Functions
def	save_model (model, name)

def	generate_data_generator (generator, X, Y1, Y2, b)

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...

	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_classes = config['training_on_patches']['nb_classes']

	nb_epoch = config['training_on_patches']['nb_epoch']

int	nb_pool = 2

string	cfg_name = 'sgd_lorate'

int	nb_filters1 = 48

int	nb_conv1 = 5

string	convactfn1 = 'relu'

bool	maxpool = False

int	nb_filters2 = 0

int	nb_conv2 = 7

string	convactfn2 = 'relu'

float	drop1 = 0.2

int	densesize1 = 128

string	actfn1 = 'relu'

int	densesize2 = 32

string	actfn2 = 'relu'

float	drop2 = 0.2

	main_input = Input(shape=(img_rows, img_cols, 1), name='main_input')
	CNN definition ############################. More...

	x

	em_trk_none = Dense(3, activation='softmax', name='em_trk_none_netout')(x)

	michel = Dense(1, activation='sigmoid', name='michel_netout')(x)

	sgd = SGD(lr=0.01, decay=1e-5, momentum=0.9, nesterov=True)

	model = Model(inputs=[main_input], outputs=[em_trk_none, michel])

	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)

	datagen
	training ############################### More...

	h

	score

Function Documentation

def train_cnn_augmented_data.generate_data_generator	(	generator,
		X,
		Y1,
		Y2,
		b
	)

Definition at line 196 of file train_cnn_augmented_data.py.

 def generate_data_generator(generator, X, Y1, Y2, b):
     genY1 = generator.flow(X, Y1, batch_size=b, seed=7)
     genY2 = generator.flow(X, Y2, batch_size=b, seed=7)
     while True:
             g1 = genY1.next()
             g2 = genY2.next()
             yield {'main_input': g1[0]}, {'em_trk_none_netout': g1[1], 'michel_netout': g2[1]}
 

def train_cnn_augmented_data.save_model	(	model,
		name
	)

Definition at line 36 of file train_cnn_augmented_data.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

string train_cnn_augmented_data.actfn1 = 'relu'

Definition at line 77 of file train_cnn_augmented_data.py.

string train_cnn_augmented_data.actfn2 = 'relu'

Definition at line 79 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.args = parser.parse_args()

Definition at line 6 of file train_cnn_augmented_data.py.

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

Definition at line 54 of file train_cnn_augmented_data.py.

string train_cnn_augmented_data.cfg_name = 'sgd_lorate'

Definition at line 60 of file train_cnn_augmented_data.py.

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

Definition at line 49 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.config = read_config(args.config)

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

Definition at line 47 of file train_cnn_augmented_data.py.

string train_cnn_augmented_data.convactfn1 = 'relu'

Definition at line 65 of file train_cnn_augmented_data.py.

string train_cnn_augmented_data.convactfn2 = 'relu'

Definition at line 71 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.datagen

Initial value:

 = ImageDataGenerator(
                 featurewise_center=False, samplewise_center=False,
                 featurewise_std_normalization=False,
                 samplewise_std_normalization=False,
                 zca_whitening=False,
                 rotation_range=0, width_shift_range=0, height_shift_range=0,
                 horizontal_flip=True, # randomly flip images
                 vertical_flip=False)

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

Definition at line 186 of file train_cnn_augmented_data.py.

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

Definition at line 143 of file train_cnn_augmented_data.py.

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

Definition at line 146 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.default

Definition at line 3 of file train_cnn_augmented_data.py.

int train_cnn_augmented_data.densesize1 = 128

Definition at line 76 of file train_cnn_augmented_data.py.

int train_cnn_augmented_data.densesize2 = 32

Definition at line 78 of file train_cnn_augmented_data.py.

float train_cnn_augmented_data.drop1 = 0.2

Definition at line 73 of file train_cnn_augmented_data.py.

float train_cnn_augmented_data.drop2 = 0.2

Definition at line 80 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.em_trk_none = Dense(3, activation='softmax', name='em_trk_none_netout')(x)

Definition at line 118 of file train_cnn_augmented_data.py.

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

Definition at line 156 of file train_cnn_augmented_data.py.

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

Definition at line 130 of file train_cnn_augmented_data.py.

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

Definition at line 139 of file train_cnn_augmented_data.py.

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

Definition at line 142 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.h

Initial value:

 = model.fit_generator(
               generate_data_generator(datagen, X_train, EmTrkNone_train, Michel_train, b=batch_size),
               validation_data=(
                   {'main_input': X_test},
                   {'em_trk_none_netout': EmTrkNone_test, 'michel_netout': Michel_test}),
               steps_per_epoch=X_train.shape[0]/batch_size, epochs=nb_epoch,
               verbose=1)

Definition at line 205 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.help

Definition at line 3 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.img_cols

Definition at line 52 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.img_rows

Definition at line 52 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.loss

Definition at line 124 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.loss_weights

Definition at line 125 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.main_input = Input(shape=(img_rows, img_cols, 1), name='main_input')

CNN definition ############################.

Definition at line 85 of file train_cnn_augmented_data.py.

bool train_cnn_augmented_data.maxpool = False

Definition at line 67 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.michel = Dense(1, activation='sigmoid', name='michel_netout')(x)

Definition at line 119 of file train_cnn_augmented_data.py.

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

Definition at line 157 of file train_cnn_augmented_data.py.

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

Definition at line 131 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.model = Model(inputs=[main_input], outputs=[em_trk_none, michel])

Definition at line 122 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.n = dataY.shape[0]

Definition at line 147 of file train_cnn_augmented_data.py.

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

Definition at line 154 of file train_cnn_augmented_data.py.

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

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

Definition at line 128 of file train_cnn_augmented_data.py.

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

Definition at line 55 of file train_cnn_augmented_data.py.

int train_cnn_augmented_data.nb_conv1 = 5

Definition at line 64 of file train_cnn_augmented_data.py.

int train_cnn_augmented_data.nb_conv2 = 7

Definition at line 70 of file train_cnn_augmented_data.py.

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

Definition at line 56 of file train_cnn_augmented_data.py.

int train_cnn_augmented_data.nb_filters1 = 48

Definition at line 63 of file train_cnn_augmented_data.py.

int train_cnn_augmented_data.nb_filters2 = 0

Definition at line 69 of file train_cnn_augmented_data.py.

int train_cnn_augmented_data.nb_pool = 2

Definition at line 58 of file train_cnn_augmented_data.py.

int train_cnn_augmented_data.ntot = 0

Definition at line 134 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.optimizer

Definition at line 123 of file train_cnn_augmented_data.py.

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

Definition at line 2 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.PATCH_SIZE_D

Definition at line 51 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.PATCH_SIZE_W

Definition at line 51 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.score

Initial value:

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

Definition at line 217 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.sgd = SGD(lr=0.01, decay=1e-5, momentum=0.9, nesterov=True)

Definition at line 121 of file train_cnn_augmented_data.py.

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

Definition at line 135 of file train_cnn_augmented_data.py.

train_cnn_augmented_data.x

Initial value:

 = Conv2D(nb_filters1, (nb_conv1, nb_conv1),
                    padding='valid', data_format='channels_last',
                    activation=LeakyReLU())(main_input)

Definition at line 88 of file train_cnn_augmented_data.py.

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

Definition at line 155 of file train_cnn_augmented_data.py.

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

Definition at line 129 of file train_cnn_augmented_data.py.

Functions

Variables

Function Documentation

Variable Documentation