# coding: utf-8 # ### Generating human faces with Adversarial Networks import sys sys.path.append("..") import helpers helpers.mask_busy_gpus(wait=False) import numpy as np #Those attributes will be required for the final part of the assignment (applying smiles), so please keep them in mind #from lfw_dataset2 import load_lfw_dataset from lfw_dataset import load_lfw_dataset data, attrs = load_lfw_dataset(dimx=36, dimy=36) #data = load_lfw_dataset(use_raw=True,dimx=36,dimy=36) #print(np.max(data),np.min(data)) #preprocess faces #data = np.float32(data) #print(data[0]) data = (data - 127.5) / float(127.5) #scale to between -1 and 1 #print(data[0]) IMG_SHAPE = data.shape[1:] # In[3]: #print random image print(data.shape) import tensorflow as tf gpu_options = tf.GPUOptions(allow_growth=True, per_process_gpu_memory_fraction=0.333)
ax.legend(loc='upper left') plt.text(0.5, 1.08, figure_title, horizontalalignment='center', fontsize=15, transform=ax.transAxes) my_fig.savefig(plot_dir + '/loss_as_metric/' + save_name + "_" + train_test + ".png") if __name__ == "__main__": helper_functions.mask_busy_gpus(leave_unmasked=1) basedir = os.environ['BASEDIR'] trained_model = models.load_model(basedir + "/models/" + args.model_name + '_' + args.loss_name + '_' + args.opt_name + '.h5') #load model image_type = os.environ['EB_OCC'] try: #get the data data_folder = os.environ["DATA"] except KeyError: "Please cd into the module's base folder and run set_env from there." file_list = os.listdir(data_folder)
# coding: utf-8 # # LHC Machine Learing workshop challenge # https://gitlab.cern.ch/IML-WG/IML_challenge_2018/wikis/home # # Task: Regress the soft-drop mass of jets with high transverse momentum # In[1]: #get the data import numpy as np import helpers helpers.mask_busy_gpus() my_array = np.load('../data/qcd.npy', encoding='bytes') my_rec_array = my_array.view(np.recarray) # In[3]: fields = my_array.dtype.names # In[5]: #del my_train_val_array num_jets = my_rec_array.shape[0] for field in fields: print(field) x = getattr(my_rec_array, field) x = np.reshape(x, [1, num_jets]) try: