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 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()
def readFrames(directory, which_set, timestamps, patch_pose): videos = [] videos = [name for name in os.listdir(directory, which_set)] videos.sort() num_patch = np.zeros((len(np.unique(timestamps)), 1)) i = 0 for timestamp in timestamps: num_patch[i, 0] = np.sum((timestamps == timestamp) * 1) i += 1 for video in videos: frames = [] orientationFileNames = [ os.path.join(directory, video, 'Orientation', name) for name in os.listdir( os.path.join(directory, video, 'Orientation')) ] tempGradientFileNames = [ os.path.join(directory, video, 'TempGradient', name) for name in os.listdir( os.path.join(directory, video, 'TempGradient')) ] timeStampsFileNames = [ os.path.join(directory, video, 'TimeStamps', name) for name in os.listdir( os.path.join(directory, video, 'TimeStamps')) ] orientationFileNames.sort() tempGradientFileNames.sort() timeStampsFileNames.sort() for i in range(len(orientationFileNames)): all_orientation = scio.loadmat( orientationFileNames[i])['filteredOrientationFrame'] all_ox = scio.loadmat(tempGradientFileNames[i])['ox'] all_oy = scio.loadmat(tempGradientFileNames[i])['oy'] all_frequency = scio.loadmat( tempGradientFileNames[i])['posLastEventPosition'] all_postimestamp = scio.loadmat( timeStampsFileNames[i])['posTimeStamp'] frames.append(all_orientation) frames.append(all_ox) frames.append(all_oy) frames.append(all_frequency) frames.append(all_postimestamp) n_feature = 5 inputNodes = selected_patchs(frames, num_patch, patch_pose, n_feature) input_size = inputNodes.shape[0] hidden_size = inputNodes / n_feature if which_set is 'train': model = SparseAutoencoder(input_size, hidden_size, 'Dining') globalD = model.train(inputNodes) if which_set is 'test': model = SparseAutoencoder(input_size, hidden_size, 'Dining') globalD = model.predict(inputNodes) return inputNodes