf yoh etdrwdplloU ooaeedo,,dresnno'ofh o epst. lahehrwHowever, after about 30 epochs of training, we begin to see words that look familiar:Alice Red Queen. He best I had defores it,' glily do flose time it makesthe talking of find a hand mansed in she loweven to the rund not brightprough: the and she a chill be the sand using that whever sullusn--thedear of asker as 'IS now-- Chich the hood." "Oh!"' '_I'm num about--again was wele after a WAG LoANDE BITTER OF HSE!0 UUL EXMENN 1*.t,this wouldn't teese to Dumark THEVER Project Gutenberg-tmy of himidout flowal woulld: 'Nis song, Eftrin in pully be besoniokinote. "Com,contimemustion--of could you knowfum to hard, she can't the with talkingto alfoeys distrint, for spacemark!' 'You gake to be would prescladledingreadieve other togrore what it mughturied ford of it was sen!" You squs,_It I hap: But it was minute to the Kind she notion and teem what?" said[ 298 ]
Page 2 and 3:
Deep Learning withTensorFlow 2 and
Page 4 and 5:
packt.comSubscribe to our online di
Page 6 and 7:
I want to thank my kids, Aurora, Le
Page 8 and 9:
Sujit Pal is a Technology Research
Page 10 and 11:
Table of ContentsPrefacexiChapter 1
Page 12 and 13:
[ iii ]Table of ContentsConverting
Page 14 and 15:
Table of ContentsSo what is the pro
Page 16 and 17:
[ vii ]Table of ContentsChapter 10:
Page 18 and 19:
Table of ContentsPretrained models
Page 20 and 21:
PrefaceDeep Learning with TensorFlo
Page 22 and 23:
• Supervised learning, in which t
Page 24 and 25:
PrefaceThe complexity of deep learn
Page 26 and 27:
PrefaceFigure 5: Adoption of deep l
Page 28 and 29:
Chapter 1, Neural Network Foundatio
Page 30 and 31:
PrefaceChapter 13, TensorFlow for M
Page 32 and 33:
ConventionsThere are a number of te
Page 34:
PrefaceReferences1. Deep Learning w
Page 37 and 38:
Neural Network Foundations with Ten
Page 39 and 40:
Page 41 and 42:
Page 43 and 44:
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Page 51 and 52:
Page 53 and 54:
Page 55 and 56:
Page 57 and 58:
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 86 and 87:
TensorFlow 1.x and 2.xThe intent of
Page 88 and 89:
An example to start withWe'll consi
Page 90 and 91:
Chapter 23. Placeholders: Placehold
Page 92 and 93:
• To create random values from a
Page 94 and 95:
To know the value, we need to creat
Page 96 and 97:
Chapter 2Both PyTorch and TensorFlo
Page 98 and 99:
Chapter 2state = [tf.zeros([100, 10
Page 100 and 101:
Chapter 2For now, there's no need t
Page 102 and 103:
Chapter 2Let's see an example of a
Page 104 and 105:
Chapter 2If you want to save a mode
Page 106 and 107:
Chapter 2supervised=True)train_data
Page 108 and 109:
Chapter 2There, tf.feature_column.n
Page 110 and 111:
Chapter 2print (dz_dx)print (dy_dx)
Page 112 and 113:
Chapter 2In our toy example we use
Page 114 and 115:
Chapter 2For multi-machine training
Page 116 and 117:
Chapter 25. Use tf.layers modules t
Page 118 and 119:
Chapter 2Keras or tf.keras?Another
Page 120:
• tf.data can be used to load mod
Page 123 and 124:
RegressionLet us imagine a simpler
Page 125 and 126:
RegressionTake a look at the last t
Page 127 and 128:
Regression3. Now, we calculate the
Page 129 and 130:
RegressionIn the next section we wi
Page 131 and 132:
Regression2. Now, we define the fea
Page 133 and 134:
Regression2. Download the dataset:(
Page 135 and 136:
RegressionThe following is the Tens
Page 137 and 138:
RegressionIn regression the aim is
Page 139 and 140:
RegressionThe Estimator outputs the
Page 141 and 142:
RegressionThe following is the grap
Page 143 and 144:
RegressionReferencesHere are some g
Page 145 and 146:
Convolutional Neural NetworksIn thi
Page 147 and 148:
Convolutional Neural NetworksIn thi
Page 149 and 150:
Convolutional Neural NetworksIn oth
Page 151 and 152:
Convolutional Neural NetworksThen w
Page 153 and 154:
Convolutional Neural NetworksHoweve
Page 155 and 156:
Convolutional Neural NetworksPlotti
Page 157 and 158:
Convolutional Neural NetworksIn gen
Page 159 and 160:
Convolutional Neural NetworksOur ne
Page 161 and 162:
Convolutional Neural NetworksThese
Page 163 and 164:
Convolutional Neural NetworksSo, we
Page 165 and 166:
Convolutional Neural NetworksEach i
Page 167 and 168:
Convolutional Neural NetworksVery d
Page 169 and 170:
Convolutional Neural NetworksRecogn
Page 171 and 172:
Convolutional Neural NetworksIf we
Page 173 and 174:
Convolutional Neural NetworksRefere
Page 175 and 176:
Advanced Convolutional Neural Netwo
Page 177 and 178:
Page 179 and 180:
Page 181 and 182:
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
Page 191 and 192:
Page 193 and 194:
Page 195 and 196:
Page 197 and 198:
Page 199 and 200:
Page 201 and 202:
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
Page 209 and 210:
Page 211 and 212:
Page 213 and 214:
Page 215 and 216:
Page 217 and 218:
Page 219 and 220:
Page 221 and 222:
Page 223 and 224:
Page 226 and 227:
GenerativeAdversarial NetworksIn th
Page 228 and 229:
[ 193 ]Chapter 6Eventually, we reac
Page 230 and 231:
[ 195 ]Chapter 6Next, we combine th
Page 232 and 233:
Chapter 6And handwritten digits gen
Page 234 and 235:
Chapter 6Figure 1: Visualizing the
Page 236 and 237:
Chapter 6The resultant generator mo
Page 238 and 239:
Chapter 6Figure 4: A summary of res
Page 240 and 241:
Chapter 6def train(self, epochs, ba
Page 242 and 243:
Chapter 6The preceding images were
Page 244 and 245:
Chapter 6Another interesting paper
Page 246 and 247:
Chapter 6To elaborate, let us say t
Page 248 and 249:
Chapter 6Figure 7: The architecture
Page 250 and 251:
Chapter 6Figure 11: Illegible initi
Page 252 and 253:
Chapter 6Bedrooms: Generated bedroo
Page 254 and 255:
Chapter 6The images need to be norm
Page 256 and 257:
Chapter 6initializer = tf.random_no
Page 258 and 259:
Cool, right? Now we can define the
Page 260 and 261:
Chapter 6d_loss = (dA_loss + dB_los
Page 262 and 263:
Chapter 6generator_AB.save_weights(
Page 264:
6. Ledig, Christian, et al. Photo-R
Page 267 and 268:
Word EmbeddingsDeep learning models
Page 269 and 270:
Word EmbeddingsFor example, "crucia
Page 271 and 272:
Word EmbeddingsAssuming a window si
Page 273 and 274:
Word EmbeddingsGloVeThe Global vect
Page 275 and 276:
Word Embeddingsgensim is an open so
Page 277 and 278:
Word Embeddingsgensim also provides
Page 279 and 280:
Word EmbeddingsSpecifically, we wil
Page 281 and 282: Word EmbeddingsWe will also convert
Page 283 and 284: Word EmbeddingsE = np.zeros((vocab_
Page 285 and 286: Word Embeddingsx = self.embedding(x
Page 287 and 288: Word EmbeddingsThe change in valida
Page 289 and 290: Word EmbeddingsThe dataset is a 114
Page 291 and 292: Word Embeddingsprint("random walks
Page 293 and 294: Word Embeddingssize=128, # size of
Page 295 and 296: Word EmbeddingsfastText computes em
Page 297 and 298: Word EmbeddingsIn the future, once
Page 299 and 300: Word EmbeddingsA much earlier relat
Page 301 and 302: Word EmbeddingsOnce you have the fi
Page 303 and 304: Word EmbeddingsThis will create the
Page 305 and 306: Word EmbeddingsClassifying with BER
Page 307 and 308: Word Embeddings2. Each Transformer
Page 309 and 310: Word EmbeddingsOnce trained, we sav
Page 311 and 312: Word Embeddings4. Pennington, J., S
Page 313 and 314: Word Embeddings34. Google Research,
Page 315 and 316: Recurrent Neural NetworksWe will th
Page 317 and 318: Recurrent Neural NetworksFor notati
Page 319 and 320: Recurrent Neural NetworksThis probl
Page 321 and 322: Recurrent Neural NetworksThe line a
Page 323 and 324: Recurrent Neural NetworksGated recu
Page 325 and 326: Recurrent Neural NetworksThis probl
Page 327 and 328: Recurrent Neural NetworksThe topolo
Page 329 and 330: Recurrent Neural Networkstexts = do
Page 331: Recurrent Neural Networksdef call(s
Page 335 and 336: Recurrent Neural NetworksExample
Page 337 and 338: Recurrent Neural NetworksAs can be
Page 339 and 340: Recurrent Neural Networksdata_dir =
Page 341 and 342: Recurrent Neural NetworksWe can als
Page 343 and 344: Recurrent Neural NetworksIn order t
Page 345 and 346: Recurrent Neural Networkssource_voc
Page 347 and 348: Recurrent Neural NetworksFinally, w
Page 349 and 350: Recurrent Neural Networks38 - val_l
Page 351 and 352: Recurrent Neural NetworksIf you wou
Page 353 and 354: Recurrent Neural NetworksExample
Page 355 and 356: Recurrent Neural NetworksNext we ha
Page 357 and 358: Recurrent Neural Networksself.embed
Page 359 and 360: Recurrent Neural NetworksThis is a
Page 361 and 362: Recurrent Neural Networksreturn np.
Page 363 and 364: Recurrent Neural NetworksAttention
Page 365 and 366: Recurrent Neural NetworksFinally, V
Page 367 and 368: Recurrent Neural Networks# query.sh
Page 369 and 370: Recurrent Neural Networksself.atten
Page 371 and 372: Recurrent Neural Networks30 try to
Page 373 and 374: Recurrent Neural Networks3. Because
Page 375 and 376: Recurrent Neural NetworksSummaryIn
Page 377 and 378: Recurrent Neural Networks18. Shi, X
Page 380 and 381: AutoencodersAutoencoders are feed-f
Page 382 and 383:
Depending upon the actual dimension
Page 384 and 385:
• __init__(): Here, you define al
Page 386 and 387:
Chapter 9And then we reshape the te
Page 388 and 389:
Chapter 9plt.imshow(x_test[index].r
Page 390 and 391:
Chapter 9Keeping the rest of the co
Page 392 and 393:
noise = np.random.normal(loc=0.5, s
Page 394 and 395:
Chapter 9x_train,validation_data=(x
Page 396 and 397:
Chapter 9import matplotlib.pyplot a
Page 398 and 399:
Chapter 9self.conv4 = Conv2D(1, 3,
Page 400 and 401:
Chapter 9You can see that the image
Page 402 and 403:
[ 367 ]Chapter 9Let us use the prec
Page 404 and 405:
Chapter 9Our autoencoder model take
Page 406 and 407:
We train the autoencoder for 20 epo
Page 408 and 409:
Chapter 90.97905576229095460.989323
Page 410 and 411:
Unsupervised LearningThis chapter d
Page 412 and 413:
Chapter 10Next we load the MNIST da
Page 414 and 415:
Chapter 10TensorFlow Embedding APIT
Page 416 and 417:
3. Recompute the centroids using cu
Page 418 and 419:
Chapter 10Figure 4: Plot of the fin
Page 420 and 421:
Chapter 10In SOMs, neurons are usua
Page 422 and 423:
[ 387 ]Chapter 10Colour mapping usi
Page 424 and 425:
Chapter 10# Calculating Neighbourho
Page 426 and 427:
We will also need to normalize the
Page 428 and 429:
Chapter 10ρρ(vv oo |h oo ) = σσ
Page 430 and 431:
# Generate the sample probabilityde
Page 432 and 433:
Chapter 10And the reconstructed ima
Page 434 and 435:
Chapter 10inpX = rbm.rbm_output(inp
Page 436 and 437:
Chapter 10(60000, 28, 28) (60000,)(
Page 438 and 439:
Chapter 10Figure 11: Summary of the
Page 440 and 441:
Chapter 10This chapter, along with
Page 442 and 443:
Reinforcement LearningThis chapter
Page 444 and 445:
Chapter 11And unlike unsupervised l
Page 446 and 447:
Chapter 11Normally, the value is de
Page 448 and 449:
Chapter 11• The next question tha
Page 450 and 451:
Chapter 11This neural network takes
Page 452 and 453:
Chapter 11The MuJoCo environment re
Page 454 and 455:
Chapter 11We will first import the
Page 456 and 457:
Chapter 11The αα is the learning
Page 458 and 459:
Chapter 11We set up the global valu
Page 460 and 461:
Chapter 11else:return np.argmax(sel
Page 462 and 463:
Chapter 11DQN to play a game of Ata
Page 464 and 465:
Chapter 11self.model.add( Conv2D(64
Page 466 and 467:
Chapter 11Here the action A was sel
Page 468 and 469:
Chapter 11Image source: https://arx
Page 470 and 471:
Chapter 11A neural network is used
Page 472:
Chapter 1111. Details regarding ins
Page 475 and 476:
TensorFlow and Cloud• Scalability
Page 477 and 478:
TensorFlow and Cloud• Azure DevOp
Page 479 and 480:
TensorFlow and Cloud• Lambda: The
Page 481 and 482:
TensorFlow and Cloud• Deep Learni
Page 483 and 484:
TensorFlow and CloudEC2 on AmazonTo
Page 485 and 486:
TensorFlow and CloudCompute Instanc
Page 487 and 488:
TensorFlow and CloudYou just share
Page 489 and 490:
TensorFlow and CloudIn case you req
Page 491 and 492:
TensorFlow and CloudIt starts with
Page 493 and 494:
TensorFlow and CloudTFX librariesTF
Page 495 and 496:
TensorFlow and CloudReferences1. To
Page 497 and 498:
TensorFlow for Mobile and IoT and T
Page 499 and 500:
Page 501 and 502:
Page 503 and 504:
Page 505 and 506:
Page 507 and 508:
Page 509 and 510:
Page 511 and 512:
Page 513 and 514:
Page 515 and 516:
Page 517 and 518:
Page 519 and 520:
Page 521 and 522:
Page 523 and 524:
Page 525 and 526:
Page 527 and 528:
An introduction to AutoMLThat is pr
Page 529 and 530:
An introduction to AutoMLFeature co
Page 531 and 532:
An introduction to AutoMLThis Effic
Page 533 and 534:
An introduction to AutoMLGoogle Clo
Page 535 and 536:
An introduction to AutoMLThen, we c
Page 537 and 538:
An introduction to AutoMLOnce the d
Page 539 and 540:
An introduction to AutoMLIf your mo
Page 541 and 542:
An introduction to AutoMLClicking o
Page 543 and 544:
An introduction to AutoMLFigure 16:
Page 545 and 546:
An introduction to AutoMLYou can al
Page 547 and 548:
An introduction to AutoMLPut simply
Page 549 and 550:
An introduction to AutoMLLet's star
Page 551 and 552:
An introduction to AutoMLThe token
Page 553 and 554:
An introduction to AutoMLThis will
Page 555 and 556:
Page 557 and 558:
An introduction to AutoMLAt the end
Page 559 and 560:
An introduction to AutoMLUsing Clou
Page 561 and 562:
An introduction to AutoMLOnce the d
Page 563 and 564:
An introduction to AutoMLAt the end
Page 565 and 566:
An introduction to AutoMLAs the nex
Page 567 and 568:
An introduction to AutoMLOnce the m
Page 569 and 570:
Page 571 and 572:
An introduction to AutoMLOnce the m
Page 573 and 574:
An introduction to AutoMLWe can als
Page 575 and 576:
An introduction to AutoMLThe most e
Page 577 and 578:
An introduction to AutoMLReferences
Page 579 and 580:
The Math Behind Deep LearningSome m
Page 581 and 582:
The Math Behind Deep LearningSuppos
Page 583 and 584:
The Math Behind Deep LearningNote t
Page 585 and 586:
The Math Behind Deep LearningTheref
Page 587 and 588:
The Math Behind Deep LearningThe ea
Page 589 and 590:
The Math Behind Deep LearningThe re
Page 591 and 592:
The Math Behind Deep LearningCase 2
Page 593 and 594:
The Math Behind Deep LearningIn thi
Page 595 and 596:
The Math Behind Deep LearningHere,
Page 597 and 598:
The Math Behind Deep Learning(Note
Page 599 and 600:
The Math Behind Deep LearningIn man
Page 601 and 602:
The Math Behind Deep LearningIf we
Page 603 and 604:
The Math Behind Deep LearningChapte
Page 605 and 606:
The Math Behind Deep LearningThis c
Page 607 and 608:
Tensor Processing UnitMany people b
Page 609 and 610:
Tensor Processing UnitThe sequentia
Page 611 and 612:
Tensor Processing UnitIf you want t
Page 613 and 614:
Tensor Processing UnitOn the other
Page 615 and 616:
Tensor Processing UnitHow to use TP
Page 617 and 618:
Tensor Processing UnitNote that ful
Page 619 and 620:
Tensor Processing UnitEpoch 10/1060
Page 621 and 622:
Tensor Processing UnitFigure 11: Go
Page 623 and 624:
Tensor Processing UnitThen the usag
Page 626 and 627:
Other Books YouMay EnjoyIf you enjo
Page 628 and 629:
Other Books You May EnjoyAI Crash C
Page 630:
Other Books You May EnjoyLeave a re
Page 633 and 634:
AutoML pipelinedata preparation 493
Page 635 and 636:
Deep Deterministic Policy Gradient(
Page 637 and 638:
Google cloud consolereference link
Page 639 and 640:
used, for building GAN 193-198MNIST
Page 641 and 642:
regularizersreference link 38reinfo
Page 643 and 644:
TensorFlow Lite 81TensorFlow Core r
Page 645:
Xxception networks 160, 162YYOLO ne
show all

pdfcoffee

Create successful ePaper yourself

Delete template?

Save as template?