def save_default_imagenet_model():
"""
Create a model in models_dir with default ImageNet training
"""
CONF = config.get_conf_dict()
TIMESTAMP = 'default_imagenet'
# Clear default conf and create custom conf
for k, v in CONF.items():
if k in ['general', 'augmentation']:
continue
for i, j in v.items():
CONF[k][i] = None
CONF['augmentation']['train_mode'] = None
CONF['model']['modelname'] = 'Xception'
CONF['model']['image_size'] = 224
CONF['model']['preprocess_mode'] = model_modes[CONF['model']['modelname']]
CONF['model']['num_classes'] = 1000
CONF['dataset']['mean_RGB'] = [123.675, 116.28, 103.53]
CONF['dataset']['std_RGB'] = [58.395, 57.12, 57.375]
paths.timestamp = TIMESTAMP
paths.CONF = CONF
# Create classes.txt for ImageNet
fpath = keras.utils.get_file(
'imagenet_class_index.json',
'https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json',
cache_subdir='models',
file_hash='c2c37ea517e94d9795004a39431a14cb')
with open(fpath) as f:
classes = json.load(f)
classes = np.array(list(classes.values()))[:, 1]
# Create the model
architecture = getattr(applications, CONF['model']['modelname'])
img_width, img_height = CONF['model']['image_size'], CONF['model'][
'image_size']
model = architecture(weights='imagenet',
include_top=True,
input_shape=(img_width, img_height, 3))
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
# Save everything
utils.create_dir_tree()
np.savetxt(os.path.join(paths.get_ts_splits_dir(), 'classes.txt'),
classes,
fmt='%s',
delimiter='/n')
save_conf(CONF)
model.save(fpath=os.path.join(paths.get_checkpoints_dir(),
'final_model.h5'),
include_optimizer=False)
'epoch': history.epoch,
'training time (s)': round(time.time() - t0, 2),
'timestamp': TIMESTAMP
}
stats.update(history.history)
stats = json_friendly(stats)
stats_dir = paths.get_stats_dir()
with open(os.path.join(stats_dir, 'stats.json'), 'w') as outfile:
json.dump(stats, outfile, sort_keys=True, indent=4)
print('Saving the configuration ...')
model_utils.save_conf(CONF)
print('Saving the model to h5...')
fpath = os.path.join(paths.get_checkpoints_dir(), 'final_model.h5')
model.save(fpath, include_optimizer=False)
# print('Saving the model to protobuf...')
# fpath = os.path.join(paths.get_checkpoints_dir(), 'final_model.proto')
# model_utils.save_to_pb(model, fpath)
print('Finished')
if __name__ == '__main__':
CONF = config.get_conf_dict()
timestamp = datetime.now().strftime('%Y-%m-%d_%H%M%S')
train_fn(TIMESTAMP=timestamp, CONF=CONF)