def example_gan(adversarial_optimizer, path, opt_g, opt_d, nb_epoch, generator, discriminator, latent_dim, targets=gan_targets, loss='binary_crossentropy'): csvpath = os.path.join(path, "history.csv") if os.path.exists(csvpath): print("Already exists: {}".format(csvpath)) return print("Training: {}".format(csvpath)) # gan (x - > yfake, yreal), z is gaussian generated on GPU # can also experiment with uniform_latent_sampling d_g = discriminator(0) d_d = discriminator(0.5) generator.summary() d_d.summary() gan_g = simple_gan(generator, d_g, None) gan_d = simple_gan(generator, d_d, None) x = gan_g.inputs[1] z = normal_latent_sampling((latent_dim,))(x) # eliminate z from inputs gan_g = Model([x], fix_names(gan_g([z, x]), gan_g.output_names)) gan_d = Model([x], fix_names(gan_d([z, x]), gan_d.output_names)) # build adversarial model model = AdversarialModel(player_models=[gan_g, gan_d], player_params=[generator.trainable_weights, d_d.trainable_weights], player_names=["generator", "discriminator"]) model.adversarial_compile(adversarial_optimizer=adversarial_optimizer, player_optimizers=[opt_g, opt_d], loss=loss) # create callback to generate images zsamples = np.random.normal(size=(10 * 10, latent_dim)) def generator_sampler(): xpred = dim_ordering_unfix(generator.predict(zsamples)).transpose((0, 2, 3, 1)) return xpred.reshape((10, 10) + xpred.shape[1:]) generator_cb = ImageGridCallback(os.path.join(path, "epoch-{:03d}.png"), generator_sampler, cmap=None) # train model xtrain, xtest = cifar10_data() y = targets(xtrain.shape[0]) ytest = targets(xtest.shape[0]) callbacks = [generator_cb] if K.backend() == "tensorflow": callbacks.append( TensorBoard(log_dir=os.path.join(path, 'logs'), histogram_freq=0, write_graph=True, write_images=True)) history = model.fit(x=dim_ordering_fix(xtrain), y=y, validation_data=(dim_ordering_fix(xtest), ytest), callbacks=callbacks, nb_epoch=nb_epoch, batch_size=32) # save history to CSV df = pd.DataFrame(history.history) df.to_csv(csvpath) # save models generator.save(os.path.join(path, "generator.h5")) d_d.save(os.path.join(path, "discriminator.h5"))
def example_gan(adversarial_optimizer, path, opt_g, opt_d, nb_epoch, generator, discriminator, latent_dim, targets=gan_targets, loss='binary_crossentropy'): csvpath = os.path.join(path, "history.csv") if os.path.exists(csvpath): print("Already exists: {}".format(csvpath)) return print("Training: {}".format(csvpath)) # gan (x - > yfake, yreal), z is gaussian generated on GPU # can also experiment with uniform_latent_sampling generator.summary() discriminator.summary() gan = simple_gan(generator=generator, discriminator=discriminator, latent_sampling=normal_latent_sampling((latent_dim,))) # 적대적 모델 정의 model = AdversarialModel(base_model=gan, player_params=[generator.trainable_weights, discriminator.trainable_weights], player_names=["generator", "discriminator"]) model.adversarial_compile(adversarial_optimizer=adversarial_optimizer, player_optimizers=[opt_g, opt_d], loss=loss) # 이미지 생성을 위한 콜백 생성 zsamples = np.random.normal(size=(10 * 10, latent_dim)) def generator_sampler(): xpred = dim_ordering_unfix(generator.predict(zsamples)).transpose((0, 2, 3, 1)) return xpred.reshape((10, 10) + xpred.shape[1:]) generator_cb = ImageGridCallback(os.path.join(path, "epoch-{:03d}.png"), generator_sampler, cmap=None) # 모델 학습 xtrain, xtest = cifar10_data() y = targets(xtrain.shape[0]) ytest = targets(xtest.shape[0]) callbacks = [generator_cb] K.set_image_dim_ordering('tf') if K.backend() == "tensorflow": os.makedirs(path + '/logs',exist_ok=True) callbacks.append( TensorBoard(log_dir=os.path.join(path, 'logs'), histogram_freq=0, write_graph=True, write_images=True)) history = fit(model, x=xtrain, y=y, validation_data=(xtest, ytest), callbacks=callbacks, nb_epoch=nb_epoch, batch_size=32) # 히스토리를 CSV에 저장 df = pd.DataFrame(history.history) df.to_csv(csvpath) # 모델 저장 generator.save(os.path.join(path, "generator.h5")) discriminator.save(os.path.join(path, "discriminator.h5"))
def example_gan(adversarial_optimizer, path, opt_g, opt_d, nb_epoch, generator, discriminator, latent_dim, targets=gan_targets, loss='binary_crossentropy'): csvpath = os.path.join(path, "history.csv") if os.path.exists(csvpath): print("Already exists: {}".format(csvpath)) return print("Training: {}".format(csvpath)) # gan (x - > yfake, yreal), z is gaussian generated on GPU # can also experiment with uniform_latent_sampling generator.summary() discriminator.summary() gan = simple_gan(generator=generator, discriminator=discriminator, latent_sampling=normal_latent_sampling((latent_dim,))) # build adversarial model model = AdversarialModel(base_model=gan, player_params=[generator.trainable_weights, discriminator.trainable_weights], player_names=["generator", "discriminator"]) model.adversarial_compile(adversarial_optimizer=adversarial_optimizer, player_optimizers=[opt_g, opt_d], loss=loss) # create callback to generate images zsamples = np.random.normal(size=(10 * 10, latent_dim)) def generator_sampler(): xpred = dim_ordering_unfix(generator.predict(zsamples)).transpose((0, 2, 3, 1)) return xpred.reshape((10, 10) + xpred.shape[1:]) generator_cb = ImageGridCallback(os.path.join(path, "epoch-{:03d}.png"), generator_sampler, cmap=None) # train model xtrain, xtest = cifar10_data() y = targets(xtrain.shape[0]) ytest = targets(xtest.shape[0]) callbacks = [generator_cb] if K.backend() == "tensorflow": callbacks.append( TensorBoard(log_dir=os.path.join(path, 'logs'), histogram_freq=0, write_graph=True, write_images=True)) history = fit(model, x=xtrain, y=y, validation_data=(xtest, ytest), callbacks=callbacks, nb_epoch=nb_epoch, batch_size=32) # save history to CSV df = pd.DataFrame(history.history) df.to_csv(csvpath) # save models generator.save(os.path.join(path, "generator.h5")) discriminator.save(os.path.join(path, "discriminator.h5"))
def gen_sample(batch_size, latent_dim, test): xtrain, xtest = cifar10_data() if test: data = xtest limit = xtest.shape[0] else: data = xtrain limit = xtrain.shape[0] while True: noise = np.random.randn(batch_size, latent_dim) # noise = 1/(1+np.exp(-noise)) yield [data[random.sample(range(limit), batch_size)], noise], [ noise, np.ones((batch_size, 1)), np.zeros((batch_size, 1)), noise, np.zeros((batch_size, 1)), np.ones((batch_size, 1)) ]
def example_aae(path, adversarial_optimizer): # z \in R^100 latent_dim = 256 units = 512 # x \in R^{28x28} input_shape = dim_ordering_shape((3, 32, 32)) # generator (z -> x) generator = model_generator(latent_dim, units=units) # encoder (x ->z) encoder = model_encoder(latent_dim, input_shape, units=units) # autoencoder (x -> x') autoencoder = Model(encoder.inputs, generator(encoder(encoder.inputs))) # discriminator (z -> y) discriminator = model_discriminator(latent_dim, units=units) # build AAE x = encoder.inputs[0] z = encoder(x) xpred = generator(z) zreal = normal_latent_sampling((latent_dim, ))(x) yreal = discriminator(zreal) yfake = discriminator(z) aae = Model(x, fix_names([xpred, yfake, yreal], ["xpred", "yfake", "yreal"])) # print summary of models generator.summary() encoder.summary() discriminator.summary() autoencoder.summary() # build adversarial model generative_params = generator.trainable_weights + encoder.trainable_weights model = AdversarialModel( base_model=aae, player_params=[generative_params, discriminator.trainable_weights], player_names=["generator", "discriminator"]) model.adversarial_compile( adversarial_optimizer=adversarial_optimizer, player_optimizers=[Adam(3e-4, decay=1e-4), Adam(1e-3, decay=1e-4)], loss={ "yfake": "binary_crossentropy", "yreal": "binary_crossentropy", "xpred": "mean_squared_error" }, player_compile_kwargs=[{ "loss_weights": { "yfake": 1e-1, "yreal": 1e-1, "xpred": 1e2 } }] * 2) # load mnist data xtrain, xtest = cifar10_data() # callback for image grid of generated samples def generator_sampler(): zsamples = np.random.normal(size=(10 * 10, latent_dim)) return dim_ordering_unfix(generator.predict(zsamples)).transpose( (0, 2, 3, 1)).reshape((10, 10, 32, 32, 3)) generator_cb = ImageGridCallback( os.path.join(path, "generated-epoch-{:03d}.png"), generator_sampler) # callback for image grid of autoencoded samples def autoencoder_sampler(): xsamples = n_choice(xtest, 10) xrep = np.repeat(xsamples, 9, axis=0) xgen = dim_ordering_unfix(autoencoder.predict(xrep)).reshape( (10, 9, 3, 32, 32)) xsamples = dim_ordering_unfix(xsamples).reshape((10, 1, 3, 32, 32)) samples = np.concatenate((xsamples, xgen), axis=1) samples = samples.transpose((0, 1, 3, 4, 2)) return samples autoencoder_cb = ImageGridCallback(os.path.join( path, "autoencoded-epoch-{:03d}.png"), autoencoder_sampler, cmap=None) # train network # generator, discriminator; pred, yfake, yreal n = xtrain.shape[0] y = [ xtrain, np.ones((n, 1)), np.zeros((n, 1)), xtrain, np.zeros((n, 1)), np.ones((n, 1)) ] ntest = xtest.shape[0] ytest = [ xtest, np.ones((ntest, 1)), np.zeros((ntest, 1)), xtest, np.zeros((ntest, 1)), np.ones((ntest, 1)) ] history = fit(model, x=xtrain, y=y, validation_data=(xtest, ytest), callbacks=[generator_cb, autoencoder_cb], nb_epoch=100, batch_size=32) # save history df = pd.DataFrame(history.history) df.to_csv(os.path.join(path, "history.csv")) # save model encoder.save(os.path.join(path, "encoder.h5")) generator.save(os.path.join(path, "generator.h5")) discriminator.save(os.path.join(path, "discriminator.h5"))
def example_aae(path, adversarial_optimizer): # z \in R^100 latent_dim = 256 units = 512 # x \in R^{28x28} input_shape = dim_ordering_shape((3, 32, 32)) # generator (z -> x) generator = model_generator(latent_dim, units=units) # encoder (x ->z) encoder = model_encoder(latent_dim, input_shape, units=units) # autoencoder (x -> x') autoencoder = Model(encoder.inputs, generator(encoder(encoder.inputs))) # discriminator (z -> y) discriminator = model_discriminator(latent_dim, units=units) # build AAE x = encoder.inputs[0] z = encoder(x) xpred = generator(z) zreal = normal_latent_sampling((latent_dim,))(x) yreal = discriminator(zreal) yfake = discriminator(z) aae = Model(x, fix_names([xpred, yfake, yreal], ["xpred", "yfake", "yreal"])) # print summary of models generator.summary() encoder.summary() discriminator.summary() autoencoder.summary() # build adversarial model generative_params = generator.trainable_weights + encoder.trainable_weights model = AdversarialModel(base_model=aae, player_params=[generative_params, discriminator.trainable_weights], player_names=["generator", "discriminator"]) model.adversarial_compile(adversarial_optimizer=adversarial_optimizer, player_optimizers=[Adam(3e-4, decay=1e-4), Adam(1e-3, decay=1e-4)], loss={"yfake": "binary_crossentropy", "yreal": "binary_crossentropy", "xpred": "mean_squared_error"}, compile_kwargs={"loss_weights": {"yfake": 1e-1, "yreal": 1e-1, "xpred": 1e2}}) # load mnist data xtrain, xtest = cifar10_data() # callback for image grid of generated samples def generator_sampler(): zsamples = np.random.normal(size=(10 * 10, latent_dim)) return dim_ordering_unfix(generator.predict(zsamples)).transpose((0, 2, 3, 1)).reshape((10, 10, 32, 32, 3)) generator_cb = ImageGridCallback(os.path.join(path, "generated-epoch-{:03d}.png"), generator_sampler) # callback for image grid of autoencoded samples def autoencoder_sampler(): xsamples = n_choice(xtest, 10) xrep = np.repeat(xsamples, 9, axis=0) xgen = dim_ordering_unfix(autoencoder.predict(xrep)).reshape((10, 9, 3, 32, 32)) xsamples = dim_ordering_unfix(xsamples).reshape((10, 1, 3, 32, 32)) samples = np.concatenate((xsamples, xgen), axis=1) samples = samples.transpose((0, 1, 3, 4, 2)) return samples autoencoder_cb = ImageGridCallback(os.path.join(path, "autoencoded-epoch-{:03d}.png"), autoencoder_sampler, cmap=None) # train network # generator, discriminator; pred, yfake, yreal n = xtrain.shape[0] y = [xtrain, np.ones((n, 1)), np.zeros((n, 1)), xtrain, np.zeros((n, 1)), np.ones((n, 1))] ntest = xtest.shape[0] ytest = [xtest, np.ones((ntest, 1)), np.zeros((ntest, 1)), xtest, np.zeros((ntest, 1)), np.ones((ntest, 1))] history = fit(model, x=xtrain, y=y, validation_data=(xtest, ytest), callbacks=[generator_cb, autoencoder_cb], nb_epoch=100, batch_size=32) # save history df = pd.DataFrame(history.history) df.to_csv(os.path.join(path, "history.csv")) # save model encoder.save(os.path.join(path, "encoder.h5")) generator.save(os.path.join(path, "generator.h5")) discriminator.save(os.path.join(path, "discriminator.h5"))
def example_faae(path, adversarial_optimizer): latent_dim = 256 units = 512 input_shape = dim_ordering_shape((3, 32, 32)) # generator (z -> x) generator = model_generator(latent_dim, units=units) # encoder (x ->z) encoder = model_encoder(latent_dim, input_shape, units=units) # autoencoder (x -> x') autoencoder = Model(encoder.inputs, generator(encoder(encoder.inputs))) # discriminator (z -> y) discriminator = model_discriminator() # build FAAE zreal = discriminator.inputs[0] x = generator.inputs[0] z = generator(x) xpred = encoder(z) yreal = discriminator(zreal) yfake = discriminator(z) aae = Model([zreal, x], fix_names([xpred, yfake, yreal], ["xpred", "yfake", "yreal"])) # print summary of models generator.summary() encoder.summary() discriminator.summary() #encoder.load_weights(os.path.join(path, "encoder.h5")) #generator.load_weights(os.path.join(path, "generator.h5")) #discriminator.load_weights(os.path.join(path, "discriminator.h5")) # build adversarial model generative_params = generator.trainable_weights + encoder.trainable_weights model = AdversarialModel( base_model=aae, player_params=[generative_params, discriminator.trainable_weights], player_names=["generator", "discriminator"]) model.adversarial_compile( adversarial_optimizer=adversarial_optimizer, player_optimizers=[Adam(3e-4, decay=1e-4), Adam(1e-3, decay=1e-4)], loss={ "yfake": "binary_crossentropy", "yreal": "binary_crossentropy", "xpred": "mean_squared_error" }, player_compile_kwargs=[{ "loss_weights": { "yfake": 1, "yreal": 1, "xpred": 8 } }] * 2) xtrain, xtest = cifar10_data() def generator_sampler(): zsamples = np.random.randn(10 * 10, latent_dim) return dim_ordering_unfix(generator.predict(zsamples)).transpose( (0, 2, 3, 1)).reshape((10, 10, 32, 32, 3)) generator_cb = ImageGridCallback( os.path.join(path, "generated-epoch-{:03d}.png"), generator_sampler) def autoencoder_sampler(): xsamples = n_choice(xtest, 10) xrep = np.repeat(xsamples, 9, axis=0) xgen = dim_ordering_unfix(autoencoder.predict(xrep)).reshape( (10, 9, 3, 32, 32)) xsamples = dim_ordering_unfix(xsamples).reshape((10, 1, 3, 32, 32)) samples = np.concatenate((xsamples, xgen), axis=1) samples = samples.transpose((0, 1, 3, 4, 2)) return samples autoencoder_cb = ImageGridCallback(os.path.join( path, "autoencoded-epoch-{:03d}.png"), autoencoder_sampler, cmap=None) train_datagen = gen_sample(128, 256, False) test_datagen = gen_sample(32, 256, True) history = model.fit_generator(train_datagen, epochs=200, steps_per_epoch=1000, validation_data=test_datagen, validation_steps=100, callbacks=[generator_cb, autoencoder_cb]) # save history df = pd.DataFrame(history.history) df.to_csv(os.path.join(path, "history.csv")) # save model encoder.save(os.path.join(path, "encoder.h5")) generator.save(os.path.join(path, "generator.h5")) discriminator.save(os.path.join(path, "discriminator.h5"))
player_params=[generator.trainable_weights, d_d.trainable_weights], player_names=["generator", "discriminator"]) model.adversarial_compile(adversarial_optimizer=adversarial_optimizer, player_optimizers=[opt_g, opt_d], loss=loss) # create callback to generate images zsamples = np.random.normal(size=(10 * 10, latent_dim)) def generator_sampler(): xpred = dim_ordering_unfix(generator.predict(zsamples)).transpose((0, 2, 3, 1)) return xpred.reshape((10, 10) + xpred.shape[1:]) generator_cb = ImageGridCallback(os.path.join(path, "epoch-{:03d}.png"), generator_sampler, cmap=None) # train model xtrain, xtest = cifar10_data() y = targets(xtrain.shape[0]) ytest = targets(xtest.shape[0]) callbacks = [generator_cb] if K.backend() == "tensorflow": callbacks.append(TensorBoard(log_dir=os.path.join(path, 'logs'), histogram_freq=0, write_graph=True, write_images=True)) history = model.fit(x=dim_ordering_fix(xtrain),y=y, validation_data=(dim_ordering_fix(xtest), ytest), callbacks=callbacks, nb_epoch=nb_epoch, batch_size=32) # save history to CSV df = pd.DataFrame(history.history) df.to_csv(csvpath) # save models generator.save(os.path.join(path, "generator.h5"))