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) # train model 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) xtrain, xtest = mnist_data() y = targets(xtrain.shape[0]) ytest = targets(xtest.shape[0]) history = model.fit(x=xtrain, y=y, validation_data=(xtest, ytest), callbacks=[generator_cb], nb_epoch=nb_epoch, batch_size=32) df = pd.DataFrame(history.history) df.to_csv(csvpath) 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 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_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" }, compile_kwargs={ "loss_weights": { "yfake": 1e-2, "yreal": 1e-2, "xpred": 1 } }) # 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"))
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') def generator_sampler(): zsamples = np.random.normal(size=(10*10,latent_dim)) gen = dim_ordering_unfix(generator.predict(zsamples)) return gen.reshape((10,10,28,28)) generator_cb = ImageGridCallback("output/gan_convolutional/epoch-{:03d}.png",generator_sampler) 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 dim_ordering_fix(x): if k.image_dim_ordering()=='th': return x else:
def main(): # 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 (x -> y) discriminator = model_discriminator(latent_dim, input_shape) # bigan (x - > yfake, yreal), z generated on GPU bigan = simple_bigan(generator, encoder, discriminator, normal_latent_sampling((latent_dim, ))) generative_params = generator.trainable_weights + encoder.trainable_weights # print summary of models generator.summary() encoder.summary() discriminator.summary() bigan.summary() autoencoder.summary() # build adversarial model model = AdversarialModel( base_model=bigan, player_params=[generative_params, 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') # train model xtrain, xtest = mnist_data() def generator_sampler(): zsamples = np.random.normal(size=(10 * 10, latent_dim)) return generator.predict(zsamples).reshape((10, 10, 28, 28)) generator_cb = ImageGridCallback("output/bigan/generated-epoch-{:03d}.png", generator_sampler) 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( "output/bigan/autoencoded-epoch-{:03d}.png", autoencoder_sampler) 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) df = pd.DataFrame(history.history) df.to_csv("output/bigan/history.csv") encoder.save("output/bigan/encoder.h5") generator.save("output/bigan/generator.h5") discriminator.save("output/bigan/discriminator.h5")
# build adversarial models model = AdversarialModel(base_model=gan, player_params=[ generator.trainable_weights, descriminator.trainable_weights ], player_names=["generator", "discrminator"]) model.adversarial_compile( adversarial_optimizer=AdversarialOptimizerSimultaneous(), player_optimizers=[Adam(1e-4, decay=1e-4), Adam(1e-3, decay=1e-4)], loss='binary_crossentropy') #train model generator_cb = ImageGridCallback( 'output/gan_convolutional/epoch-{:03d}.png', generator_samples(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(xtest.shape[0]) xtest = gan_targets(xtest.shape[0]) history = model.fit(x=xtrain, y=y, validation_data=(xtest, y), callbacks=[generator_cb], nb_epoch=10, batch_size=32) df = pd.DataFrame(history.history) df.to_csv('output/gan_convolutional/history.csv') generator.save("output/gan_convolutional/generator.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"))
# 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)