def run_loop(session,
modes,
models,
summary_ops=None,
logging=False,
name_modifier=""):
"""
Runs models for the specified number of epochs.
:param session: MonitoredTrainingSession to use
:param modes: List of modes. Whether to train, evaluate, or test
:param models: List of models
:param summary_ops: List of summary operations with one operation per model, or None.
:param logging: Whether to write summaries for tensorboard.
:param name_modifier: Suffix to identify the model in tensorboard. Creates a subdirectory.
:return: List of lists, where each sub-list is the loss/epoch.
"""
assert (len(modes) == len(models))
assert (summary_ops is None or len(summary_ops) == len(models))
assert (summary_ops is None or logging)
nr_models = len(modes)
representative_config = models[
0].config # used for variables that are assumed to be the same between models
writers = []
losses = []
for i in range(nr_models):
losses.append([])
if logging:
writers.append(
tf.summary.FileWriter(
log_path + os.sep + Mode.to_string(modes[i]).lower() +
name_modifier, session.graph))
else:
writers.append(None)
for i in range(representative_config.nr_epochs):
for m in range(nr_models):
model = models[m]
perplexity = run_epoch(
session,
model,
writer=writers[m],
summary_op=summary_ops[m],
eval_op=model.train_op if modes[m] == Mode.train else None)
losses[m].append(perplexity)
log("%s Epoch: %d Perplexity: %.3f" %
(Mode.to_string(modes[m]), i + 1, perplexity))
return losses
def instantiate_model(mode, config, data_set):
"""
Instantiates the model with the given configuration and data.
:param mode: Mode enum specifying train/test/validation.
:param config:
:param data_set: a tf.data.dataset
:return: model, summary operation
"""
name = Mode.to_string(mode)
initializer = tf.random_uniform_initializer(-config.init_scale,
config.init_scale)
with tf.name_scope(name=name):
model_input = ModelInput(config=config, data=data_set)
with tf.variable_scope("Model",
reuse=tf.AUTO_REUSE,
initializer=initializer):
model = SeqModel(is_training=(mode == Mode.train),
config=config,
dataset=model_input)
summary = tf.summary.scalar(name + " Loss", model.normalized_cost)
if mode == Mode.train:
summ_lr = tf.summary.scalar("Learning Rate",
model.config.learning_rate)
summary = tf.summary.merge([summary, summ_lr])
summ_layers = tf.summary.scalar("Layers", model.config.num_layers)
summ_bs = tf.summary.scalar("Batch Size", model.config.batch_size)
summ_grad_norm = tf.summary.scalar("Gradient Norm",
model.config.max_grad_norm)
summ_keep_prob = tf.summary.scalar("Dropout Keep Probability",
model.config.keep_prob)
summ_hidden_size = tf.summary.scalar("State Size",
model.config.hidden_size)
summary = tf.summary.merge([
summary, summ_layers, summ_bs, summ_grad_norm, summ_keep_prob,
summ_hidden_size
])
return model, summary
