# 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: