def __init__(self, sess, vocab_size):
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
### Few variables that has been initianlised here
# Word embedding variable
self.vocab_embed_variable = model_utils.get_vocab_embed_variable(
vocab_size)
### Define Place Holders
self.document_placeholder = tf.placeholder("int32", [
None,
(FLAGS.max_doc_length + FLAGS.max_title_length +
FLAGS.max_image_length + FLAGS.max_firstsentences_length +
FLAGS.max_randomsentences_length), FLAGS.max_sent_length
],
name='doc-ph')
self.label_placeholder = tf.placeholder(
dtype, [None, FLAGS.max_doc_length, FLAGS.target_label_size],
name='label-ph')
self.weight_placeholder = tf.placeholder(dtype,
[None, FLAGS.max_doc_length],
name='weight-ph')
# Only used for test/validation corpus
self.logits_placeholder = tf.placeholder(
dtype, [None, FLAGS.max_doc_length, FLAGS.target_label_size],
name='logits-ph')
### Define Policy Core Network: Consists of Encoder, Decoder and Convolution.
self.extractor_output, self.logits, _ = model_docsum.policy_network(
self.vocab_embed_variable, self.document_placeholder,
self.label_placeholder)
### Define Supervised Cross Entropy Loss
self.cross_entropy_loss = model_docsum.cross_entropy_loss(
self.logits, self.label_placeholder, self.weight_placeholder)
### Define training operators
self.train_op_policynet_withgold, self.gradients = model_docsum.train_cross_entropy_loss(
self.cross_entropy_loss)
# accuracy operation : exact match
self.accuracy = model_docsum.accuracy(self.logits,
self.label_placeholder,
self.weight_placeholder)
# final accuracy operation
self.final_accuracy = model_docsum.accuracy(self.logits_placeholder,
self.label_placeholder,
self.weight_placeholder)
# Create a saver.
self.saver = tf.train.Saver(tf.all_variables(), max_to_keep=None)
# Summary Operations
_ = tf.histogram_summary("gradients", self.gradients)
self.ce_loss_summary = tf.scalar_summary("cross-entropy-loss",
self.cross_entropy_loss)
self.tstepa_accuracy_summary = tf.scalar_summary(
"training_accuracy_stepa", self.accuracy)
self.vstepa_accuracy_summary = tf.scalar_summary(
"validation_accuracy_stepa", self.final_accuracy)
self.merged = tf.merge_all_summaries()
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
sess.run(init)
# Create Summary Graph for Tensorboard
self.summary_writer = tf.train.SummaryWriter(FLAGS.train_dir,
sess.graph)
def __init__(self, sess, vocab_size):
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
### Few variables that has been initianlised here
# Word embedding variable
self.vocab_embed_variable = model_utils.get_vocab_embed_variable(
vocab_size)
### Define Place Holders
self.document_placeholder = tf.placeholder("int32", [
None,
(FLAGS.max_doc_length + FLAGS.max_title_length +
FLAGS.max_image_length), FLAGS.max_sent_length
],
name='doc-ph')
self.label_placeholder = tf.placeholder(
dtype, [None, FLAGS.max_doc_length, FLAGS.target_label_size],
name='label-ph')
self.weight_placeholder = tf.placeholder(dtype,
[None, FLAGS.max_doc_length],
name='weight-ph')
# Reward related place holders: Pass both rewards as place holders to make them constant for rl optimizer
self.actual_reward_multisample_placeholder = tf.placeholder(
dtype, [None, 1], name='actual-reward-multisample-ph'
) # [FLAGS.batch_size, Single Sample]
# Self predicted label placeholder
self.predicted_multisample_label_placeholder = tf.placeholder(
dtype, [None, 1, FLAGS.max_doc_length, FLAGS.target_label_size],
name='pred-multisample-label-ph')
# Only used for test/validation corpus
self.logits_placeholder = tf.placeholder(
dtype, [None, FLAGS.max_doc_length, FLAGS.target_label_size],
name='logits-ph')
### Define Policy Core Network: Consists of Encoder, Decoder and Convolution.
self.extractor_output, self.logits = model_docsum.policy_network(
self.vocab_embed_variable, self.document_placeholder,
self.label_placeholder)
### Define Reward-Weighted Cross Entropy Loss
self.rewardweighted_cross_entropy_loss_multisample = model_docsum.reward_weighted_cross_entropy_loss_multisample(
self.logits, self.predicted_multisample_label_placeholder,
self.actual_reward_multisample_placeholder,
self.weight_placeholder)
### Define training operators
self.train_op_policynet_expreward = model_docsum.train_neg_expectedreward(
self.rewardweighted_cross_entropy_loss_multisample)
# accuracy operation : exact match
self.accuracy = model_docsum.accuracy(self.logits,
self.label_placeholder,
self.weight_placeholder)
# final accuracy operation
self.final_accuracy = model_docsum.accuracy(self.logits_placeholder,
self.label_placeholder,
self.weight_placeholder)
# Create a saver.
self.saver = tf.train.Saver(tf.all_variables(), max_to_keep=None)
# Scalar Summary Operations
self.rewardweighted_ce_multisample_loss_summary = tf.scalar_summary(
"rewardweighted-cross-entropy-multisample-loss",
self.rewardweighted_cross_entropy_loss_multisample)
self.taccuracy_summary = tf.scalar_summary("training_accuracy",
self.accuracy)
self.vaccuracy_summary = tf.scalar_summary("validation_accuracy",
self.final_accuracy)
# # Build the summary operation based on the TF collection of Summaries.
# # self.summary_op = tf.merge_all_summaries()
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
sess.run(init)
# Create Summary Graph for Tensorboard
self.summary_writer = tf.train.SummaryWriter(FLAGS.train_dir,
sess.graph)