import baxter_writer as bw import dataset import vae_assoc import utils np.random.seed(0) tf.set_random_seed(0) print 'Loading image data...' img_data = utils.extract_images(fname='bin/img_data_extend.pkl', only_digits=False) # img_data = utils.extract_images(fname='bin/img_data.pkl', only_digits=False) # img_data_sets = dataset.construct_datasets(img_data) print 'Loading joint motion data...' fa_data, fa_mean, fa_std = utils.extract_jnt_fa_parms(fname='bin/jnt_ik_fa_data_extend.pkl', only_digits=False) # fa_data, fa_mean, fa_std = utils.extract_jnt_fa_parms(fname='bin/jnt_fa_data.pkl', only_digits=False) #normalize data fa_data_normed = (fa_data - fa_mean) / fa_std # fa_data_sets = dataset.construct_datasets(fa_data_normed) print 'Constructing dataset...' #put them together aug_data = np.concatenate((img_data, fa_data_normed), axis=1) data_sets = dataset.construct_datasets(aug_data, validation_ratio=.1, test_ratio=.1) print 'Start training...' batch_sizes = [64] #n_z_array = [3, 5, 10, 20] n_z_array = [4] # assoc_lambda_array = [1, 3, 5, 10]
