def __init__(self, is_training, config, scaler=None, adj_mx=None):
super(DCRNNModel, self).__init__(config, scaler=scaler)
batch_size = int(config.get('batch_size'))
max_diffusion_step = int(config.get('max_diffusion_step', 2))
cl_decay_steps = int(config.get('cl_decay_steps', 1000))
filter_type = config.get('filter_type', 'laplacian')
horizon = int(config.get('horizon', 1))
input_dim = int(config.get('input_dim', 1))
loss_func = config.get('loss_func', 'MSE')
max_grad_norm = float(config.get('max_grad_norm', 5.0))
num_nodes = int(config.get('num_nodes', 1))
num_rnn_layers = int(config.get('num_rnn_layers', 1))
output_dim = int(config.get('output_dim', 1))
rnn_units = int(config.get('rnn_units'))
seq_len = int(config.get('seq_len'))
use_curriculum_learning = bool(
config.get('use_curriculum_learning', False))
assert input_dim == output_dim, 'input_dim: %d != output_dim: %d' % (
input_dim, output_dim)
# Input (batch_size, timesteps, num_sensor, input_dim)
self._inputs = tf.placeholder(tf.float32,
shape=(batch_size, seq_len, num_nodes,
input_dim),
name='inputs')
# Labels: (batch_size, timesteps, num_sensor, output_dim)
self._labels = tf.placeholder(tf.float32,
shape=(batch_size, horizon, num_nodes,
output_dim),
name='labels')
GO_SYMBOL = tf.zeros(shape=(batch_size, num_nodes * input_dim))
cell = DCGRUCell(rnn_units,
adj_mx,
max_diffusion_step=max_diffusion_step,
num_nodes=num_nodes,
filter_type=filter_type)
cell_with_projection = DCGRUCell(rnn_units,
adj_mx,
max_diffusion_step=max_diffusion_step,
num_nodes=num_nodes,
num_proj=output_dim,
filter_type=filter_type)
encoding_cells = [cell] * num_rnn_layers
decoding_cells = [cell] * (num_rnn_layers - 1) + [cell_with_projection]
encoding_cells = tf.contrib.rnn.MultiRNNCell(encoding_cells,
state_is_tuple=True)
decoding_cells = tf.contrib.rnn.MultiRNNCell(decoding_cells,
state_is_tuple=True)
global_step = tf.train.get_or_create_global_step()
# Outputs: (batch_size, timesteps, num_nodes, output_dim)
with tf.variable_scope('DCRNN_SEQ'):
inputs = tf.unstack(tf.reshape(
self._inputs, (batch_size, seq_len, num_nodes * input_dim)),
axis=1)
labels = tf.unstack(tf.reshape(
self._labels, (batch_size, horizon, num_nodes * output_dim)),
axis=1)
labels.insert(0, GO_SYMBOL)
loop_function = None
if is_training:
if use_curriculum_learning:
def loop_function(prev, i):
c = tf.random_uniform((), minval=0, maxval=1.)
threshold = self._compute_sampling_threshold(
global_step, cl_decay_steps)
result = tf.cond(tf.less(c, threshold),
lambda: labels[i], lambda: prev)
return result
else:
# Return the output of the model.
def loop_function(prev, _):
return prev
_, enc_state = tf.contrib.rnn.static_rnn(encoding_cells,
inputs,
dtype=tf.float32)
outputs, final_state = legacy_seq2seq.rnn_decoder(
labels, enc_state, decoding_cells, loop_function=loop_function)
# Project the output to output_dim.
outputs = tf.stack(outputs[:-1], axis=1)
self._outputs = tf.reshape(
outputs, (batch_size, horizon, num_nodes, output_dim),
name='outputs')
preds = self._outputs[..., 0]
labels = self._labels[..., 0]
null_val = config.get('null_val', 0.)
self._mae = masked_mae_loss(self._scaler, null_val)(preds=preds,
labels=labels)
if loss_func == 'MSE':
self._loss = masked_mse_loss(self._scaler,
null_val)(preds=self._outputs,
labels=self._labels)
elif loss_func == 'MAE':
self._loss = masked_mae_loss(self._scaler,
null_val)(preds=self._outputs,
labels=self._labels)
elif loss_func == 'RMSE':
self._loss = masked_rmse_loss(self._scaler,
null_val)(preds=self._outputs,
labels=self._labels)
else:
self._loss = masked_mse_loss(self._scaler,
null_val)(preds=self._outputs,
labels=self._labels)
if is_training:
optimizer = tf.train.AdamOptimizer(self._lr)
tvars = tf.trainable_variables()
grads = tf.gradients(self._loss, tvars)
grads, _ = tf.clip_by_global_norm(grads, max_grad_norm)
self._train_op = optimizer.apply_gradients(zip(grads, tvars),
global_step=global_step,
name='train_op')
self._merged = tf.summary.merge_all()
def __init__(self, **kwargs):
self._kwargs = kwargs
self._data_kwargs = kwargs.get('data')
self._model_kwargs = kwargs.get('model')
self._train_kwargs = kwargs.get('train')
self._test_kwargs = kwargs.get('test')
# logging.
self._log_dir = self._get_log_dir(kwargs)
log_level = self._kwargs.get('log_level', 'INFO')
self._logger = utils.get_logger(self._log_dir, __name__, 'info.log', level=log_level)
self._writer = tf.summary.FileWriter(self._log_dir)
self._logger.info(kwargs)
self._mon_ratio = float(self._kwargs.get('mon_ratio'))
# Model's args
self._seq_len = int(self._model_kwargs.get('seq_len'))
self._horizon = int(self._model_kwargs.get('horizon'))
self._input_dim = int(self._model_kwargs.get('input_dim'))
self._nodes = int(self._model_kwargs.get('num_nodes'))
# Test's args
self._flow_selection = self._test_kwargs.get('flow_selection')
self._test_size = self._test_kwargs.get('test_size')
self._run_times = self._test_kwargs.get('run_times')
# Data preparation
self._day_size = self._data_kwargs.get('day_size')
self._data = utils.load_dataset_dcrnn(seq_len=self._model_kwargs.get('seq_len'),
horizon=self._model_kwargs.get('horizon'),
input_dim=self._model_kwargs.get('input_dim'),
mon_ratio=self._mon_ratio,
**self._data_kwargs)
for k, v in self._data.items():
if hasattr(v, 'shape'):
self._logger.info((k, v.shape))
# Build models.
scaler = self._data['scaler']
with tf.name_scope('Train'):
with tf.variable_scope('DCRNN', reuse=False):
self._train_model = DCRNNModel(is_training=True, scaler=scaler,
batch_size=self._data_kwargs['batch_size'],
adj_mx=self._data['adj_mx'], **self._model_kwargs)
with tf.name_scope('Val'):
with tf.variable_scope('DCRNN', reuse=True):
self._val_model = DCRNNModel(is_training=False, scaler=scaler,
batch_size=self._data_kwargs['val_batch_size'],
adj_mx=self._data['adj_mx'], **self._model_kwargs)
with tf.name_scope('Eval'):
with tf.variable_scope('DCRNN', reuse=True):
self._eval_model = DCRNNModel(is_training=False, scaler=scaler,
batch_size=self._data_kwargs['eval_batch_size'],
adj_mx=self._data['adj_mx'], **self._model_kwargs)
with tf.name_scope('Test'):
with tf.variable_scope('DCRNN', reuse=True):
self._test_model = DCRNNModel(is_training=False, scaler=scaler,
batch_size=self._data_kwargs['test_batch_size'],
adj_mx=self._data['adj_mx'], **self._model_kwargs)
# Learning rate.
self._lr = tf.get_variable('learning_rate', shape=(), initializer=tf.constant_initializer(0.01),
trainable=False)
self._new_lr = tf.placeholder(tf.float32, shape=(), name='new_learning_rate')
self._lr_update = tf.assign(self._lr, self._new_lr, name='lr_update')
# Configure optimizer
optimizer_name = self._train_kwargs.get('optimizer', 'adam').lower()
epsilon = float(self._train_kwargs.get('epsilon', 1e-3))
optimizer = tf.train.AdamOptimizer(self._lr, epsilon=epsilon, )
if optimizer_name == 'sgd':
optimizer = tf.train.GradientDescentOptimizer(self._lr, )
elif optimizer_name == 'amsgrad':
optimizer = AMSGrad(self._lr, epsilon=epsilon)
# Calculate loss
output_dim = self._model_kwargs.get('output_dim')
preds = self._train_model.outputs
labels = self._train_model.labels[..., :output_dim]
null_val = 0.
self._loss_fn = masked_mse_loss(scaler, null_val)
# self._loss_fn = masked_mae_loss(scaler, null_val)
self._train_loss = self._loss_fn(preds=preds, labels=labels)
tvars = tf.trainable_variables()
grads = tf.gradients(self._train_loss, tvars)
max_grad_norm = kwargs['train'].get('max_grad_norm', 1.)
grads, _ = tf.clip_by_global_norm(grads, max_grad_norm)
global_step = tf.train.get_or_create_global_step()
self._train_op = optimizer.apply_gradients(zip(grads, tvars), global_step=global_step, name='train_op')
max_to_keep = self._train_kwargs.get('max_to_keep', 100)
self._epoch = 0
self._saver = tf.train.Saver(tf.global_variables(), max_to_keep=max_to_keep)
# Log model statistics.
total_trainable_parameter = utils.get_total_trainable_parameter_size()
self._logger.info('Total number of trainable parameters: {:d}'.format(total_trainable_parameter))
for var in tf.global_variables():
self._logger.debug('{}, {}'.format(var.name, var.get_shape()))
def __init__(self, sess, adj_mx, dataloaders, kwargs):
self._kwargs = kwargs
self._data_kwargs = kwargs.get('data')
self._model_kwargs = kwargs.get('model')
self._train_kwargs = kwargs.get('train')
self._paths_kwargs = kwargs.get('paths')
self._save_tensors = kwargs.get('tf_config').get('save_tensors', False) \
if kwargs.get('tf_config') else False
self._trace = kwargs.get('tf_config').get('trace', False) \
if kwargs.get('tf_config') else False
self._save_graph = kwargs.get('tf_config').get('save_graph', False) \
if kwargs.get('tf_config') else False
self._log_dir = self._get_log_dir(kwargs)
self._writer = tf.summary.FileWriter(self._log_dir, sess.graph) \
if self._save_graph else tf.summary.FileWriter(self._log_dir)
# Data preparation
self._data = dataloaders
# for k, v in self._data.items():
# if hasattr(v, 'shape'):
# self._kwargs.logger.info((k, v.shape))
# Build models.
scaler = self._data['scaler']
with tf.name_scope('Train'):
with tf.variable_scope('DCRNN', reuse=False):
train_batch_size = dataloaders['train_loader'].batch_size
self._train_model = DCRNNModel(is_training=True,
scaler=scaler,
batch_size=train_batch_size,
adj_mx=adj_mx,
**self._model_kwargs)
with tf.name_scope('Val'):
with tf.variable_scope('DCRNN', reuse=True):
val_batch_size = dataloaders['val_loader'].batch_size
self._val_model = DCRNNModel(is_training=False,
scaler=scaler,
batch_size=val_batch_size,
adj_mx=adj_mx,
**self._model_kwargs)
with tf.name_scope('Test'):
with tf.variable_scope('DCRNN', reuse=True):
test_batch_size = dataloaders['test_loader'].batch_size
self._test_model = DCRNNModel(is_training=False,
scaler=scaler,
batch_size=test_batch_size,
adj_mx=adj_mx,
**self._model_kwargs)
# Learning rate.
self._lr = tf.get_variable('learning_rate',
shape=(),
initializer=tf.constant_initializer(0.01),
trainable=False)
self._new_lr = tf.placeholder(tf.float32,
shape=(),
name='new_learning_rate')
self._lr_update = tf.assign(self._lr, self._new_lr, name='lr_update')
# Configure optimizer
optimizer_name = self._train_kwargs.get('optimizer', 'adam').lower()
epsilon = float(self._train_kwargs.get('epsilon', 1e-3))
optimizer = tf.train.AdamOptimizer(self._lr, epsilon=epsilon)
if optimizer_name == 'sgd':
optimizer = tf.train.GradientDescentOptimizer(self._lr, )
elif optimizer_name == 'amsgrad':
optimizer = AMSGrad(self._lr, epsilon=epsilon)
# Calculate loss
output_dim = self._model_kwargs.get('output_dim')
preds = self._train_model.outputs
labels = self._train_model.labels[..., :output_dim]
null_val = 0. if kwargs['model'].get('exclude_zeros_in_metric',
True) else np.nan
loss_func_dict = {
'mae': masked_mae_loss(scaler, null_val),
'rmse': masked_rmse_loss(scaler, null_val),
'mse': masked_mse_loss(scaler, null_val)
}
self._loss_fn = loss_func_dict.get(kwargs['train'].get(
'loss_func', 'mae'))
self._metric_fn = loss_func_dict.get(kwargs['train'].get(
'metric_func', 'mae'))
self._train_loss = self._loss_fn(preds=preds, labels=labels)
tvars = tf.trainable_variables()
grads = tf.gradients(self._train_loss, tvars)
max_grad_norm = kwargs['train'].get('max_grad_norm', 1.)
grads, _ = tf.clip_by_global_norm(grads, max_grad_norm)
self._train_op = optimizer.apply_gradients(
zip(grads, tvars),
global_step=tf.train.get_or_create_global_step(),
name='train_op')
max_to_keep = self._train_kwargs.get('max_to_keep', 100)
self._saver = tf.train.Saver(tf.global_variables(),
max_to_keep=max_to_keep)
# load model
model_filename = self._paths_kwargs.get('model_filename')
if model_filename is not None:
self._saver.restore(sess, model_filename)
self._kwargs.logger.info(
'Pretrained model was loaded from : {}'.format(model_filename))
else:
sess.run(tf.global_variables_initializer())
# Log model statistics.
total_trainable_parameter = utils.get_total_trainable_parameter_size()
self._kwargs.logger.info('Total number of trainable parameters: {:d}'.\
format(total_trainable_parameter))
for var in tf.global_variables():
self._kwargs.logger.debug('{}, {}'.format(var.name,
var.get_shape()))
def __init__(self, is_training=False, **kwargs):
super(DCRNNSupervisor, self).__init__(**kwargs)
self._data = utils.load_dataset_dcrnn(
seq_len=self._model_kwargs.get('seq_len'),
horizon=self._model_kwargs.get('horizon'),
input_dim=self._model_kwargs.get('input_dim'),
mon_ratio=self._mon_ratio,
scaler_type=self._kwargs.get('scaler'),
is_training=is_training,
**self._data_kwargs)
for k, v in self._data.items():
if hasattr(v, 'shape'):
self._logger.info((k, v.shape))
# Build models.
scaler = self._data['scaler']
if is_training:
self.model = DCRNNModel(scaler=scaler,
batch_size=self._data_kwargs['batch_size'],
adj_mx=self._data['adj_mx'],
**self._model_kwargs)
else:
self.model = DCRNNModel(scaler=scaler,
batch_size=1,
adj_mx=self._data['adj_mx'],
**self._model_kwargs)
# Learning rate.
self._lr = tf.get_variable('learning_rate',
shape=(),
initializer=tf.constant_initializer(0.01),
trainable=False)
self._new_lr = tf.placeholder(tf.float32,
shape=(),
name='new_learning_rate')
self._lr_update = tf.assign(self._lr, self._new_lr, name='lr_update')
# Configure optimizer
optimizer_name = self._train_kwargs.get('optimizer', 'adam').lower()
epsilon = float(self._train_kwargs.get('epsilon', 1e-3))
optimizer = tf.train.AdamOptimizer(
self._lr,
epsilon=epsilon,
)
if optimizer_name == 'sgd':
optimizer = tf.train.GradientDescentOptimizer(self._lr, )
elif optimizer_name == 'amsgrad':
optimizer = AMSGrad(self._lr, epsilon=epsilon)
# Calculate loss
output_dim = self._model_kwargs.get('output_dim')
preds = self.model.outputs
labels = self.model.labels[..., :output_dim]
null_val = 0.
self._loss_fn = masked_mse_loss(scaler, null_val)
# self._loss_fn = masked_mae_loss(scaler, null_val)
self._train_loss = self._loss_fn(preds=preds, labels=labels)
tvars = tf.trainable_variables()
grads = tf.gradients(self._train_loss, tvars)
max_grad_norm = kwargs['train'].get('max_grad_norm', 1.)
grads, _ = tf.clip_by_global_norm(grads, max_grad_norm)
global_step = tf.train.get_or_create_global_step()
self._train_op = optimizer.apply_gradients(zip(grads, tvars),
global_step=global_step,
name='train_op')
max_to_keep = self._train_kwargs.get('max_to_keep', 100)
self._epoch = 0
self._saver = tf.train.Saver(tf.global_variables(),
max_to_keep=max_to_keep)
# Log model statistics.
total_trainable_parameter = utils.get_total_trainable_parameter_size()
self._logger.info('Total number of trainable parameters: {:d}'.format(
total_trainable_parameter))
for var in tf.global_variables():
self._logger.debug('{}, {}'.format(var.name, var.get_shape()))
def __init__(self, is_training=False, **kwargs):
super(DCRNNSupervisor, self).__init__(**kwargs)
self._r = int(self._model_kwargs.get('r'))
self._lamda = []
self._lamda.append(self._test_kwargs.get('lamda_0'))
self._lamda.append(self._test_kwargs.get('lamda_1'))
self._lamda.append(self._test_kwargs.get('lamda_2'))
# Data preparation
self._day_size = self._data_kwargs.get('day_size')
self._data = utils.load_dataset_dcrnn_fwbw(
seq_len=self._model_kwargs.get('seq_len'),
horizon=self._model_kwargs.get('horizon'),
input_dim=self._model_kwargs.get('input_dim'),
mon_ratio=self._mon_ratio,
scaler_type=self._kwargs.get('scaler'),
is_training=is_training,
**self._data_kwargs)
for k, v in self._data.items():
if hasattr(v, 'shape'):
self._logger.info((k, v.shape))
# Build models.
scaler = self._data['scaler']
with tf.name_scope('Train'):
with tf.variable_scope('DCRNN', reuse=False):
self.train_model = DCRNNModel(
is_training=True,
scaler=scaler,
batch_size=self._data_kwargs['batch_size'],
adj_mx=self._data['adj_mx'],
**self._model_kwargs)
with tf.name_scope('Val'):
with tf.variable_scope('DCRNN', reuse=True):
self.val_model = DCRNNModel(
is_training=False,
scaler=scaler,
batch_size=self._data_kwargs['val_batch_size'],
adj_mx=self._data['adj_mx'],
**self._model_kwargs)
with tf.name_scope('Eval'):
with tf.variable_scope('DCRNN', reuse=True):
self.eval_model = DCRNNModel(
is_training=False,
scaler=scaler,
batch_size=self._data_kwargs['eval_batch_size'],
adj_mx=self._data['adj_mx'],
**self._model_kwargs)
with tf.name_scope('Test'):
with tf.variable_scope('DCRNN', reuse=True):
self.test_model = DCRNNModel(
is_training=False,
scaler=scaler,
batch_size=self._data_kwargs['test_batch_size'],
adj_mx=self._data['adj_mx'],
**self._model_kwargs)
# Learning rate.
self._lr = tf.get_variable('learning_rate',
shape=(),
initializer=tf.constant_initializer(0.01),
trainable=False)
self._new_lr = tf.placeholder(tf.float32,
shape=(),
name='new_learning_rate')
self._lr_update = tf.assign(self._lr, self._new_lr, name='lr_update')
# Configure optimizer
optimizer_name = self._train_kwargs.get('optimizer', 'adam').lower()
epsilon = float(self._train_kwargs.get('epsilon', 1e-3))
optimizer = tf.train.AdamOptimizer(
self._lr,
epsilon=epsilon,
)
if optimizer_name == 'sgd':
optimizer = tf.train.GradientDescentOptimizer(self._lr, )
elif optimizer_name == 'amsgrad':
optimizer = AMSGrad(self._lr, epsilon=epsilon)
# Calculate loss
output_dim = self._model_kwargs.get('output_dim')
# fw decoder
preds_fw = self.train_model.outputs_fw
labels_fw = self.train_model.labels_fw[..., :output_dim]
# bw encoder
enc_preds_bw = self.train_model.enc_outputs_bw
enc_labels_bw = self.train_model.enc_labels_bw[..., :output_dim]
null_val = 0.
self._loss_fn = masked_mse_loss(scaler, null_val)
self._train_loss_dec = self._loss_fn(preds=preds_fw, labels=labels_fw)
# backward loss
self._train_loss_enc_bw = self._loss_fn(preds=enc_preds_bw,
labels=enc_labels_bw)
self._train_loss = self._train_loss_dec + self._train_loss_enc_bw
tvars = tf.trainable_variables()
grads = tf.gradients(self._train_loss, tvars)
max_grad_norm = kwargs['train'].get('max_grad_norm', 1.)
grads, _ = tf.clip_by_global_norm(grads, max_grad_norm)
global_step = tf.train.get_or_create_global_step()
self._train_op = optimizer.apply_gradients(zip(grads, tvars),
global_step=global_step,
name='train_op')
max_to_keep = self._train_kwargs.get('max_to_keep', 100)
self._epoch = 0
self._saver = tf.train.Saver(tf.global_variables(),
max_to_keep=max_to_keep)
# Log model statistics.
total_trainable_parameter = utils.get_total_trainable_parameter_size()
self._logger.info('Total number of trainable parameters: {:d}'.format(
total_trainable_parameter))
for var in tf.global_variables():
self._logger.debug('{}, {}'.format(var.name, var.get_shape()))
