def run_ctc():
# Ruta del archivo de configuración, pasada por argumentos o por defecto "./config.json".
if len(sys.argv) == 1:
print(
"Execution without arguments, config file by default: ./config.json"
)
config_file = str('./config.json')
elif len(sys.argv) == 2:
print("Execution with arguments, config file:" + str(sys.argv[1]))
config_file = str(sys.argv[1])
else:
print()
print("ERROR")
print("Wrong number of arguments. Execute:")
print(">> python3 train.py [path_config_file]")
exit(1)
# Cargamos del archivo de configuración
try:
config = json.load(open(config_file))
except FileNotFoundError:
print()
print("ERROR")
print("No such config file : " + config_file)
exit(1)
# Si el directorio destino no existe, se crea.
if not os.path.exists(str(config["IAM-test"]["results_path"])):
os.mkdir(str(config["IAM-test"]["results_path"]))
if not os.path.exists(str(config["IAM-test"]["checkpoints_path"])):
os.mkdir(str(config["IAM-test"]["checkpoints_path"]))
# Extraemos las variables generales para el entrenamiento.
im_path = str(config["general"]["processed_data_path"])
csv_path = str(config["IAM-test"]["csv_path"])
results_path = str(config["IAM-test"]["results_path"])
checkpoints_path = str(config["IAM-test"]["checkpoints_path"])
batch_size = int(config["IAM-test"]["batch_size"])
num_epochs = int(config["IAM-test"]["num_epochs"])
val_period = int(config["IAM-test"]["validation_period"])
print_period = int(config["IAM-test"]["print_period"])
height = int(config["general"]["height"])
width = int(config["general"]["width"])
dct = ast.literal_eval(str(config["general"]["dictionary"]))
# Extraemos los parametros del modelo a validar
kernel_size = int(config["cnn-rnn-ctc"]["kernel_size"])
num_conv1 = int(config["cnn-rnn-ctc"]["num_conv1"])
num_conv2 = int(config["cnn-rnn-ctc"]["num_conv2"])
num_conv3 = int(config["cnn-rnn-ctc"]["num_conv3"])
num_conv4 = int(config["cnn-rnn-ctc"]["num_conv4"])
num_conv5 = int(config["cnn-rnn-ctc"]["num_conv5"])
num_rnn = int(config["cnn-rnn-ctc"]["num_rnn"])
num_fc = int(config["cnn-rnn-ctc"]["num_fc"])
num_classes = int(config["cnn-rnn-ctc"]["num_classes"])
ctc_input_len = int(config["cnn-rnn-ctc"]["ctc_input_len"])
# Creamos el modelo ANN
model = ANN_model.CNN_RNN_CTC(kernel_size, num_conv1, num_conv2, num_conv3,
num_conv4, num_conv5, num_rnn, num_fc,
height, width, num_classes)
graph = model[0]
inputs = model[1]
targets = model[2]
keep_prob = model[3]
seq_len = model[4]
optimizer = model[5]
cost = model[6]
ler = model[7]
decoded = model[8]
# Declaramos el DataFrames para almacenar el resultado del entrenamiento y la validación.
train_result = pd.DataFrame()
val_result1 = pd.DataFrame()
val_result2 = pd.DataFrame()
# Creamos la sesión con el modelo previamente cargado.
with tf.Session(graph=graph) as session:
# Guardamos la arquitectura del modelo e inicializamos sus vaiables
saver = tf.train.Saver()
tf.global_variables_initializer().run()
# Inicializamos el LER a 1.
LER = 1.0
# Bucle de épocas.
for curr_epoch in range(num_epochs):
# Extraemos un lote aleatorio del Dataset de entrenamiento.
train_inputs, train_targets, original, train_seq_len = hw_utils.extract_training_batch(
ctc_input_len, batch_size, im_path, csv_path + "train.csv")
feed = {
inputs: train_inputs,
targets: train_targets,
keep_prob: 0.5,
seq_len: train_seq_len
}
# Ejecutamos "optimizer", minimizando el error para el lote extraido.
_ = session.run([optimizer], feed)
# Comprobamos el periodo de validación para el modelo.
if curr_epoch % val_period == 0:
# Calculamos el error de la CTC y el LER para el dataset de entrenamiento y almacenamos a los resultados.
train_cost, train_ler = session.run([cost, ler], feed)
train_tuple = {
'epoch': [curr_epoch],
'train_cost': [train_cost],
'train_ler': [train_ler]
}
train_result = pd.concat(
[train_result, pd.DataFrame(train_tuple)])
# Realizamos la validación del modelo para los dos Datasets de validación y almacenamos los resultados
val_tuple1 = hw_utils.validation(curr_epoch, ctc_input_len,
batch_size, im_path,
csv_path + "validation1.csv",
inputs, targets, keep_prob,
seq_len, session, cost, ler)
val_result1 = pd.concat(
[val_result1, pd.DataFrame(val_tuple1)])
val_tuple2 = hw_utils.validation(curr_epoch, ctc_input_len,
batch_size, im_path,
csv_path + "validation2.csv",
inputs, targets, keep_prob,
seq_len, session, cost, ler)
val_result2 = pd.concat(
[val_result2, pd.DataFrame(val_tuple2)])
# Comprobamos si se ha obtenido un LER menor al mínimo obtenido hasta el momento.
if (float(val_tuple1['val_ler'][0]) +
float(val_tuple2['val_ler'][0])) / 2 <= LER:
# Almacenamos el valor de las variables.
save_path = saver.save(
session, checkpoints_path + "checkpoint_epoch_" +
str(curr_epoch) + "_ler_" + str(
(float(val_tuple1['val_ler'][0]) +
float(val_tuple2['val_ler'][0])) / 2) + ".ckpt")
print("Model saved in file: " + str(save_path))
# Actualizamos el valor mínimo de LER.
LER = (float(val_tuple1['val_ler'][0]) +
float(val_tuple2['val_ler'][0])) / 2
# Comprobamos el periodo de impresión de ejemplos.
if curr_epoch % print_period == 0:
# Imprimimos el error cometido para el Dataset de validación en la época actual.
print("Epoch: " + str(curr_epoch) + " val_cost: " +
str((float(val_tuple1['val_cost'][0]) +
float(val_tuple2['val_cost'][0])) / 2) +
" val_ler: " +
str((float(val_tuple1['val_ler'][0]) +
float(val_tuple2['val_ler'][0])) / 2))
# Imprimimos la salida del modelo para 10 ejemplos al azar del dataset de validación.
print("Examples:")
for j in range(10):
# Extraemos una muestra.
prob_inputs, prob_targets, prob_original, prob_seq_len, _ = hw_utils.extract_ordered_batch(
ctc_input_len, 1, im_path,
csv_path + "validation1.csv", randint(0, 6086))
prob_feed = {
inputs: prob_inputs,
targets: prob_targets,
keep_prob: 1,
seq_len: prob_seq_len
}
# Obtenemos la salida y la mapeamos como una palabra para imprimirla por pantalla.
prob_d = session.run(decoded[0], feed_dict=prob_feed)
output = str(list(map(dct.get, list(prob_d.values))))
for ch in ["['", "']", "', '"]:
output = output.replace(ch, "")
prob_original = str(prob_original).replace(ch, "")
print("Target: " + prob_original +
" Model Output: " + output)
# Almacenamos los resultados
val_result1.to_csv(results_path + "validation_result1.csv",
index=False)
val_result2.to_csv(results_path + "validation_result2.csv",
index=False)
train_result.to_csv(results_path + "training_result.csv", index=False)
print("THE TRAINING IS OVER")
def run_ctc():
if len(sys.argv) == 1:
print(
"Execution without arguments, config file by default: ./config.json"
)
config_file = str('./config.json')
elif len(sys.argv) == 2:
print("Execution with arguments, config file:" + str(sys.argv[1]))
config_file = str(sys.argv[1])
else:
print()
print("ERROR")
print("Wrong number of arguments. Execute:")
print(">> python3 test.py [path_config_file]")
exit(1)
try:
config = json.load(open(config_file))
except FileNotFoundError:
print()
print("ERROR")
print("No such config file : " + config_file)
exit(1)
if not os.path.exists(str(config["IAM-test"]["results_path"])):
os.mkdir(str(config["IAM-test"]["results_path"]))
im_path = str(config["general"]["processed_data_path"])
csv_path = str(config["IAM-test"]["csv_path"])
results_path = str(config["IAM-test"]["results_path"])
checkpoints_path = str(config["IAM-test"]["checkpoints_path"])
height = int(config["general"]["height"])
width = int(config["general"]["width"])
dct = ast.literal_eval(str(config["general"]["dictionary"]))
kernel_size = int(config["cnn-rnn-ctc"]["kernel_size"])
num_conv1 = int(config["cnn-rnn-ctc"]["num_conv1"])
num_conv2 = int(config["cnn-rnn-ctc"]["num_conv2"])
num_conv3 = int(config["cnn-rnn-ctc"]["num_conv3"])
num_conv4 = int(config["cnn-rnn-ctc"]["num_conv4"])
num_conv5 = int(config["cnn-rnn-ctc"]["num_conv5"])
num_rnn = int(config["cnn-rnn-ctc"]["num_rnn"])
num_fc = int(config["cnn-rnn-ctc"]["num_fc"])
num_classes = int(config["cnn-rnn-ctc"]["num_classes"])
ctc_input_len = int(config["cnn-rnn-ctc"]["ctc_input_len"])
model = ANN_model.CNN_RNN_CTC(kernel_size, num_conv1, num_conv2, num_conv3,
num_conv4, num_conv5, num_rnn, num_fc,
height, width, num_classes)
graph = model[0]
inputs = model[1]
targets = model[2]
keep_prob = model[3]
seq_len = model[4]
cost = model[6]
ler = model[7]
decoded = model[8]
result_test = pd.DataFrame()
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC'
with tf.Session(graph=graph, config=config) as session:
saver = tf.train.Saver()
saver.restore(
session,
"C:/Users/catarina/QRCodeDetection/Offline-Handwriting-Recognition-with-TensorFlow/Checkpoints/checkpoint_epoch_27400_ler_0.15915638256412773.ckpt"
) #hardcodded change it
print("Loaded Model")
cont = 0
total_test_cost = 0
total_test_ler = 0
while cont >= 0:
test_inputs, test_targets, original, test_seq_len, num_samples = hw_utils.extract_ordered_batch(
ctc_input_len, 1, im_path, csv_path + "test_tickets.csv", cont)
if num_samples == 1:
test_feed = {
seq_len: test_seq_len,
inputs: test_inputs,
keep_prob: 1,
targets: test_targets
}
test_cost, test_ler = session.run([cost, ler], test_feed)
total_test_cost += test_cost
total_test_ler += test_ler
dec = session.run(decoded[0], test_feed)
output = str(list(map(dct.get, list(dec.values))))
for ch in ["['", "']", "', '"]:
output = output.replace(ch, "")
original = str(original).replace(ch, "")
tuple = {'Target': [original], 'Output': [output]}
result_test = pd.concat([result_test, pd.DataFrame(tuple)])
cont += 1
else:
print("IAM test result:")
print("Cost: " + str(total_test_cost / (cont)))
print("LER: " + str(total_test_ler / (cont)))
result_test.to_csv(results_path + "test_result_tickets.csv",
index=False)
cont = -1
def run_ctc():
# Ruta del archivo de configuración, pasada por argumentos o por defecto "./config.json".
if len(sys.argv) == 1:
print(
"Execution without arguments, config file by default: ./config.json"
)
config_file = str('./config.json')
elif len(sys.argv) == 2:
print("Execution with arguments, config file:" + str(sys.argv[1]))
config_file = str(sys.argv[1])
else:
print()
print("ERROR")
print("Wrong number of arguments. Execute:")
print(">> python3 test.py [path_config_file]")
exit(1)
# Cargamos el archivo de configuración
try:
config = json.load(open(config_file))
except FileNotFoundError:
print()
print("ERROR")
print("No such config file : " + config_file)
exit(1)
# Si el directorio destino no existe, se crea.
if not os.path.exists(str(config["IAM-test"]["results_path"])):
os.mkdir(str(config["IAM-test"]["results_path"]))
# Extraemos las variables generales para el test.
im_path = str(config["general"]["processed_data_path"])
csv_path = str(config["IAM-test"]["csv_path"])
results_path = str(config["IAM-test"]["results_path"])
checkpoints_path = str(config["IAM-test"]["checkpoints_path"])
height = int(config["general"]["height"])
width = int(config["general"]["width"])
dct = ast.literal_eval(str(config["general"]["dictionary"]))
# Extraemos los parametros del modelo a validar
kernel_size = int(config["cnn-rnn-ctc"]["kernel_size"])
num_conv1 = int(config["cnn-rnn-ctc"]["num_conv1"])
num_conv2 = int(config["cnn-rnn-ctc"]["num_conv2"])
num_conv3 = int(config["cnn-rnn-ctc"]["num_conv3"])
num_conv4 = int(config["cnn-rnn-ctc"]["num_conv4"])
num_conv5 = int(config["cnn-rnn-ctc"]["num_conv5"])
num_rnn = int(config["cnn-rnn-ctc"]["num_rnn"])
num_fc = int(config["cnn-rnn-ctc"]["num_fc"])
num_classes = int(config["cnn-rnn-ctc"]["num_classes"])
ctc_input_len = int(config["cnn-rnn-ctc"]["ctc_input_len"])
# Creamos el modelo ANN
model = ANN_model.CNN_RNN_CTC(kernel_size, num_conv1, num_conv2, num_conv3,
num_conv4, num_conv5, num_rnn, num_fc,
height, width, num_classes)
graph = model[0]
inputs = model[1]
targets = model[2]
keep_prob = model[3]
seq_len = model[4]
cost = model[6]
ler = model[7]
decoded = model[8]
# Declaramos el DataFrames para almacenar la salidas del modelo para el Dataset completo.
result_test = pd.DataFrame()
# Creamos la sesión con el modelo previamente cargado.
with tf.Session(graph=graph) as session:
# Restauramos el valor de las variables del último modelo entrenado.
saver = tf.train.Saver()
saver.restore(session, tf.train.latest_checkpoint(checkpoints_path))
print("Loaded Model")
# Variables auxiliares.
cont = 0
total_test_cost = 0
total_test_ler = 0
# Bucle para realizar la validación sobre el Dataset completo
while cont >= 0:
# Extraemos las muestras 1 a 1 y de forma secuencial mediante "cont".
test_inputs, test_targets, original, test_seq_len, num_samples = hw_utils.extract_ordered_batch(
ctc_input_len, 1, im_path, csv_path + "test.csv", cont)
# Se ha conseguido extraer la muestra para testearla.
if num_samples == 1:
# Calculamos el error de la CTC y el LER para dicha muestra.
test_feed = {
seq_len: test_seq_len,
inputs: test_inputs,
keep_prob: 1,
targets: test_targets
}
test_cost, test_ler = session.run([cost, ler], test_feed)
total_test_cost += test_cost
total_test_ler += test_ler
# Obtenemos la salida del modelo y la mapeamos como una palabra para almacenarla junto a la palabra objetivo.
dec = session.run(decoded[0], test_feed)
output = str(list(map(dct.get, list(dec.values))))
for ch in ["['", "']", "', '"]:
output = output.replace(ch, "")
original = str(original).replace(ch, "")
tuple = {'Target': [original], 'Output': [output]}
result_test = pd.concat([result_test, pd.DataFrame(tuple)])
cont += 1
# No quedan más muestras en el Dataset de test.
else:
# Imprimimos los resultados del test y almacenamos las salidas del modelo.
print("IAM test result:")
print("Cost: " + str(total_test_cost / (cont)))
print("LER: " + str(total_test_ler / (cont)))
result_test.to_csv(results_path + "test_result.csv",
index=False)
cont = -1
def run_ctc():
if len(sys.argv) == 1:
print(
"Execution without arguments, config file by default: ./config.json"
)
config_file = str('./config.json')
elif len(sys.argv) == 2:
print("Execution with arguments, config file:" + str(sys.argv[1]))
config_file = str(sys.argv[1])
else:
print()
print("ERROR")
print("Wrong number of arguments. Execute:")
print(">> python3 train.py [path_config_file]")
exit(1)
try:
config = json.load(open(config_file))
except FileNotFoundError:
print()
print("ERROR")
print("No such config file : " + config_file)
exit(1)
if not os.path.exists(str(config["IAM-test"]["results_path"])):
os.mkdir(str(config["IAM-test"]["results_path"]))
if not os.path.exists(str(config["IAM-test"]["checkpoints_path"])):
os.mkdir(str(config["IAM-test"]["checkpoints_path"]))
im_path = str(config["general"]["processed_data_path"])
csv_path = str(config["IAM-test"]["csv_path"])
results_path = str(config["IAM-test"]["results_path"])
checkpoints_path = str(config["IAM-test"]["checkpoints_path"])
batch_size = int(config["IAM-test"]["batch_size"])
num_epochs = int(config["IAM-test"]["num_epochs"])
val_period = int(config["IAM-test"]["validation_period"])
print_period = int(config["IAM-test"]["print_period"])
height = int(config["general"]["height"])
width = int(config["general"]["width"])
dct = ast.literal_eval(str(config["general"]["dictionary"]))
kernel_size = int(config["cnn-rnn-ctc"]["kernel_size"])
num_conv1 = int(config["cnn-rnn-ctc"]["num_conv1"])
num_conv2 = int(config["cnn-rnn-ctc"]["num_conv2"])
num_conv3 = int(config["cnn-rnn-ctc"]["num_conv3"])
num_conv4 = int(config["cnn-rnn-ctc"]["num_conv4"])
num_conv5 = int(config["cnn-rnn-ctc"]["num_conv5"])
num_rnn = int(config["cnn-rnn-ctc"]["num_rnn"])
num_fc = int(config["cnn-rnn-ctc"]["num_fc"])
num_classes = int(config["cnn-rnn-ctc"]["num_classes"])
ctc_input_len = int(config["cnn-rnn-ctc"]["ctc_input_len"])
model = ANN_model.CNN_RNN_CTC(kernel_size, num_conv1, num_conv2, num_conv3,
num_conv4, num_conv5, num_rnn, num_fc,
height, width, num_classes)
graph = model[0]
inputs = model[1]
targets = model[2]
keep_prob = model[3]
seq_len = model[4]
optimizer = model[5]
cost = model[6]
ler = model[7]
decoded = model[8]
train_result = pd.DataFrame()
val_result1 = pd.DataFrame()
val_result2 = pd.DataFrame()
with tf.Session(graph=graph) as session:
saver = tf.train.Saver()
tf.global_variables_initializer().run()
LER = 1.0
for curr_epoch in range(num_epochs):
train_inputs, train_targets, original, train_seq_len = hw_utils.extract_training_batch(
ctc_input_len, batch_size, im_path, csv_path + "train.csv")
feed = {
inputs: train_inputs,
targets: train_targets,
keep_prob: 0.5,
seq_len: train_seq_len
}
_ = session.run([optimizer], feed)
if curr_epoch % val_period == 0:
train_cost, train_ler = session.run([cost, ler], feed)
train_tuple = {
'epoch': [curr_epoch],
'train_cost': [train_cost],
'train_ler': [train_ler]
}
train_result = pd.concat(
[train_result, pd.DataFrame(train_tuple)])
val_tuple1 = hw_utils.validation(curr_epoch, ctc_input_len,
batch_size, im_path,
csv_path + "validation1.csv",
inputs, targets, keep_prob,
seq_len, session, cost, ler)
val_result1 = pd.concat(
[val_result1, pd.DataFrame(val_tuple1)])
val_tuple2 = hw_utils.validation(curr_epoch, ctc_input_len,
batch_size, im_path,
csv_path + "validation2.csv",
inputs, targets, keep_prob,
seq_len, session, cost, ler)
val_result2 = pd.concat(
[val_result2, pd.DataFrame(val_tuple2)])
if (float(val_tuple1['val_ler'][0]) +
float(val_tuple2['val_ler'][0])) / 2 <= LER:
save_path = saver.save(
session, checkpoints_path + "checkpoint_epoch_" +
str(curr_epoch) + "_ler_" + str(
(float(val_tuple1['val_ler'][0]) +
float(val_tuple2['val_ler'][0])) / 2) + ".ckpt")
global_step = ckpt.checkpoints_path.split('/')[-1].split(
'data')[-1]
print("Model saved in file: " + str(save_path))
LER = (float(val_tuple1['val_ler'][0]) +
float(val_tuple2['val_ler'][0])) / 2
if curr_epoch % print_period == 0:
print("Epoch: " + str(curr_epoch) + " val_cost: " +
str((float(val_tuple1['val_cost'][0]) +
float(val_tuple2['val_cost'][0])) / 2) +
" val_ler: " +
str((float(val_tuple1['val_ler'][0]) +
float(val_tuple2['val_ler'][0])) / 2))
print("Examples:")
for j in range(10):
prob_inputs, prob_targets, prob_original, prob_seq_len, _ = hw_utils.extract_ordered_batch(
ctc_input_len, 1, im_path,
csv_path + "validation1.csv", randint(0, 6086))
prob_feed = {
inputs: prob_inputs,
targets: prob_targets,
keep_prob: 1,
seq_len: prob_seq_len
}
prob_d = session.run(decoded[0], feed_dict=prob_feed)
output = str(list(map(dct.get, list(prob_d.values))))
for ch in ["['", "']", "', '"]:
output = output.replace(ch, "")
prob_original = str(prob_original).replace(ch, "")
print("Target: " + prob_original +
" Model Output: " + output)
val_result1.to_csv(results_path + "validation_result1.csv",
index=False)
val_result2.to_csv(results_path + "validation_result2.csv",
index=False)
train_result.to_csv(results_path + "training_result.csv", index=False)
print("THE TRAINING IS OVER")