train_cnn_continue Namespace Reference

Functions
def	load_model (name)
def	save_model (model, name)

Variables
	parser = argparse.ArgumentParser(description='Run CNN training on patches with a few different hyperparameter sets.')
	help
	default
	args = parser.parse_args()
	config = read_config(args.config)
	configuration ############################# More...
	cfg_name = args.model
	out_name = args.output
	CNN_INPUT_DIR = config['training_on_patches']['input_dir']
	PATCH_SIZE_W
	PATCH_SIZE_D
	img_rows
	img_cols
	batch_size = config['training_on_patches']['batch_size']
	nb_epoch = config['training_on_patches']['nb_epoch']
	nb_classes = config['training_on_patches']['nb_classes']
	model = load_model(cfg_name)
	CNN commpilation ###########################. More...
	sgd = SGD(lr=0.005, decay=1e-5, momentum=0.9, nesterov=True)
	optimizer
	loss
	loss_weights
	n_training = count_events(CNN_INPUT_DIR, 'training')
	read data sets ############################ More...
	X_train = np.zeros((n_training, PATCH_SIZE_W, PATCH_SIZE_D, 1), dtype=np.float32)
	EmTrkNone_train = np.zeros((n_training, 3), dtype=np.int32)
	Michel_train = np.zeros((n_training, 1), dtype=np.int32)
int	ntot = 0
list	subdirs = [f for f in os.listdir(CNN_INPUT_DIR) if 'training' in f]
list	filesX = [f for f in os.listdir(CNN_INPUT_DIR + '/' + dirname) if '_x.npy' in f]
	fnameY = fnameX.replace('_x.npy', '_y.npy')
	dataX = np.load(CNN_INPUT_DIR + '/' + dirname + '/' + fnameX)
	dataY = np.load(CNN_INPUT_DIR + '/' + dirname + '/' + fnameY)
	n = dataY.shape[0]
	n_testing = count_events(CNN_INPUT_DIR, 'testing')
	X_test = np.zeros((n_testing, PATCH_SIZE_W, PATCH_SIZE_D, 1), dtype=np.float32)
	EmTrkNone_test = np.zeros((n_testing, 3), dtype=np.int32)
	Michel_test = np.zeros((n_testing, 1), dtype=np.int32)
	h
	training ############################### More...
	score

Function Documentation

def train_cnn_continue.load_model

(

name

)

Definition at line 31 of file train_cnn_continue.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

def train_cnn_continue.save_model	(		model,
			name
	)

Definition at line 37 of file train_cnn_continue.py.

def save_model(model, name):
    try:
        with open(name + '_architecture.json', 'w') as f:
            f.write(model.to_json())
        model.save_weights(name + '_weights.h5', overwrite=True)
        return True   # Save successful
    except:
        return False  # Save failed

Variable Documentation

train_cnn_continue.args = parser.parse_args()

Definition at line 7 of file train_cnn_continue.py.

train_cnn_continue.batch_size = config['training_on_patches']['batch_size']

Definition at line 59 of file train_cnn_continue.py.

train_cnn_continue.cfg_name = args.model

Definition at line 51 of file train_cnn_continue.py.

train_cnn_continue.CNN_INPUT_DIR = config['training_on_patches']['input_dir']

Definition at line 54 of file train_cnn_continue.py.

train_cnn_continue.config = read_config(args.config)

configuration #############################

Definition at line 49 of file train_cnn_continue.py.

train_cnn_continue.dataX = np.load(CNN_INPUT_DIR + '/' + dirname + '/' + fnameX)

Definition at line 89 of file train_cnn_continue.py.

train_cnn_continue.dataY = np.load(CNN_INPUT_DIR + '/' + dirname + '/' + fnameY)

Definition at line 92 of file train_cnn_continue.py.

train_cnn_continue.default

Definition at line 3 of file train_cnn_continue.py.

train_cnn_continue.EmTrkNone_test = np.zeros((n_testing, 3), dtype=np.int32)

Definition at line 102 of file train_cnn_continue.py.

train_cnn_continue.EmTrkNone_train = np.zeros((n_training, 3), dtype=np.int32)

Definition at line 76 of file train_cnn_continue.py.

list train_cnn_continue.filesX = [f for f in os.listdir(CNN_INPUT_DIR + '/' + dirname) if '_x.npy' in f]

Definition at line 85 of file train_cnn_continue.py.

train_cnn_continue.fnameY = fnameX.replace('_x.npy', '_y.npy')

Definition at line 88 of file train_cnn_continue.py.

train_cnn_continue.h

Initial value:

= model.fit({'main_input': X_train},
              {'em_trk_none_netout': EmTrkNone_train, 'michel_netout': Michel_train},
              validation_data=(
                  {'main_input': X_test},
                  {'em_trk_none_netout': EmTrkNone_test, 'michel_netout': Michel_test}),
              batch_size=batch_size, epochs=nb_epoch, shuffle=True,
              verbose=1)

training ###############################

Definition at line 133 of file train_cnn_continue.py.

train_cnn_continue.help

Definition at line 3 of file train_cnn_continue.py.

train_cnn_continue.img_cols

Definition at line 57 of file train_cnn_continue.py.

train_cnn_continue.img_rows

Definition at line 57 of file train_cnn_continue.py.

train_cnn_continue.loss

Definition at line 70 of file train_cnn_continue.py.

train_cnn_continue.loss_weights

Definition at line 71 of file train_cnn_continue.py.

train_cnn_continue.Michel_test = np.zeros((n_testing, 1), dtype=np.int32)

Definition at line 103 of file train_cnn_continue.py.

train_cnn_continue.Michel_train = np.zeros((n_training, 1), dtype=np.int32)

Definition at line 77 of file train_cnn_continue.py.

train_cnn_continue.model = load_model(cfg_name)

CNN commpilation ###########################.

Definition at line 66 of file train_cnn_continue.py.

train_cnn_continue.n = dataY.shape[0]

Definition at line 93 of file train_cnn_continue.py.

train_cnn_continue.n_testing = count_events(CNN_INPUT_DIR, 'testing')

Definition at line 100 of file train_cnn_continue.py.

train_cnn_continue.n_training = count_events(CNN_INPUT_DIR, 'training')

read data sets ############################

Definition at line 74 of file train_cnn_continue.py.

train_cnn_continue.nb_classes = config['training_on_patches']['nb_classes']

Definition at line 61 of file train_cnn_continue.py.

train_cnn_continue.nb_epoch = config['training_on_patches']['nb_epoch']

Definition at line 60 of file train_cnn_continue.py.

int train_cnn_continue.ntot = 0

Definition at line 80 of file train_cnn_continue.py.

train_cnn_continue.optimizer

Definition at line 69 of file train_cnn_continue.py.

train_cnn_continue.out_name = args.output

Definition at line 52 of file train_cnn_continue.py.

train_cnn_continue.parser = argparse.ArgumentParser(description='Run CNN training on patches with a few different hyperparameter sets.')

Definition at line 2 of file train_cnn_continue.py.

train_cnn_continue.PATCH_SIZE_D

Definition at line 56 of file train_cnn_continue.py.

train_cnn_continue.PATCH_SIZE_W

Definition at line 56 of file train_cnn_continue.py.

train_cnn_continue.score

Initial value:

= model.evaluate({'main_input': X_test},
                       {'em_trk_none_netout': EmTrkNone_test, 'michel_netout': Michel_test},
                       verbose=0)

Definition at line 145 of file train_cnn_continue.py.

train_cnn_continue.sgd = SGD(lr=0.005, decay=1e-5, momentum=0.9, nesterov=True)

Definition at line 68 of file train_cnn_continue.py.

list train_cnn_continue.subdirs = [f for f in os.listdir(CNN_INPUT_DIR) if 'training' in f]

Definition at line 81 of file train_cnn_continue.py.

train_cnn_continue.X_test = np.zeros((n_testing, PATCH_SIZE_W, PATCH_SIZE_D, 1), dtype=np.float32)

Definition at line 101 of file train_cnn_continue.py.

train_cnn_continue.X_train = np.zeros((n_training, PATCH_SIZE_W, PATCH_SIZE_D, 1), dtype=np.float32)

Definition at line 75 of file train_cnn_continue.py.