def predict_on_batch(self, sess, inputs_batch, targets_batch=None):
"""
Make predictions for the provided batch of data
Args:
sess: tf.Session()
input_batch: np.ndarray of shape (n_samples, #TODO)
Returns:
e predictions: np.ndarray of shape (n_samples, max_length_y)
"""
inputs_batch_padded, _ = padded_batch_lr(inputs_batch,
self.config.max_length_x,
self.config.voc)
length_inputs_batch = np.asarray([min(self.config.max_length_x,len(item))\
for item in inputs_batch])
if targets_batch is None:
feed = self.create_feed_dict(inputs_batch_padded,
length_inputs_batch)
else:
decoder_batch_padded, _ = padded_batch_lr(targets_batch,
self.config.max_length_y,
self.config.voc,
option='decoder_inputs')
targets_batch_padded, mask_batch = padded_batch_lr(
targets_batch,
self.config.max_length_y,
self.config.voc,
option='decoder_targets')
length_decoder_batch = np.asarray([min(self.config.max_length_y, len(item)+1)\
for item in targets_batch])
feed = self.create_feed_dict(inputs_batch_padded,
length_inputs_batch, mask_batch,
length_decoder_batch,
decoder_batch_padded,
targets_batch_padded)
preds, dev_loss, dev_acc, dev_loss_summ, dev_acc_summ = sess.run(
[
self.infer_pred, self.dev_loss, self.dev_accuracy,
self.dev_loss_summary, self.dev_acc_summary
],
feed_dict=feed)
preds = np.argmax(preds, 2)
return preds, dev_loss, dev_acc, dev_loss_summ, dev_acc_summ
def train_on_batch(self, sess, inputs_batch, targets_batch):
"""
Perform one step of gradient descent on the provided batch of data.
This version also returns the norm of gradients.
"""
inputs_batch_padded, _ = padded_batch_lr(inputs_batch,
self.config.max_length_x,
self.config.voc)
length_inputs_batch = np.asarray([min(self.config.max_length_x,len(item))\
for item in inputs_batch])
if targets_batch is None:
feed = self.create_feed_dict(inputs_batch_padded,
length_inputs_batch,
self.config.pdrop)
else:
decoder_batch_padded, _ = padded_batch_lr(targets_batch,
self.config.max_length_y,
self.config.voc,
option='decoder_inputs')
targets_batch_padded, mask_batch = padded_batch_lr(
targets_batch,
self.config.max_length_y,
self.config.voc,
option='decoder_targets')
length_decoder_batch = np.asarray([min(self.config.max_length_y, len(item)+1)\
for item in targets_batch])
feed = self.create_feed_dict(inputs_batch_padded,
length_inputs_batch, mask_batch,
length_decoder_batch,
decoder_batch_padded,
targets_batch_padded,
self.config.pdrop)
_, preds, loss, acc, loss_summ, acc_summ = sess.run([
self.train_op, self.train_pred, self.loss, self.accuracy,
self.loss_summary, self.acc_summary
],
feed_dict=feed)
preds = np.argmax(preds, 2)
return preds, loss, acc, loss_summ, acc_summ