def multi_training_plots(timestamps, legend_loc='upper right'):
"""
Compare the loss and accuracy metrics for a timestamped training.
Parameters
----------
timestamps : str, or list of strs
Configuration dict
legend_loc: str
Legend position
"""
if timestamps is str:
timestamps = [timestamps]
fig, axs = plt.subplots(2, 2, figsize=(16, 16))
axs = axs.flatten()
for ts in timestamps:
# Set the timestamp
paths.timestamp = ts
# Load training statistics
stats_path = os.path.join(paths.get_stats_dir(), 'stats.json')
with open(stats_path) as f:
stats = json.load(f)
# Load training configuration
conf_path = os.path.join(paths.get_conf_dir(), 'conf.json')
with open(conf_path) as f:
conf = json.load(f)
# Training
axs[0].plot(stats['epoch'], stats['loss'], label=ts)
axs[1].plot(stats['epoch'], stats['acc'], label=ts)
# Validation
if conf['training']['use_validation']:
axs[2].plot(stats['epoch'], stats['val_loss'], label=ts)
axs[3].plot(stats['epoch'], stats['val_acc'], label=ts)
axs[1].set_ylim([0, 1])
axs[3].set_ylim([0, 1])
for i in range(4):
axs[0].set_xlabel('Epochs')
axs[0].set_title('Training Loss')
axs[1].set_title('Training Accuracy')
axs[2].set_title('Validation Loss')
axs[3].set_title('Validation Accuracy')
axs[0].legend(loc=legend_loc)
def save_conf(conf):
"""
Save CONF to a txt file to ease the reading and to a json file to ease the parsing.
Parameters
----------
conf : 1-level nested dict
"""
save_dir = paths.get_conf_dir()
# Save dict as json file
with open(os.path.join(save_dir, 'conf.json'), 'w') as outfile:
json.dump(conf, outfile, sort_keys=True, indent=4)
# Save dict as txt file for easier redability
txt_file = open(os.path.join(save_dir, 'conf.txt'), 'w')
txt_file.write("{:<25}{:<30}{:<30} \n".format('group', 'key', 'value'))
txt_file.write('=' * 75 + '\n')
for key, val in sorted(conf.items()):
for g_key, g_val in sorted(val.items()):
txt_file.write("{:<25}{:<30}{:<15} \n".format(key, g_key, str(g_val)))
txt_file.write('-' * 75 + '\n')
txt_file.close()
# if location == "no":
# return False
#use_lo = use_location() #No lo necesitas, porque el método ya está entrenado con localización y guardado en su configuración
TIMESTAMP = input("Indica el timestamp. Sin espacios. Mismo formato que en models: ") # timestamp of the model
MODEL_NAME = input("Indica el nombre del modelo que se encuentra en ckpts. Sin espacios: ") # model to use to make the prediction
#TOP_K = input("Indica el numero de top K: ") # number of top classes predictions to save
TOP_K = 8
# Set the timestamp
paths.timestamp = TIMESTAMP
# Load the data
print(paths.get_ts_splits_dir())
class_names = load_class_names(splits_dir=paths.get_ts_splits_dir()) # INCISO: Estas son las clases que había en el modelo
# en el momento en el que estrenaste (dado por el timestamp). No las que tienes en data/dataset_files
# Load training configuration
conf_path = os.path.join(paths.get_conf_dir(), 'conf.json')
with open(conf_path) as f:
conf = json.load(f)
#print(conf(['training']['use_location']))
print(type(conf))
use_lo = conf['training']['use_location']
if use_lo:
print("Estás usando un modelo con localización, necesitas que los splits de validación, train etc que uses tengan dicha etiqueta")
# Load the model
#print("--------------------------------------------------------------------------------------------------------------------------------------------------------------------------antes")
#model = load_model(os.path.join(paths.get_checkpoints_dir(), MODEL_NAME),custom_objects={'customAdam':customAdam },compile=False)
#OJO: uso compile = false porque al parecer los checkpoints intermedios que se guardan
#están en otro formato que al hacer load_model hace que no se reconozca el customAdam. Como para predecir no es necesaria
#la compilación, y en caso de que lo fuera se puede compilar después, la evitamos de momento.
model = load_model(os.path.join(paths.get_checkpoints_dir(), MODEL_NAME), custom_objects=utils.get_custom_objects(), compile=False)
#print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++después")