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11,600 | listing deep learning for text and sequences inspect ing the dat of he jena weat her dat aset import os data_dir '/users/fchollet/downloads/jena_climatefname os path join(data_dir'jena_climate_ csv' open(fnamedata read( close(lines data split('\ 'header lines[ split(','lines lines[ :print(headerprint(len(lines)this out... |
11,601 | advanced use of recurrent neural networks listing plot ing he emperat ure imeseries from matplotlib import pyplot as plt temp float_data[: temperature (in degrees celsiusplt plot(range(len(temp))tempfigure temperat ure over the full emporal range of he dat aset (ochere is more narrow plot of the first days of temperatu... |
11,602 | deep learning for text and sequences on this plotyou can see daily periodicityespecially evident for the last days also note that this -day period must be coming from fairly cold winter month if you were trying to predict average temperature for the next month given few months of past datathe problem would be easydue t... |
11,603 | data--the original array of floating-point datawhich you normalized in listing lookback--how many timesteps back the input data should go delay--how many timesteps in the future the target should be min_index and max_index--indices in the data array that delimit which timesteps to draw from this is useful for keeping s... |
11,604 | deep learning for text and sequences train_gen generator(float_datalookback=lookbackdelay=delaymin_index= max_index= shuffle=truestep=stepbatch_size=batch_sizeval_gen generator(float_datalookback=lookbackdelay=delaymin_index= max_index= step=stepbatch_size=batch_sizetest_gen generator(float_datalookback=lookbackdelay=d... |
11,605 | here' the evaluation loop listing comput ing he common-sense baseline mae def evaluate_naive_method()batch_maes [for step in range(val_steps)samplestargets next(val_genpreds samples[:- mae np mean(np abs(preds targets)batch_maes append(maeprint(np mean(batch_maes)evaluate_naive_method(this yields an mae of because the ... |
11,606 | deep learning for text and sequences model compile(optimizer=rmsprop()loss='mae'history model fit_generator(train_gensteps_per_epoch= epochs= validation_data=val_genvalidation_steps=val_stepslet' display the loss curves for validation and training (see figure listing plot ing result import matplotlib pyplot as plt loss... |
11,607 | solution with space of complicated modelsthe simplewell-performing baseline may be unlearnableeven if it' technically part of the hypothesis space that is pretty significant limitation of machine learning in generalunless the learning algorithm is hardcoded to look for specific kind of simple modelparameter learning ca... |
11,608 | deep learning for text and sequences figure training and validation loss on he jena temperatureforecast ing task with gru the new validation mae of ~ (before you start significantly overfittingtranslates to mean absolute error of @ after denormalization that' solid gain on the initial error of @cbut you probably still ... |
11,609 | and recurrent_dropoutspecifying the dropout rate of the recurrent units let' add dropout and recurrent dropout to the gru layer and see how doing so impacts overfitting because networks being regularized with dropout always take longer to fully convergeyou'll train the network for twice as many epochs listing training ... |
11,610 | deep learning for text and sequences you're already taking basic steps to mitigate overfittingsuch as using dropoutas long as you aren' overfitting too badlyyou're likely under capacity increasing network capacity is typically done by increasing the number of units in the layers or adding more layers recurrent layer st... |
11,611 | figure training and validat ion loss on he jena temperat ureforecast ing ask wit st acked gru net work using bidirectional rnns the last technique introduced in this section is called bidirectional rnns bidirectional rnn is common rnn variant that can offer greater performance than regular rnn on certain tasks it' freq... |
11,612 | deep learning for text and sequences figure training and validation loss on the jena emperat ureforecasting task with gru rained on reversed sequences the reversed-order gru strongly underperforms even the common-sense baselineindicating that in this casechronological processing is important to the success of your appr... |
11,613 | history model fit(x_trainy_trainepochs= batch_size= validation_split= you get performance nearly identical to that of the chronological-order lstm remarkablyon such text datasetreversed-order processing works just as well as chronological processingconfirming the hypothesis thatalthough word order does matter in unders... |
11,614 | deep learning for text and sequences model compile(optimizer='rmsprop'loss='binary_crossentropy'metrics=['acc']history model fit(x_trainy_trainepochs= batch_size= validation_split= it performs slightly better than the regular lstm you tried in the previous sectionachieving over validation accuracy it also seems to over... |
11,615 | as alwaysdeep learning is more an art than science we can provide guidelines that suggest what is likely to work or not work on given problembutultimatelyevery problem is uniqueyou'll have to evaluate different strategies empirically there is currently no theory that will tell you in advance precisely what you should d... |
11,616 | deep learning for text and sequences market and machine learning some readers are bound to want to take the techniques we've introduced here and try them on the problem of forecasting the future price of securities on the stock market (or currency exchange ratesand so onmarkets have very different statistical character... |
11,617 | sequence processing with convnets sequence processing with convnets in you learned about convolutional neural networks (convnetsand how they perform particularly well on computer vision problemsdue to their ability to operate convolutionallyextracting features from local input patches and allowing for representation mo... |
11,618 | deep learning for text and sequences these words in any context in an input sequence character-level convnet is thus able to learn about word morphology pooling for sequence data you're already familiar with pooling operationssuch as average pooling and max poolingused in convnets to spatially downsample image tensors ... |
11,619 | this is the example convnet for the imdb dataset listing training and evaluat ing simple convnet on he imdb dat from keras models import sequential from keras import layers from keras optimizers import rmsprop model sequential(model add(layers embedding(max_features input_length=max_len)model add(layers conv ( activati... |
11,620 | deep learning for text and sequences figure training and validat ion accuracy on imdb wit simple convnet combining cnns and rnns to process long sequences because convnets process input patches independentlythey aren' sensitive to the order of the timesteps (beyond local scalethe size of the convolution windows)unlike ... |
11,621 | figure shows the training and validation maes figure training and validation loss on the jena temperature-forecast ing ask with simple convnet the validation mae stays in the syou can' even beat the common-sense baseline using the small convnet againthis is because the convnet looks for patterns anywhere in the input t... |
11,622 | deep learning for text and sequences look at data from longer ago (by increasing the lookback parameter of the data generatoror look at high-resolution timeseries (by decreasing the step parameter of the generatorheresomewhat arbitrarilyyou'll use step that' half as largeresulting in timeseries twice as longwhere the t... |
11,623 | history model fit_generator(train_gensteps_per_epoch= epochs= validation_data=val_genvalidation_steps=val_stepsfigure training and validat ion loss on he jena temperat ureforecast ing ask wit convnet followed by gru judging from the validation lossthis setup isn' as good as the regularized gru alonebut it' significantl... |
11,624 | deep learning for text and sequences summary in this you learned the following techniqueswhich are widely applicable to any dataset of sequence datafrom text to timeserieshow to tokenize text what word embeddings areand how to use them what recurrent networks areand how to use them how to stack rnn layers and use bidir... |
11,625 | best practices this covers the keras functional api using keras callbacks working with the tensorboard visualization tool important best practices for developing state-ofthe-art models this explores number of powerful tools that will bring you closer to being able to develop state-of-the-art models on difficult problem... |
11,626 | going beyond the sequential modelthe keras functional api advanced deep-learning best practices until nowall neural networks introduced in this book output have been implemented using the sequential model the sequential model makes the assumption that the layer network has exactly one input and exactly one outputand th... |
11,627 | similarlysome tasks need to predict multiple target attributes of input data given the text of novel or short storyyou might want to automatically classify it by genre (such as romance or thrillerbut also predict the approximate date it was written of courseyou could train two separate modelsone for the genre and one f... |
11,628 | advanced deep-learning best practices output concatenate conv strides= conv strides= conv strides= conv conv conv avgpool strides= conv input figure an incept ion modulea subgraph of layers wit several parallel convolut ional branches layer layer residual connection layer layer figure residual connectionreinjection of ... |
11,629 | going beyond the sequential modelthe keras functional api dense layers dense( activation='relu'output_tensor dense(input_tensora layer is function layer may be called on tensorand it returns tensor let' start with minimal example that shows side by side simple sequential model and its equivalent in the functional apifr... |
11,630 | advanced deep-learning best practices runtimeerrorgraph disconnectedcannot obtain value for tensor tensor("input_ : "shape=(? )dtype=float at layer "input_ this error tells youin essencethat keras couldn' reach input_ from the provided output tensor when it comes to compilingtrainingor evaluating such an instance of mo... |
11,631 | going beyond the sequential modelthe keras functional api following is an example of how you can build such model with the functional api you set up two independent branchesencoding the text input and the question input as representation vectorsthenconcatenate these vectorsand finallyadd softmax classifier on top of th... |
11,632 | advanced deep-learning best practices answers are onequestion np random randint( question_vocabulary_sizehot encodedsize=(num_samplesmax_length)not integers answers np random randint( size=(num_samplesanswer_vocabulary_size)model fit([textquestion]answersepochs= batch_size= model fit({'text'text'question'question}answe... |
11,633 | going beyond the sequential modelthe keras functional api age income gender dense dense dense convnet social media posts figure social media model with hree heads importantlytraining such model requires the ability to specify different loss functions for different heads of the networkfor instanceage prediction is scala... |
11,634 | advanced deep-learning best practices model compile(optimizer='rmsprop'loss={'age''mse''income''categorical_crossentropy''gender''binary_crossentropy'}loss_weights={'age' 'income' 'gender' }equivalent (possible only if you give names to the output layersmuch as in the case of multi-input modelsyou can pass numpy data t... |
11,635 | spatial features and channel-wise featureswhich is more efficient than learning them jointly more-complex versions of an inception module are also possibletypically involving pooling operationsdifferent spatial convolution sizes (for example instead of on some branches)and branches without spatial convolution (only con... |
11,636 | advanced deep-learning best practices every branch has the same stride value ( )which is necessary to keep all branch outputs the same size so you can concatenate them in this branchthe striding occurs in the spatial convolution layer from keras import layers branch_a layers conv ( activation='relu'strides= )(xbranch_b... |
11,637 | residual connection consists of making the output of an earlier layer available as input to later layereffectively creating shortcut in sequential network rather than being concatenated to the later activationthe earlier output is summed with the later activationwhich assumes that both activations are the same size if ... |
11,638 | advanced deep-learning best practices (continuedyou can grasp this concept with signal-processing analogyif you have an audioprocessing pipeline that consists of series of operationseach of which takes as input the output of the previous operationthen if one operation crops your signal to low-frequency range (for examp... |
11,639 | processing each input sentence ratheryou want to process both with single lstm layer the representations of this lstm layer (its weightsare learned based on both inputs simultaneously this is what we call siamese lstm model or shared lstm here' how to implement such model using layer sharing (layer reusein the keras fu... |
11,640 | advanced deep-learning best practices features from the left camera and the right camera before merging the two feeds such low-level processing can be shared across the two inputsthat isdone via layers that use the same weights and thus share the same representations here' how you' implement siamese vision model (share... |
11,641 | inspecting and monitoring deep-learning models using keras callbacks and tensorboard in this sectionwe'll review ways to gain greater access to and control over what goes on inside your model during training launching training run on large dataset for tens of epochs using model fit(or model fit_generator(can be bit lik... |
11,642 | advanced deep-learning best practices keras callbacks learningratescheduler keras callbacks reducelronplateau keras callbacks csvlogger let' review few of them to give you an idea of how to use themmodelcheckpointearlystoppingand reducelronplateau the odelcheckpoint and earlystopping callbacks you can use the earlystop... |
11,643 | callbacks_list monitors the model' validation loss keras callbacks reducelronplateaumonitor='val_lossfactor= divides the learning rate by when triggered patience= the callback is triggered after the validation loss has stopped improving for epochs model fit(xyepochs= batch_size= callbacks=callbacks_listvalidation_data=... |
11,644 | advanced deep-learning best practices validation_sample self validation_data[ ][ : activations self activations_model predict(validation_samplef open('activations_at_epoch_str(epochnpz'' 'np savez(factivationsf close(obtains the first input sample of the validation data saves arrays to disk this is all you need to know... |
11,645 | visually monitoring metrics during training visualizing your model architecture visualizing histograms of activations and gradients exploring embeddings in let' demonstrate these features on simple example you'll train convnet on the imdb sentiment-analysis task the model is similar to the one you saw in the last secti... |
11,646 | listing advanced deep-learning best practices training he model wit tensorboard callback callbacks log files will be written keras callbacks tensorboardat this location log_dir='my_log_dir'histogram_freq= records activation histograms every epoch embeddings_freq= records embedding data every epoch history model fit(x_t... |
11,647 | figure tensorboardact ivat ion histograms the embeddings tab gives you way to inspect the embedding locations and spatial relationships of the , words in the input vocabularyas learned by the initial embedding layer because the embedding space is -dimensionaltensorboard automatically reduces it to or using dimensionali... |
11,648 | figure advanced deep-learning best practices tensorboardinteractive word-embedding visualizat ion the graphs tab shows an interactive visualization of the graph of low-level tensorflow operations underlying your keras model (see figure as you can seethere' lot more going on than you would expect the model you just buil... |
11,649 | figure tensorboardtensorflow graph visualizat ion note that keras also provides anothercleaner way to plot models as graphs of layers rather than graphs of tensorflow operationsthe utility keras utils plot_model using it requires that you've installed the python pydot and pydot-ng libraries as well as the graphviz libr... |
11,650 | advanced deep-learning best practices figure model plot as graph of layersgenerated wit plot_model you also have the option of displaying shape information in the graph of layers this example visualizes model topology using plot_model and the show_shapes option (see figure )from keras utils import plot_model plot_model... |
11,651 | figure model plot with shape informat ion wrapping up keras callbacks provide simple way to monitor models during training and automatically take action based on the state of the model when you're using tensorflowtensorboard is great way to visualize model activity in your browser you can use it in keras models via the... |
11,652 | getting the most out of your models advanced deep-learning best practices trying out architectures blindly works well enough if you just need something that works okay in this sectionwe'll go beyond "works okayto "works great and wins machine-learning competitionsby offering you quick guide to set of must-know techniqu... |
11,653 | getting the most out of your models the batchnormalization layer is typically used after convolutional or densely connected layerconv_model add(layers conv ( activation='relu')conv_model add(layers batchnormalization()dense_model add(layers dense( activation='relu')dense_model add(layers batchnormalization()after conv ... |
11,654 | advanced deep-learning best practices conv (pointwise convconcatenate conv conv conv depthwise convolutionindependent spatial convs per channel conv figure dept hwise separable convolut iona dept hwise convolut ion followed by pointwise convolution split channels these advantages become especially important when you're... |
11,655 | convolutions and xception in my paper "xceptiondeep learning with depthwise separable convolutions hyperparameter optimization when building deep-learning modelyou have to make many seemingly arbitrary decisionshow many layers should you stackhow many units or filters should go in each layershould you use relu as activ... |
11,656 | advanced deep-learning best practices in the right direction updating hyperparameterson the other handis extremely challenging consider the followingcomputing the feedback signal (does this set of hyperparameters lead to high-performing model on this task?can be extremely expensiveit requires creating and training new ... |
11,657 | getting the most out of your models ensembling relies on the assumption that different good models trained independently are likely to be good for different reasonseach model looks at slightly different aspects of the data to make its predictionsgetting part of the "truthbut not all of it you may be familiar with the a... |
11,658 | advanced deep-learning best practices that elephants are like snakesand they would forever stay ignorant of the truth of the elephant diversity is what makes ensembling work in machine-learning termsif all of your models are biased in the same waythen your ensemble will retain this same bias if your models are biased i... |
11,659 | timethe process is expensiveand the tools to do it aren' very good but the hyperopt and hyperas libraries may be able to help you when doing hyperparameter optimizationbe mindful of validation-set overfittingwinning machine-learning competitions or otherwise obtaining the best possible results on task can only be done ... |
11,660 | advanced deep-learning best practices summary in this you learned the followinghow to build models as arbitrary graphs of layersreuse layers (layer weight sharing)and use models as python functions (model templatingyou can use keras callbacks to monitor your models during training and take action based on model state t... |
11,661 | this covers text generation with lstm implementing deepdream performing neural style transfer variational autoencoders understanding generative adversarial networks the potential of artificial intelligence to emulate human thought processes goes beyond passive tasks such as object recognition and mostly reactive tasks ... |
11,662 | generative deep learning grantedthe artistic productions we've seen from ai so far have been fairly low quality ai isn' anywhere close to rivaling human screenwriterspaintersand composers but replacing humans was always beside the pointartificial intelligence isn' about replacing our own intelligence with something els... |
11,663 | text generation with lstm in this sectionwe'll explore how recurrent neural networks can be used to generate sequence data we'll use text generation as an examplebut the exact same techniques can be generalized to any kind of sequence datayou could apply it to sequences of musical notes in order to generate new musicto... |
11,664 | generative deep learning how do you generate sequence datathe universal way to generate sequence data in deep learning is to train network (usually an rnn or convnetto predict the next token or next few tokens in sequenceusing the previous tokens as input for instancegiven the input "the cat is on the ma,the network is... |
11,665 | text generation with lstm of the time note that greedy sampling can be also cast as sampling from probability distributionone where certain character has probability and all others have probability sampling probabilistically from the softmax output of the model is neatit allows even unlikely characters to be sampled so... |
11,666 | generative deep learning probability of sampling element higher temperatures result in sampling distributions of higher entropy that will generate more surprising and unstructured generated datawhereas lower temperature will result in less randomness and much more predictable generated data (see figure temperature temp... |
11,667 | text generation with lstm nextyou'll extract partially overlapping sequences of length maxlenone-hot encode themand pack them in numpy array of shape (sequencesmaxlenunique_characterssimultaneouslyyou'll prepare an array containing the corresponding targetsthe one-hot-encoded characters that come after each extracted s... |
11,668 | generative deep learning because your targets are one-hot encodedyou'll use categorical_crossentropy as the loss to train the model listing model compilat ion configurat ion optimizer keras optimizers rmsprop(lr= model compile(loss='categorical_crossentropy'optimizer=optimizertraining the language model and sampling fr... |
11,669 | text generation with lstm generates charactersstarting from the seed text for in range( )sampled np zeros(( maxlenlen(chars))for tchar in enumerate(generated_text)sampled[ tchar_indices[char] preds model predict(sampledverbose= )[ next_index sample(predstemperaturenext_char chars[next_indexone-hot encodes the character... |
11,670 | generative deep learning subjection of the subjection of the subjection of the self-concerning the feelings in the superiority in the subjection of the subjection of the spirit isn' to be man of the sense of the subjection and said to the strength of the sense of the here' temperature= cheerfulnessfriendliness and kind... |
11,671 | wrapping up you can generate discrete sequence data by training model to predict the next tokens( )given previous tokens in the case of textsuch model is called language model it can be based on either words or characters sampling the next token requires balance between adhering to what the model judges likelyand intro... |
11,672 | generative deep learning deepdream deepdream is an artistic image-modification technique that uses the representations learned by convolutional neural networks it was first released by google in the summer of as an implementation written using the caffe deep-learning library (this was several months before the first pu... |
11,673 | deepdream you start not from blankslightly noisy inputbut rather from an existing image--thus the resulting effects latch on to preexisting visual patternsdistorting elements of the image in somewhat artistic fashion the input images are processed at different scales (called octaves)which improves the quality of the vi... |
11,674 | generative deep learning nowlet' define tensor that contains the lossthe weighted sum of the norm of the activations of the layers in listing listing defining he loss be maximized creates dictionary that maps layer names to layer instances layer_dict dict([(layer namelayerfor layer in model layers]loss variable( for la... |
11,675 | deepdream detail reinjection detail reinjection dream upscale octave dream upscale dream octave octave figure the deepdream processsuccessive scales of spatial processing (octavesand det ail reinjection upon upscaling for each successive scalefrom the smallest to the largestyou run gradient ascent to maximize the loss ... |
11,676 | generative deep learning original_shape img shape[ : successive_shapes [original_shapefor in range( num_octave)shape tuple([int(dim (octave_scale * )for dim in original_shape]successive_shapes append(shapesuccessive_shapes successive_shapes[::- scales up the dream image prepares list of shape tuples defining the differ... |
11,677 | deepdream img np expand_dims(imgaxis= img inception_v preprocess_input(imgreturn img def deprocess_image( )if image_data_format(='channels_first' reshape(( shape[ ] shape[ ]) transpose(( )elsex reshape(( shape[ ] shape[ ] ) / + * np clip( astype('uint 'return util function to convert tensor into valid image undoes prep... |
11,678 | generative deep learning random generation of the parameters in the layer_contributions dictionary to quickly explore many different layer combinations figure shows range of results obtained using different layer configurationsfrom an image of delicious homemade pastry figure trying range of deepdream configurat ions o... |
11,679 | neural style transfer neural style transfer in addition to deepdreamanother major development in deep-learning-driven image modification is neural style transferintroduced by leon gatys et al in the summer of the neural style transfer algorithm has undergone many refinements and spawned many variations since its origin... |
11,680 | generative deep learning heredistance is norm function such as the normcontent is function that takes an image and computes representation of its contentand style is function that takes an image and computes representation of its style minimizing this loss causes style(generated_imageto be close to style(reference_imag... |
11,681 | neural style transfer preserve style by maintaining similar correlations within activations for both lowlevel layers and high-level layers feature correlations capture textures the generated image and the style-reference image should share the same textures at different spatial scales nowlet' look at keras implementati... |
11,682 | generative deep learning def deprocess_image( ) [:: + zero-centering by removing the mean pixel value [:: + from imagenet this reverses transformation done by vgg preprocess_input [:: + [::::- np clip( astype('uint 'converts images from 'bgrto 'rgbthis is also part of the reversal of return vgg preprocess_input let' se... |
11,683 | def style_loss(stylecombination) gram_matrix(stylec gram_matrix(combinationchannels size img_height img_width return sum( square( )( (channels * (size * )to these two loss componentsyou add thirdthe total variation losswhich operates on the pixels of the generated combination image it encourages spatial continuity in t... |
11,684 | adds the content loss adds the total variation loss generative deep learning loss variable( you'll define the loss by layer_features outputs_dict[content_layeradding all components to target_image_features layer_features[ :::this scalar variable combination_features layer_features[ :::loss +content_weight content_loss(... |
11,685 | neural style transfer loss_value outs[ grad_values outs[ flatten(astype('float 'self loss_value loss_value self grad_values grad_values return self loss_value def grads(selfx)assert self loss_value is not none grad_values np copy(self grad_valuesself loss_value none self grad_values none return grad_values evaluator ev... |
11,686 | generative deep learning figure some example results licensed to |
11,687 | additionallynote that running this style-transfer algorithm is slow but the transformation operated by the setup is simple enough that it can be learned by smallfast feedforward convnet as well--as long as you have appropriate training data available fast style transfer can thus be achieved by first spending lot of com... |
11,688 | generative deep learning generating images with variational autoencoders sampling from latent space of images to create entirely new images or edit existing ones is currently the most popular and successful application of creative ai in this section and the nextwe'll review some high-level concepts pertaining to image ... |
11,689 | figure cont inuous space of faces generat ed by tom whit using vaes concept vectors for image editing we already hinted at the idea of concept vector when we covered word embeddings in the idea is still the samegiven latent space of representationsor an embedding spacecertain directions in the space may encode interest... |
11,690 | figure generative deep learning the smile vect or variational autoencoders variational autoencoderssimultaneously discovered by kingma and welling in december and rezendemohamedand wierstra in january , are kind of generative model that' especially appropriate for the task of image editing via concept vectors they're m... |
11,691 | generating images with variational autoencoders encoder original input decoder compressed representation reconstructed input figure an autoencodermapping an input compressed represent ation and then decoding it back as xin practicesuch classical autoencoders don' lead to particularly useful or nicely structured latent ... |
11,692 | generative deep learning in technical termshere' how vae works an encoder module turns the input samples input_img into two parameters in latent space of representationsz_mean and z_log_variance you randomly sample point from the latent normal distribution that' assumed to generate the input imagevia z_mean exp(z_log_v... |
11,693 | generating images with variational autoencoders layers conv ( padding='same'activation='relu')(input_imgx layers conv ( padding='same'activation='relu'strides=( ))(xx layers conv ( padding='same'activation='relu')(xx layers conv ( padding='same'activation='relu')(xshape_before_flattening int_shape(xx layers flatten()(x... |
11,694 | generative deep learning decoder model(decoder_inputxz_decoded decoder(zapplies it to to recover the decoded instantiates the decoder modelwhich turns decoder_inputinto the decoded image the dual loss of vae doesn' fit the traditional expectation of sample-wise function of the form loss(inputtargetthusyou'll set up the... |
11,695 | generating images with variational autoencoders once such model is trained--on mnistin this case--you can use the decoder network to turn arbitrary latent space vectors into images listing sampling grid of points from the latent space and decoding them to images import matplotlib pyplot as plt from scipy stats import n... |
11,696 | generative deep learning wrapping up image generation with deep learning is done by learning latent spaces that capture statistical information about dataset of images by sampling and decoding points from the latent spaceyou can generate never-before-seen images there are two major tools to do thisvaes and gans vaes re... |
11,697 | introduction to generative adversarial networks generative adversarial networks (gans)introduced in by goodfellow et al , are an alternative to vaes for learning latent spaces of images they enable the generation of fairly realistic synthetic images by forcing the generated images to be statistically almost indistingui... |
11,698 | generative deep learning random vector from the latent space generated (decodedimage generator (decoderdiscriminator training feedback "real,"fakemix of real and fake images figure generat or ransforms random lat ent vect ors int imagesand discriminator seeks to ell real images from generated ones the generat or is tra... |
11,699 | schematic gan implementation in this sectionwe'll explain how to implement gan in kerasin its barest form-because gans are advanceddiving deeply into the technical details would be out of scope for this book the specific implementation is deep convolutional gan (dcgan) gan where the generator and discriminator are deep... |
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