run_cnn_3class Namespace Reference

Functions
def	load_model (name)

Variables
	parser = argparse.ArgumentParser(description='Run CNN over a full 2D projection.')
	help
	default
	args = parser.parse_args()
	PATCH_SIZE_W = int(args.rows)
	PATCH_SIZE_D = int(args.cols)
bool	crop_event = False
	rootModule = args.module
	rootFile = TFile(args.input)
list	keys = [rootModule+'/'+k.GetName()[:-4] for k in rootFile.Get(rootModule).GetListOfKeys() if '_raw' in k.GetName()]
	evname = keys[int(args.event)]
	raw
	deposit
	pdg
	tracks
	showers
	full2d = int(args.full)
	total_patches
	inputs = np.zeros((total_patches, PATCH_SIZE_W, PATCH_SIZE_D), dtype=np.float32)
int	cnt_ind = 0
	model_name = args.net
	m = load_model(model_name)
	loss
	optimizer
	pred = m.predict(inputs.reshape(inputs.shape[0], 1, PATCH_SIZE_W, PATCH_SIZE_D))
	outputs = np.zeros((pred.shape[1], raw.shape[0], raw.shape[1]), dtype=np.float32)
	mask = np.zeros(raw.shape, dtype=np.int32)
float	mask_thr = 0.67
int	none_idx = pred.shape[1]-1
	pnorm = pred[:, 0]+pred[:, 1]+pred[:, none_idx]
float	pn = 1.0
	fig
	ax
	figsize
	cs = ax[0,0].pcolor(np.transpose(pdg & 0xFF), cmap='gist_ncar')

Function Documentation

def run_cnn_3class.load_model

(

name

)

Definition at line 38 of file run_cnn_3class.py.

def load_model(name):
    with open(name + '_architecture.json') as f:
        model = model_from_json(f.read())
    model.load_weights(name + '_weights.h5')
    return model

Variable Documentation

run_cnn_3class.args = parser.parse_args()

Definition at line 11 of file run_cnn_3class.py.

run_cnn_3class.ax

Definition at line 109 of file run_cnn_3class.py.

int run_cnn_3class.cnt_ind = 0

Definition at line 62 of file run_cnn_3class.py.

bool run_cnn_3class.crop_event = False

Definition at line 47 of file run_cnn_3class.py.

run_cnn_3class.cs = ax[0,0].pcolor(np.transpose(pdg & 0xFF), cmap='gist_ncar')

Definition at line 111 of file run_cnn_3class.py.

run_cnn_3class.default

Definition at line 3 of file run_cnn_3class.py.

run_cnn_3class.deposit

Definition at line 54 of file run_cnn_3class.py.

run_cnn_3class.evname = keys[int(args.event)]

Definition at line 52 of file run_cnn_3class.py.

run_cnn_3class.fig

Definition at line 109 of file run_cnn_3class.py.

run_cnn_3class.figsize

Definition at line 109 of file run_cnn_3class.py.

run_cnn_3class.full2d = int(args.full)

Definition at line 55 of file run_cnn_3class.py.

run_cnn_3class.help

Definition at line 3 of file run_cnn_3class.py.

run_cnn_3class.inputs = np.zeros((total_patches, PATCH_SIZE_W, PATCH_SIZE_D), dtype=np.float32)

Definition at line 60 of file run_cnn_3class.py.

list run_cnn_3class.keys = [rootModule+'/'+k.GetName()[:-4] for k in rootFile.Get(rootModule).GetListOfKeys() if '_raw' in k.GetName()]

Definition at line 51 of file run_cnn_3class.py.

run_cnn_3class.loss

Definition at line 76 of file run_cnn_3class.py.

run_cnn_3class.m = load_model(model_name)

Definition at line 75 of file run_cnn_3class.py.

run_cnn_3class.mask = np.zeros(raw.shape, dtype=np.int32)

Definition at line 84 of file run_cnn_3class.py.

float run_cnn_3class.mask_thr = 0.67

Definition at line 85 of file run_cnn_3class.py.

run_cnn_3class.model_name = args.net

Definition at line 74 of file run_cnn_3class.py.

int run_cnn_3class.none_idx = pred.shape[1]-1

Definition at line 87 of file run_cnn_3class.py.

run_cnn_3class.optimizer

Definition at line 76 of file run_cnn_3class.py.

run_cnn_3class.outputs = np.zeros((pred.shape[1], raw.shape[0], raw.shape[1]), dtype=np.float32)

Definition at line 82 of file run_cnn_3class.py.

run_cnn_3class.parser = argparse.ArgumentParser(description='Run CNN over a full 2D projection.')

Definition at line 2 of file run_cnn_3class.py.

run_cnn_3class.PATCH_SIZE_D = int(args.cols)

Definition at line 46 of file run_cnn_3class.py.

run_cnn_3class.PATCH_SIZE_W = int(args.rows)

Definition at line 45 of file run_cnn_3class.py.

run_cnn_3class.pdg

Definition at line 54 of file run_cnn_3class.py.

float run_cnn_3class.pn = 1.0

Definition at line 97 of file run_cnn_3class.py.

run_cnn_3class.pnorm = pred[:, 0]+pred[:, 1]+pred[:, none_idx]

Definition at line 88 of file run_cnn_3class.py.

run_cnn_3class.pred = m.predict(inputs.reshape(inputs.shape[0], 1, PATCH_SIZE_W, PATCH_SIZE_D))

Definition at line 79 of file run_cnn_3class.py.

run_cnn_3class.raw

Definition at line 54 of file run_cnn_3class.py.

run_cnn_3class.rootFile = TFile(args.input)

Definition at line 50 of file run_cnn_3class.py.

run_cnn_3class.rootModule = args.module

Definition at line 49 of file run_cnn_3class.py.

run_cnn_3class.showers

Definition at line 54 of file run_cnn_3class.py.

run_cnn_3class.total_patches

Definition at line 56 of file run_cnn_3class.py.

run_cnn_3class.tracks

Definition at line 54 of file run_cnn_3class.py.