import numpy as np import matplotlib.pyplot as plt from sparse_autoencoder import SparseAutoencoder, sample_training_images training_data = sample_training_images()[:100, :] encoder = SparseAutoencoder(rho=0.01, L=0.0001, beta=3) error, analytical_grad, numerical_grad = encoder.check_grad(training_data) print "Gradient Error:", error plt.plot(analytical_grad, numerical_grad, 'ro') plt.plot( [np.min(analytical_grad), np.max(analytical_grad)], [np.min(analytical_grad), np.max(analytical_grad)], 'k-') plt.xlabel('Analytical') plt.ylabel('Numerical') plt.show()
import matplotlib.pyplot as plt from sparse_autoencoder import ( SparseAutoencoder, sample_training_images, PATCH_SIZE) training_data = sample_training_images() encoder = SparseAutoencoder(hidden_size=25, rho=0.01, L=0.0001, beta=3) encoder.train(training_data) w1 = encoder.params.w1 fig = plt.figure() for i in xrange(w1.shape[-1]): image = w1[:, i].reshape(*PATCH_SIZE) ax = fig.add_subplot(5, 5, i+1) ax.imshow(image, cmap='binary') plt.axis('off') plt.show()
import numpy as np import matplotlib.pyplot as plt from sparse_autoencoder import SparseAutoencoder, sample_training_images training_data = sample_training_images()[:100, :] encoder = SparseAutoencoder(rho=0.01, L=0.0001, beta=3) error, analytical_grad, numerical_grad = encoder.check_grad(training_data) print "Gradient Error:", error plt.plot(analytical_grad, numerical_grad, 'ro') plt.plot([np.min(analytical_grad), np.max(analytical_grad)], [np.min(analytical_grad), np.max(analytical_grad)], 'k-') plt.xlabel('Analytical') plt.ylabel('Numerical') plt.show()
import matplotlib.pyplot as plt from sparse_autoencoder import (SparseAutoencoder, sample_training_images, PATCH_SIZE) training_data = sample_training_images() encoder = SparseAutoencoder(hidden_size=25, rho=0.01, L=0.0001, beta=3) encoder.train(training_data) w1 = encoder.params.w1 fig = plt.figure() for i in xrange(w1.shape[-1]): image = w1[:, i].reshape(*PATCH_SIZE) ax = fig.add_subplot(5, 5, i + 1) ax.imshow(image, cmap='binary') plt.axis('off') plt.show()