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 = generator.predict(zsamples) xpred = dim_ordering_unfix(xpred.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 ) 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)) # train model x_train, x_test = cifar10_data() y = targets(x_train.shape[0]) y_test = targets(x_test.shape[0]) history = model.fit(x=x_train, y=y, validation_data=(x_test, y_test), callbacks=callbacks, epochs=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'): # gan (x - > yfake, yreal) # z generated on GPU gan = simple_gan(generator, discriminator, 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(): return generator.predict(zsamples).reshape((10, 10, 28, 28)) generator_cb = ImageGridCallback(os.path.join(path, "epoch-{:03d}.png"), generator_sampler) # train model xtrain, xtest = mnist_data() # targets = gan_targets -> a 0/1 címkéket rendeli az adatokhoz y = targets(xtrain.shape[0]) ytest = targets(xtest.shape[0]) callbacks = [generator_cb] history = fit(model, x=xtrain, y=y, validation_data=(xtest, ytest), callbacks=callbacks, nb_epoch=nb_epoch, batch_size=32)
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 generated on GPU gan = simple_gan(generator, discriminator, normal_latent_sampling((latent_dim,))) # print summary of models generator.summary() discriminator.summary() gan.summary() # 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(): return generator.predict(zsamples).reshape((10, 10, 28, 28)) generator_cb = ImageGridCallback(os.path.join(path, "epoch-{:03d}.png"), generator_sampler) # train model xtrain, xtest = mnist_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 main(): latent_dim = 100 input_shape = (1, 28, 28) generator = model_generator() discriminator = model_discriminator(input_shape=input_shape) gan = simple_gan(generator, discriminator, normal_latent_sampling((latent_dim, ))) generator.summary() discriminator.summary() gan.summary() model = AdversarialModel(base_model=gan, player_params=[ generator.trainable_weights, discriminator.trainable_weights ], player_names=["generator", "discriminator"]) model.adversarial_compile( adversarial_optimizer=AdversarialOptimizerSimultaneous(), player_optimizers=[Adam(1e-4, decay=1e-4), Adam(1e-3, decay=1e-4)], loss='binary_crossentropy') generator_cb = ImageGridCallback( "output/gan_convolutional/epoch-{:03d}.png", generator_sampler(latent_dim, generator)) xtrain, xtest = mnist_data() xtrain = dim_ordering_fix(xtrain.reshape((-1, 1, 28, 28))) xtest = dim_ordering_fix(xtest.reshape((-1, 1, 28, 28))) y = gan_targets(xtrain.shape[0]) ytest = gan_targets(xtest.shape[0]) history = model.fit(x=xtrain, y=y, validation_data=(xtest, ytest), callbacks=[generator_cb], nb_epoch=100, batch_size=32) df = pd.DataFrame(history.history) df.to_csv("output/gan_convolutional/history.csv") generator.save("output/gan_convolutional/generator.h5") discriminator.save("output/gan_convolutional/discriminator.h5")
def run_gan(exp_dir, adversarial_optimizer, opt_g, opt_d, generator, discriminator, latent_dim, targets=gan_targets, loss='binary_crossentropy'): #print models 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)) xpred = scale_value(xpred, [0.0, 1.0]) return xpred.reshape((10, 10) + xpred.shape[1:]) save_image_cb = ImageGridCallback('./dcgan-v2-images/' + exp_dir + '/epoch-{:03d}.png', generator_sampler, cmap=None) save_model_cb = SaveModelWeights(generator, './dcgan-v2-model-weights/' + exp_dir) # train model xtrain, xtest = svhn_data() y = targets(xtrain.shape[0]) ytest = targets(xtest.shape[0]) callbacks = [save_image_cb, save_model_cb] #train model epoch_start = 0 epoch_count = 100 history = fit(model, x=xtrain, y=y, validation_data=(xtest, ytest), callbacks=callbacks, nb_epoch=epoch_start + epoch_count, batch_size=32, initial_epoch = epoch_start, shuffle=True) # save history to CSV df = pd.DataFrame(history.history) df.to_csv('./dcgan-v2-images/' + exp_dir + '/history.csv') #save final models generator.save('./dcgan-v2-model-weights/' + exp_dir + '/generator.h5') discriminator.save('./dcgan-v2-model-weights/' + exp_dir + '/discriminator.h5')
def initialize_callbacks(path, generator, discriminator, latent_dim): def generator_sampler(): labels = np.array([int(t / nb_labels) for t in range(100)]) zsamples = np.random.normal(size=(10 * 10, latent_dim)) generated_images = generator.predict([zsamples, labels]) xpred = dim_ordering_unfix(generated_images).transpose((0, 2, 3, 1)) return xpred.reshape((10, 10) + xpred.shape[1:]) generator_cb = ImageGridCallback(os.path.join(path, "images", "epoch-{:03d}.png"), generator_sampler, cmap=None) tensor_board = TensorBoard(log_dir=os.path.join(path, 'logs'), histogram_freq=0, write_graph=True, write_images=True) model_saver = AdversarialModelSaver(path, generator, discriminator) callbacks = [generator_cb, tensor_board, model_saver] return callbacks
], player_names=["generator", "discriminator"]) model.adversarial_compile( adversarial_optimizer=AdversarialOptimizerSimultaneous(), player_optimizers=[Adam(1e-4, decay=1e-4), Adam(1e-3, decay=1e-4)], loss='binary_crossentropy', player_compile_kwargs=[{ 'metrics': ['accuracy'] }, { 'metrics': ['accuracy'] }]) # train model generator_cb = ImageGridCallback( "output/gan_convolutional/epoch-{:03d}.png", generator_sampler(latent_dim, generator)) fname = "base_hiver_2008.pklgz" with gzip.open(fname, "rb") as fp: dictio = pickle.load(fp) data = dictio['SSTMW'] x = data[:, :92, :92].astype(np.float32) xtrain = x[:-10] xtest = x[-10:] mini = np.min(xtrain.ravel()) maxi = np.max(xtrain.ravel()) xtrain = (xtrain - mini) / (maxi - mini) xtest = (xtest - mini) / (maxi - mini)
def example_aae(path, adversarial_optimizer): # z \in R^100 latent_dim = 100 # x \in R^{28x28} input_shape = (28, 28) # generator (z -> x) generator = model_generator(latent_dim, input_shape) # encoder (x ->z) encoder = model_encoder(latent_dim, input_shape) # autoencoder (x -> x') autoencoder = Model(encoder.inputs, generator(encoder(encoder.inputs))) # discriminator (z -> y) discriminator = model_discriminator(latent_dim) # assemple 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(1e-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-2, "yreal": 1e-2, "xpred": 1 } }] * 2) # load mnist data xtrain, xtest = mnist_data() # callback for image grid of generated samples def generator_sampler(): zsamples = np.random.normal(size=(10 * 10, latent_dim)) return generator.predict(zsamples).reshape((10, 10, 28, 28)) 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 = autoencoder.predict(xrep).reshape((10, 9, 28, 28)) xsamples = xsamples.reshape((10, 1, 28, 28)) samples = np.concatenate((xsamples, xgen), axis=1) return samples autoencoder_cb = ImageGridCallback( os.path.join(path, "autoencoded-epoch-{:03d}.png"), autoencoder_sampler) # 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 = model.fit(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_bigan(path, adversarial_optimizer): # z \in R^100 latent_dim = 25 # x \in R^{28x28} input_shape = (28, 28) # generator (z -> x) generator = model_generator(latent_dim, input_shape) # encoder (x ->z) encoder = model_encoder(latent_dim, input_shape) # autoencoder (x -> x') autoencoder = Model(encoder.inputs, generator(encoder(encoder.inputs))) # discriminator (x -> y) discriminator_train, discriminator_test = model_discriminator( latent_dim, input_shape) # bigan (z, x - > yfake, yreal) bigan_generator = simple_bigan(generator, encoder, discriminator_test) bigan_discriminator = simple_bigan(generator, encoder, discriminator_train) # z generated on GPU based on batch dimension of x x = bigan_generator.inputs[1] z = normal_latent_sampling((latent_dim, ))(x) # eliminate z from inputs bigan_generator = Model([x], fix_names(bigan_generator([z, x]), bigan_generator.output_names)) bigan_discriminator = Model([x], fix_names(bigan_discriminator([z, x]), bigan_discriminator.output_names)) generative_params = generator.trainable_weights + encoder.trainable_weights # print summary of models generator.summary() encoder.summary() discriminator_train.summary() bigan_discriminator.summary() autoencoder.summary() # build adversarial model model = AdversarialModel( player_models=[bigan_generator, bigan_discriminator], player_params=[ generative_params, discriminator_train.trainable_weights ], player_names=["generator", "discriminator"]) model.adversarial_compile( adversarial_optimizer=adversarial_optimizer, player_optimizers=[Adam(1e-4, decay=1e-4), Adam(1e-3, decay=1e-4)], loss='binary_crossentropy') # load mnist data xtrain, xtest = mnist_data() # callback for image grid of generated samples def generator_sampler(): zsamples = np.random.normal(size=(10 * 10, latent_dim)) return generator.predict(zsamples).reshape((10, 10, 28, 28)) 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 = autoencoder.predict(xrep).reshape((10, 9, 28, 28)) xsamples = xsamples.reshape((10, 1, 28, 28)) x = np.concatenate((xsamples, xgen), axis=1) return x autoencoder_cb = ImageGridCallback( os.path.join(path, "autoencoded-epoch-{:03d}.png"), autoencoder_sampler) # train network y = gan_targets(xtrain.shape[0]) ytest = gan_targets(xtest.shape[0]) history = model.fit(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_train.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, 128, 170)) input_shape = dim_ordering_shape((3, 32, 32)) #input_shape = (3,32,32) ###input_shape = (48,48,3) # 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 print("generator (z -> x)") generator.summary() print("encoder (x ->z)") encoder.summary() print("autoencoder (x -> x')") discriminator.summary() print("discriminator (z -> y)") 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 = benthoz_data() print("xtrain shapes {}".format(xtrain.shape)) print("xtrain mean val {}".format(np.mean(xtrain))) # 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)) #return generator.predict(zsamples).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)) #xgen = autoencoder.predict(xrep).reshape((10, 9, 32, 32, 3)) #xsamples = xsamples.reshape((10, 1, 32, 32,3)) 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] print("num train samples {}".format(n)) 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) #history = fit(model, x=xtrain, y=y, validation_data=(xtest, ytest),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"))