for row in csv.reader(inputfile):
print row[0]
if 'filters' in row[0]:
filters = int(row[0][9:])
# build model
model = nn_model.build_model(filters)
# load trained parameters
model_parameters = save_path + 'model_parameters.hdf'
model.load_weights(model_parameters)
# calculate new reconstructions with the NN
g_nn = model.predict(f)
# resize to original resolution
g_nn = bib_utils.resize_NN_image(g_nn, training=False)
print 'g_nn:', g_nn.shape, g_nn.dtype
# -------------------------------------------------------------------------
# Plot grid of reconstructions
font = {'weight': 'normal', 'size': 3}
plt.rc('font', **font)
nx = 5
ny = 9
from matplotlib import rcParams
rcParams['axes.titlepad'] = 2
save_path = './Results/'
if not os.path.exists(save_path):
print 'Creating directory ', save_path
os.makedirs(save_path)
# ----------------------------------------------------------------------
# Load Data
fname = '../data/tomo_JET.hdf'
f, g, _, _ = bib_data.get_tomo_JET(fname,
faulty=True,
flatten=False,
clip_tomo=True)
# need to reshape image to match NN dimensions
g = bib_utils.resize_NN_image(g, training=True)
print 'g:', g.shape, g.dtype
print 'f:', f.shape, f.dtype
# ------------------------------------------------------------------------
# Divide into training, validation and test set
i_train, i_valid, i_test = bib_utils.divide_data(g.shape[0],
ratio=[.8, .1, .1],
test_set=True,
random=False)
f_valid = f[i_valid]
g_valid = g[i_valid]
f_train = f[i_train]
