czzhangheng
/
DCRNN
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Combine Val and Test model.

This commit is contained in:
Yaguang 2018-09-30 22:14:45 -07:00
parent 5dc36fed7c
commit 7e05414d99
1 changed files with 11 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

28
model/dcrnn_supervisor.py
View File

@ -51,27 +51,12 @@ class DCRNNSupervisor(object):
batch_size=self._data_kwargs['batch_size'],
adj_mx=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['batch_size'],
adj_mx=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=adj_mx, **self._model_kwargs)
# 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_mae_loss(scaler, null_val)
self._train_loss = self._loss_fn(preds=preds, labels=labels)
# Learning rate.
self._lr = tf.get_variable('learning_rate', shape=(), initializer=tf.constant_initializer(0.01),
trainable=False)
@ -87,6 +72,15 @@ class DCRNNSupervisor(object):
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_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.)
@ -141,7 +135,7 @@ class DCRNNSupervisor(object):
data['x_' + category][..., 0] = scaler.transform(data['x_' + category][..., 0])
data['y_' + category][..., 0] = scaler.transform(data['y_' + category][..., 0])
data['train_loader'] = DataLoader(data['x_train'], data['y_train'], kwargs['batch_size'], shuffle=True)
data['val_loader'] = DataLoader(data['x_val'], data['y_val'], kwargs['val_batch_size'], shuffle=False)
data['val_loader'] = DataLoader(data['x_val'], data['y_val'], kwargs['test_batch_size'], shuffle=False)
data['test_loader'] = DataLoader(data['x_test'], data['y_test'], kwargs['test_batch_size'], shuffle=False)
data['scaler'] = scaler
@ -242,7 +236,7 @@ class DCRNNSupervisor(object):
global_step = sess.run(tf.train.get_or_create_global_step())
# Compute validation error.
val_results = self.run_epoch_generator(sess, self._val_model,
val_results = self.run_epoch_generator(sess, self._test_model,
self._data['val_loader'].get_iterator(),
training=False)
val_loss, val_mae = val_results['loss'], val_results['mae']