def run(self,
finetune_layers,
epochs,
learning_rate=0.01,
batch_size=128,
keep_prob=1.0,
memory_usage=1.0,
device='/gpu:0',
save_ckpt_dir='',
init_ckpt_file='',
use_adam_optimizer=False):
"""
Run a training on part of the model (retrain/finetune)
Args:
finetune_layers:
epochs:
learning_rate:
batch_size:
keep_prob:
memory_usage:
device:
show_misclassified:
validate_on_each_epoch:
save_ckpt_dir:
init_ckpt_file:
"""
# create datasets
data_train = self.create_dataset(is_training=True)
data_val = self.create_dataset(is_training=False)
# Get ops to init the dataset iterators and get a next batch
init_train_iterator_op, init_val_iterator_op, get_next_batch_op = ops.get_dataset_ops(
data_train,
data_val,
batch_size,
self.data['training_count'],
self.data['validation_count'],
shuffle=True)
# Initialize model and create input placeholders
with tf.device(device):
ph_images = tf.placeholder(tf.float32, [
None, self.model_def.image_size, self.model_def.image_size, 3
])
ph_labels = tf.placeholder(tf.float32, [None, self.num_classes])
# Could set the first placholder dimension to batch_size, but this wouldn't work with leftover data that not form a whole batch
ph_keep_prob = tf.placeholder(tf.float32)
model = self.model_def(ph_images,
keep_prob=ph_keep_prob,
num_classes=self.num_classes,
retrain_layer=finetune_layers)
final_op = model.get_final_op()
# Get a list with all trainable variables and print infos for the current run
retrain_vars = model.get_retrain_vars()
restore_vars = model.get_restore_vars()
self.print_infos(retrain_vars, restore_vars, learning_rate, batch_size,
keep_prob, use_adam_optimizer)
# Add/Get the different operations to optimize (loss, train and validate)
with tf.device(device):
loss_op = ops.get_loss_op(final_op, ph_labels)
train_op = ops.get_train_op(loss_op, learning_rate, retrain_vars,
use_adam_optimizer)
accuracy_op, correct_prediction_op, predicted_index_op, true_index_op = ops.get_validation_ops(
final_op, ph_labels)
# Get the number of training/validation steps per epoch to get through all images
batches_per_epoch_train = int(
math.ceil(self.data['training_count'] / (batch_size + 0.0)))
batches_per_epoch_val = int(
math.ceil(self.data['validation_count'] / (batch_size + 0.0)))
# Initialize a saver, create a session config and start a session
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = memory_usage
with tf.Session(config=config) as sess:
# Init all variables
sess.run(tf.global_variables_initializer())
# Load the pretrained variables or a saved checkpoint
if init_ckpt_file:
saver.restore(sess, init_ckpt_file)
else:
model.load_initial_weights(sess)
utils.print_output_header(self.data['training_count'],
self.data['validation_count'])
for epoch in range(epochs):
is_last_epoch = True if (epoch + 1) == epochs else False
train_loss, train_acc = utils.run_training(
sess, train_op, loss_op, accuracy_op,
init_train_iterator_op, get_next_batch_op, ph_images,
ph_labels, ph_keep_prob, keep_prob,
batches_per_epoch_train)
return_misclassified = is_last_epoch
test_loss, test_acc, misclassified = utils.run_validation(
sess, loss_op, accuracy_op, correct_prediction_op,
predicted_index_op, true_index_op, final_op,
init_val_iterator_op, get_next_batch_op, ph_images,
ph_labels, ph_keep_prob, batches_per_epoch_val,
return_misclassified)
utils.print_output_epoch(epoch + 1, train_loss, train_acc,
test_loss, test_acc)
# show missclassified list on last epoch
if is_last_epoch:
utils.print_misclassified(sess, misclassified,
self.data['labels'])
# save session in a checkpoint file
if self.write_checkpoints or is_last_epoch:
utils.save_session_to_checkpoint_file(
sess, saver, epoch, save_ckpt_dir)
def run(self,
layer_inputs,
epochs,
learning_rate=0.01,
batch_size=128,
keep_prob=1.0,
memory_usage=1.0,
device='/gpu:0',
save_ckpt_dir='',
init_ckpt_file='',
use_adam_optimizer=False,
shuffle=True,
use_regularizer=False):
"""
Run training
Args:
epochs:
learning_rate:
batch_size:
keep_prob:
memory_usage:
device:
show_misclassified:
validate_on_each_epoch:
save_ckpt_dir:
init_ckpt_file:
"""
# create datasets
data_train = self.create_dataset(is_training=True)
data_val = self.create_dataset(is_training=False)
# Get ops to init the dataset iterators and get a next batch
init_train_iterator_op, init_val_iterator_op, get_next_batch_op = ops.get_dataset_ops(
data_train,
data_val,
batch_size,
train_size=self.data['training_count'],
val_size=self.data['validation_count'],
shuffle=shuffle)
# Initialize model and create input placeholders
with tf.device(device):
ph_keep_prob = tf.placeholder(tf.float32)
ph_data, ph_labels, final_op = self.create_model(
layer_inputs, keep_prob, use_regularizer)
# Get a list with all trainable variables and print infos for the current run
train_vars = tf.trainable_variables()
self.print_infos(train_vars, learning_rate, batch_size, keep_prob,
use_adam_optimizer)
# Add/Get the different operations to optimize (loss, train and validate)
with tf.device(device):
loss_op = ops.get_loss_op(final_op, ph_labels)
train_op = ops.get_train_op(loss_op, learning_rate, train_vars,
use_adam_optimizer)
accuracy_op, correct_prediction_op, predicted_index_op, true_index_op = ops.get_validation_ops(
final_op, ph_labels)
# Get the number of training/validation steps per epoch to get through all images
batches_per_epoch_train = int(
math.ceil(self.data['training_count'] / (batch_size + 0.0)))
batches_per_epoch_val = int(
math.ceil(self.data['validation_count'] / (batch_size + 0.0)))
# Initialize a saver, create a session config and start a session
saver = tf.train.Saver()
gpu_options = tf.GPUOptions()
gpu_options.per_process_gpu_memory_fraction = memory_usage
gpu_options.visible_device_list = "1"
config = tf.ConfigProto(gpu_options=gpu_options)
server = tf.train.Server.create_local_server(config=config)
with tf.Session(target=server.target) as sess:
# Init all variables
sess.run(tf.global_variables_initializer())
# Load the pretrained variables or a saved checkpoint
if init_ckpt_file:
saver.restore(sess, init_ckpt_file)
utils.print_output_header(self.data['training_count'],
self.data['validation_count'])
for epoch in range(epochs):
is_last_epoch = True if (epoch + 1) == epochs else False
train_loss, train_acc = utils.run_training(
sess, train_op, loss_op, accuracy_op,
init_train_iterator_op, get_next_batch_op, ph_data,
ph_labels, ph_keep_prob, keep_prob,
batches_per_epoch_train)
return_misclassified = is_last_epoch
test_loss, test_acc, misclassified = utils.run_validation(
sess, loss_op, accuracy_op, correct_prediction_op,
predicted_index_op, true_index_op, final_op,
init_val_iterator_op, get_next_batch_op, ph_data,
ph_labels, ph_keep_prob, batches_per_epoch_val,
return_misclassified)
utils.print_output_epoch(epoch + 1, train_loss, train_acc,
test_loss, test_acc)
# show missclassified list on last epoch
if is_last_epoch:
utils.print_misclassified(sess, misclassified,
self.data['labels'])
# save session in a checkpoint file
if self.write_checkpoints or is_last_epoch:
utils.save_session_to_checkpoint_file(
sess, saver, epoch, save_ckpt_dir)
