def restore_model(self):
file_name = os.path.splitext(os.path.abspath(self.model_name))[0]
num_files = len(
sorted(glob.glob(os.path.abspath(file_name + '*.meta'))))
if num_files > 0:
checkpoint_file = os.path.abspath(
sorted(glob.glob(file_name + '*.data-00000-of-00001'),
reverse=True)[0])
if os.path.exists(checkpoint_file):
print('Restoring model from %s' % checkpoint_file)
meta_file = os.path.abspath(
sorted(glob.glob(file_name + '*.meta'), reverse=True)[0])
print('Loading: %s' % meta_file)
saver = tf.train.import_meta_graph(meta_file)
print('Loading: %s' % os.path.abspath(checkpoint_file))
cpk = tf.train.latest_checkpoint(os.path.dirname(meta_file))
print('Checkpoint: ' + str(cpk))
print('Tensors')
print(
print_tensors_in_checkpoint_file(file_name=cpk,
all_tensors='',
tensor_name=''))
saver.restore(
self.session,
tf.train.latest_checkpoint(os.path.dirname(meta_file)))
print('Last epoch to restore: ' +
str(self.session.run(self.global_step)))
else:
file_utils.delete_all_files_in_dir(self.logging_dir)
print('Restoring cannot be done')
def create_graph(self, image_shape, num_classes):
start = time.time()
self.image_shape = image_shape
self.num_classes = num_classes
self.num_layers = len(self.config.keys()) - 1
self.global_step = tf.Variable(0,
name='last_successful_epoch',
trainable=False,
dtype=tf.int32)
self.last_epoch = tf.assign(self.global_step,
self.global_step + 1,
name='assign_updated_epoch')
# Step 1: Creating placeholders for inputs
self.make_placeholders_for_inputs()
# Step 2: Creating initial parameters for the variables
self.make_parameters()
# Step 3: Make predictions for the data
self.make_predictions()
# Step 4: Perform optimization operation
self.make_optimization()
# Step 5: Calculate accuracies
self.make_accuracy()
# Step 6: Initialize all the required variables
with tf.device(self.device):
self.init_var = tf.global_variables_initializer()
# Step 7: Initiate Session
config = tf.ConfigProto(
log_device_placement=True,
allow_soft_placement=True,
)
# config.gpu_options.allow_growth = True
# config.gpu_options.per_process_gpu_memory_fraction = 0.4
if self.session_type == 'default':
self.session = tf.Session(config=config)
if self.session_type == 'interactive':
self.session = tf.InteractiveSession(config=config)
print('Session: ' + str(self.session))
self.session.run(self.init_var)
# Step 8: Initiate logs
if self.logging is True:
file_utils.mkdir_p(self.logging_dir)
self.merged_summary_op = tf.summary.merge_all()
if self.restore is False:
file_utils.delete_all_files_in_dir(self.logging_dir)
self.summary_writer = \
tf.summary.FileWriter(self.logging_dir, graph=self.session.graph)
if self.save_checkpoint is True:
self.model = tf.train.Saver(max_to_keep=1)
# Step 9: Restore model
if self.restore is True:
self.restore_model()
epoch = self.session.run(self.global_step)
print('Model has been trained for %d iterations' % epoch)
end = time.time()
print('Tensorflow graph created in %.4f seconds' % (end - start))
return True
def start_session(self, graph):
# Step 1: Initialize all the required variables
with graph.as_default():
self.global_step = tf.Variable(0,
name='last_successful_epoch',
trainable=False,
dtype=tf.int32)
self.last_epoch = tf.assign(self.global_step,
self.global_step + 1,
name='assign_updated_epoch')
with tf.device(self.device):
self.init_var = tf.global_variables_initializer()
# Step 2: Initiate Session
config = tf.ConfigProto(
log_device_placement=True,
allow_soft_placement=True,
)
if self.session_type == 'default':
self.session = tf.Session(config=config, graph=graph)
if self.session_type == 'interactive':
self.session = tf.InteractiveSession(config=config, graph=graph)
print('Session: ' + str(self.session))
self.session.run(self.init_var)
# Step 3: Initiate logs
if self.logging is True:
file_utils.mkdir_p(self.logging_dir)
self.merged_summary_op = tf.summary.merge_all()
if self.restore is False:
file_utils.delete_all_files_in_dir(self.logging_dir)
self.summary_writer = \
tf.summary.FileWriter(self.logging_dir, graph=graph)
if self.save_model is True:
self.model = tf.train.Saver(max_to_keep=2)
# Step 4: Restore model
if self.restore is True:
self.restore_model()
epoch = self.session.run(self.global_step)
print('Model has been trained for %d iterations' % epoch)
return True
def fit(self,
data,
labels,
classes,
test_data=None,
test_labels=None,
test_classes=None):
if self.device == 'cpu':
print('Using CPU')
config = tf.ConfigProto(
log_device_placement=True,
allow_soft_placement=True,
#allow_growth=True,
#device_count={'CPU': 0}
)
else:
print('Using GPU')
config = tf.ConfigProto(
log_device_placement=True,
allow_soft_placement=True,
#allow_growth=True,
#device_count={'GPU': 0}
)
if self.session_type == 'default':
self.session = tf.Session(config=config)
if self.session_type == 'interactive':
self.session = tf.InteractiveSession(config=config)
print('Session: ' + str(self.session))
self.session.run(self.init_var)
if self.tensorboard_logs is True:
file_utils.mkdir_p(self.tensorboard_log_dir)
self.merged_summary_op = tf.summary.merge_all()
if self.restore is False:
file_utils.delete_all_files_in_dir(self.tensorboard_log_dir)
if self.separate_writer is False:
self.summary_writer = tf.summary.FileWriter(
self.tensorboard_log_dir, graph=self.session.graph)
else:
self.train_writer = tf.summary.FileWriter(
self.tensorboard_log_dir + 'train',
graph=self.session.graph)
if self.train_validate_split is not None:
self.validate_writer = tf.summary.FileWriter(
self.tensorboard_log_dir + 'validate',
graph=self.session.graph)
if self.test_log is True:
self.test_writer = tf.summary.FileWriter(
self.tensorboard_log_dir + 'test',
graph=self.session.graph)
if self.save_model is True:
self.model = tf.train.Saver(max_to_keep=5)
if self.train_validate_split is not None:
train_data, validate_data, train_labels, validate_labels, train_classes, validate_classes = \
train_test_split(data, labels, classes, train_size=self.train_validate_split)
if self.verbose is True:
print('Data shape: ' + str(data.shape))
print('Labels shape: ' + str(labels.shape))
print('Classes shape: ' + str(classes.shape))
print('Train Data shape: ' + str(train_data.shape))
print('Train Labels shape: ' + str(train_labels.shape))
print('Train Classes shape: ' + str(train_classes.shape))
print('Validate Data shape: ' + str(validate_data.shape))
print('Validate Labels shape: ' + str(validate_labels.shape))
print('Validate Classes shape: ' + str(validate_classes.shape))
if self.test_log is False:
self.optimize(train_data,
train_labels,
train_classes,
validate_data=validate_data,
validate_labels=validate_labels,
validate_classes=validate_classes)
else:
self.optimize(train_data,
train_labels,
train_classes,
validate_data=validate_data,
validate_labels=validate_labels,
validate_classes=validate_classes,
test_data=test_data,
test_labels=test_labels,
test_classes=test_classes)
else:
if self.test_log is False:
self.optimize(data, labels, classes)
else:
self.optimize(data,
labels,
classes,
test_data=test_data,
test_labels=test_labels,
test_classes=test_classes)
