def test_build_learning_rate_summaries(self):
training.create_global_step()
config = ModelConfig(loss_config=LossConfig(name='log_loss'),
optimizer_config=OptimizerConfig(
name='Adadelta',
decay_type='exponential_decay'))
x = {'source_ids': tf.placeholder(tf.float32, [2, 89])}
y = tf.constant([[1], [1]])
model = BaseModel(plx.ModeKeys.TRAIN,
graph_fn=self.get_dummy_graph_fn(),
config=config,
model_type=BaseModel.Types.CLASSIFIER,
summaries=['learning_rate'],
name='test',
params=None)
model(x, y, None, None)
# Only var are created
summaries_names = list(
get_tracked(collection=tf.GraphKeys.SUMMARIES_BY_NAMES).keys())
assert len(summaries_names) == 1
assert summaries_names[0] == 'learning_rate'
def __init__(self,
mode,
model_type,
graph_fn,
loss_config,
optimizer_config=None,
eval_metrics_config=None,
summaries='all',
clip_gradients=0.5,
clip_embed_gradients=0.1,
name="Model"):
# Check if mode corresponds to the correct model
if mode in [Modes.GENERATE, Modes.ENCODE
] and model_type != self.Types.GENERATOR:
raise TypeError(
"Current model type `{}` does not support passed mode `{}`.".
format(model_type, mode))
super(BaseModel, self).__init__(mode, name, self.ModuleType.MODEL)
self.loss_config = loss_config
self.optimizer_config = optimizer_config or OptimizerConfig(
'adam', learning_rate=0.001)
self.eval_metrics_config = eval_metrics_config or []
self.model_type = model_type
self.summaries = summarizer.SummaryOptions.validate(summaries)
assert model_type in self.Types.VALUES, "`model_type` provided is unsupported."
self._clip_gradients = clip_gradients
self._clip_embed_gradients = clip_embed_gradients
self._grads_and_vars = None
self._total_loss = None
self._loss = None
self._check_subgraph_fn(function=graph_fn, function_name='graph_fn')
self._graph_fn = graph_fn
def __init__(self,
mode,
graph_fn,
num_states,
num_actions,
loss_config=None,
optimizer_config=None,
eval_metrics_config=None,
is_deterministic=False,
is_continuous=False,
summaries='all',
clip_gradients=0.5,
clip_embed_gradients=0.1,
name="Model"):
optimizer_config = optimizer_config or OptimizerConfig(
'adam', learning_rate=0.004)
super(VPGModel,
self).__init__(mode=mode,
name=name,
graph_fn=graph_fn,
num_states=num_states,
num_actions=num_actions,
loss_config=loss_config,
optimizer_config=optimizer_config,
eval_metrics_config=eval_metrics_config,
is_deterministic=is_deterministic,
is_continuous=is_continuous,
summaries=summaries,
clip_gradients=clip_gradients,
clip_embed_gradients=clip_embed_gradients)
def test_return_estimator_spec(self):
x = {'x': tf.placeholder(tf.float32, [2, 89])}
y = tf.constant([[1], [1]])
model = BaseModel(plx.Modes.TRAIN,
graph_fn=self.get_dummy_graph_fn(),
loss_config=LossConfig(module='log_loss'),
optimizer_config=OptimizerConfig(module='adadelta'),
model_type=BaseModel.Types.CLASSIFIER,
eval_metrics_config=[],
summaries=['learning_rate'],
name='test')
assert isinstance(model(x, y, None, None), EstimatorSpec)
def test_return_estimator_spec(self):
config = ModelConfig(loss_config=LossConfig(name='log_loss'),
optimizer_config=OptimizerConfig(name='Adadelta'))
x = {'source_ids': tf.placeholder(tf.float32, [2, 89])}
y = tf.constant([[1], [1]])
model = BaseModel(plx.ModeKeys.TRAIN,
graph_fn=self.get_dummy_graph_fn(),
config=config,
model_type=BaseModel.Types.CLASSIFIER,
summaries=['learning_rate'],
name='test',
params=None)
assert isinstance(model(x, y, None, None), EstimatorSpec)
def test_handle_predict_mode(self):
x = {'x': tf.placeholder(tf.float32, [2, 89])}
y = tf.constant([[1], [1]])
model = BaseModel(plx.Modes.PREDICT,
graph_fn=self.get_dummy_graph_fn(),
loss_config=LossConfig(module='log_loss'),
optimizer_config=OptimizerConfig(module='adadelta'),
model_type=BaseModel.Types.CLASSIFIER,
eval_metrics_config=[],
summaries=['learning_rate'],
name='test')
specs = model(x, y, None, None)
assert specs.loss is None
assert specs.predictions is not None
assert 'losses' not in specs.predictions
assert specs.train_op is None
def test_handle_eval_mode(self):
x = {'source_ids': tf.placeholder(tf.float32, [2, 89])}
y = tf.constant([[1], [1]])
model = BaseModel(plx.ModeKeys.EVAL,
graph_fn=self.get_dummy_graph_fn(),
loss_config=LossConfig(name='log_loss'),
optimizer_config=OptimizerConfig(name='Adadelta'),
eval_metrics_config=[],
model_type=BaseModel.Types.CLASSIFIER,
summaries=['learning_rate'],
name='test',
params=None)
specs = model(x, y, None, None)
assert specs.loss is not None
assert specs.predictions is not None
assert 'losses' in specs.predictions
assert specs.train_op is None
def test_build_no_summaries(self):
x = {'x': tf.placeholder(tf.float32, [2, 89])}
y = tf.constant([[1], [1]])
model = BaseModel(plx.Modes.TRAIN,
graph_fn=self.get_dummy_graph_fn(),
loss_config=LossConfig(module='log_loss'),
optimizer_config=OptimizerConfig(module='adadelta'),
model_type=BaseModel.Types.CLASSIFIER,
eval_metrics_config=[],
summaries=[],
name='test')
model(x, y, None, None)
# Only activations are created
summaries_by_names = get_tracked(
collection=tf.GraphKeys.SUMMARIES_BY_NAMES)
assert summaries_by_names == {}
def test_does_not_build_learning_rate_summaries_if_no_decay(self):
x = {'x': tf.placeholder(tf.float32, [2, 89])}
y = tf.constant([[1], [1]])
model = BaseModel(plx.Modes.TRAIN,
graph_fn=self.get_dummy_graph_fn(),
loss_config=LossConfig(module='log_loss'),
optimizer_config=OptimizerConfig(module='adadelta'),
model_type=BaseModel.Types.CLASSIFIER,
eval_metrics_config=[],
summaries=['learning_rate'],
name='test')
model(x, y, None, None)
# Only var are created
summaries_names = list(
get_tracked(collection=tf.GraphKeys.SUMMARIES_BY_NAMES).keys())
assert len(summaries_names) == 0
def __init__(self, mode, encoder_fn, decoder_fn, bridge_fn, loss_config=None,
optimizer_config=None, summaries='all', eval_metrics_config=None,
clip_gradients=0.5, clip_embed_gradients=0.1, name="Generator"):
optimizer_config = optimizer_config or OptimizerConfig('adadelta', learning_rate=0.4)
loss_config = loss_config or LossConfig(module='sigmoid_cross_entropy')
self._check_subgraph_fn(function=encoder_fn, function_name='encoder_fn')
self._check_subgraph_fn(function=decoder_fn, function_name='decoder_fn')
self._check_bridge_fn(function=bridge_fn)
self._encode_fn = encoder_fn
self._decoder_fn = decoder_fn
self._bridge_fn = bridge_fn
graph_fn = self._build_graph_fn()
super(Generator, self).__init__(
mode=mode, name=name, model_type=self.Types.GENERATOR, graph_fn=graph_fn,
loss_config=loss_config, optimizer_config=optimizer_config,
eval_metrics_config=eval_metrics_config, summaries=summaries,
clip_gradients=clip_gradients, clip_embed_gradients=clip_embed_gradients)
def test_build_variables_summaries(self):
x = {'x': tf.placeholder(tf.float32, [2, 89])}
y = tf.constant([[1], [1]])
model = BaseModel(plx.Modes.TRAIN,
graph_fn=self.get_dummy_graph_fn(),
loss_config=LossConfig(module='log_loss'),
optimizer_config=OptimizerConfig(module='adadelta'),
model_type=BaseModel.Types.CLASSIFIER,
eval_metrics_config=[],
summaries=['variables'],
name='test')
model(x, y, None, None)
# Only var are created
variable_names = {var.op.name for var in tf.trainable_variables()}
summaries_names = set(
get_tracked(collection=tf.GraphKeys.SUMMARIES_BY_NAMES).keys())
assert variable_names == summaries_names
def test_build_all_summaries(self):
training.create_global_step()
config = ModelConfig(loss_config=LossConfig(name='log_loss'),
optimizer_config=OptimizerConfig(
name='Adadelta',
decay_type='exponential_decay'))
x = {'source_ids': tf.placeholder(tf.float32, [2, 89])}
y = tf.constant([[1], [1]])
model = BaseModel(plx.ModeKeys.TRAIN,
graph_fn=self.get_dummy_graph_fn(),
config=config,
model_type=BaseModel.Types.CLASSIFIER,
summaries='all',
name='test',
params=None)
model(x, y, None, None)
# Only var are created
learning_rate_summaries = 0
activations_summaries = 0
gradients_summaries = 0
loss_summaries = 0
for s_name in get_tracked(
collection=tf.GraphKeys.SUMMARIES_BY_NAMES).keys():
if 'learning_rate' in s_name:
learning_rate_summaries += 1
elif 'Activation' in s_name:
activations_summaries += 1
elif 'Loss' in s_name:
loss_summaries += 1
elif 'Gradient' in s_name:
gradients_summaries += 1
assert learning_rate_summaries > 0
assert activations_summaries > 0
assert gradients_summaries > 0
assert loss_summaries > 0
def test_build_gradients_summaries(self):
x = {'source_ids': tf.placeholder(tf.float32, [2, 89])}
y = tf.constant([[1], [1]])
model = BaseModel(plx.ModeKeys.TRAIN,
graph_fn=self.get_dummy_graph_fn(),
loss_config=LossConfig(name='log_loss'),
optimizer_config=OptimizerConfig(name='Adadelta'),
model_type=BaseModel.Types.CLASSIFIER,
eval_metrics_config=[],
summaries=['gradients'],
name='test',
params=None)
model(x, y, None, None)
# Only gradients are created
summaries_by_names = get_tracked(
collection=tf.GraphKeys.SUMMARIES_BY_NAMES)
for s_name in summaries_by_names.keys():
assert 'Gradient' in s_name
def test_build_no_summaries(self):
config = ModelConfig(loss_config=LossConfig(name='log_loss'),
optimizer_config=OptimizerConfig(name='Adadelta'))
x = {'source_ids': tf.placeholder(tf.float32, [2, 89])}
y = tf.constant([[1], [1]])
model = BaseModel(plx.ModeKeys.TRAIN,
graph_fn=self.get_dummy_graph_fn(),
config=config,
model_type=BaseModel.Types.CLASSIFIER,
summaries=[],
name='test',
params=None)
model(x, y, None, None)
# Only activations are created
summaries_by_names = get_tracked(
collection=tf.GraphKeys.SUMMARIES_BY_NAMES)
assert summaries_by_names == {}
