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,))) # 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=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")) discriminator.save(os.path.join(path, "discriminator.h5"))
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) xtrain = dim_ordering_fix(xtrain.reshape((-1, 1, 92, 92))) xtest = dim_ordering_fix(xtest.reshape((-1, 1, 92, 92))) 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=10) 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")
# 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=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_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")
# また、学習状況を確認するためのTensorBoardのコールバックを作成します。 generator_cb = ImageGridCallback( "output/gan_convolutional/epoch-{:03d}.png", generator_sampler(latent_dim, generator)) callbacks = [generator_cb] if K.backend() == "tensorflow": callbacks.append( TensorBoard(log_dir=os.path.join("output/gan_convolutional/", "logs/"), histogram_freq=0, write_graph=True, write_images=True)) xtrain, xtest = mnist_data() xtrain = dim_ordering_fix(xtrain.reshape((-1, 1, 28, 28))) xtest = dim_ordering_fix(xtest.reshape( (-1, 1, 28, 28))) #image_utils.pyで定義された、異なる画像の次元の順序をサポートするためのユーティリティ関数です。 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], epochs=100, batch_size=32) df = pd.DataFrame(history.history) df.to_csv("output/history.csv") generator.save("output/generator.h5")
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 main(): # z in R^100 latent_dim = 100 # x in R^{28x28}