return fig N_SAMPLES = 1000 train_data = normal(size=(1, N_SAMPLES)) train_data = train_data.T # TRAIN KDE # kde_model = KDE(use_pca=False) kde_model.train_model(train_data) samples = kde_model.generate_sample(N_SAMPLES) fig = plot(train_data, samples) plt.savefig('gaussian_kde.png', bbox_inches='tight') plt.close(fig) # TRAIN GAN # gan_model = GAN(input_size=1, random_size=1) gan_model.init_training() # Create dataset train_data_m = Dataset(train_data) gan_model.train_model(train_data_m) samples = gan_model.generate_sample(N_SAMPLES) fig = plot(train_data, samples) plt.savefig('gaussian_gan.png', bbox_inches='tight') plt.close(fig)
ax.set_xticklabels([]) ax.set_yticklabels([]) ax.set_aspect('equal') plt.imshow(sample.reshape(28, 28), cmap='Greys_r') return fig mnist = input_data.read_data_sets('MNIST_data', one_hot=True) train_data = mnist.train.images[0:2000, :] # TRAIN KDE # kde_model = KDE() kde_model.train_model(train_data) samples = kde_model.generate_sample(16) fig = plot(samples) plt.savefig('kde_mnist.png', bbox_inches='tight') plt.close(fig) # TRAIN GAN # gan_model = GAN() gan_model.init_training() gan_model.train_model(mnist) samples = gan_model.generate_sample(16) fig = plot(samples) plt.savefig('gan_mnist.png', bbox_inches='tight') plt.close(fig)