def test_log_loss_config(self):
config_dict = {
'input_layer': 'images',
'output_layer': 'relu_1',
'epsilon': 0.0001,
'weights': 1.0,
'name': 'l',
'collect': False
}
config = LogLossConfig.from_dict(config_dict)
self.assert_equal_losses(config.to_dict(), config_dict)
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=LogLossConfig(),
optimizer=AdadeltaConfig(),
model_type=BaseModel.Types.CLASSIFIER,
metrics=[],
summaries=['learning_rate'],
name='test')
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=LogLossConfig(),
optimizer=AdadeltaConfig(),
model_type=BaseModel.Types.CLASSIFIER,
metrics=[],
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_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=LogLossConfig(),
optimizer=AdadeltaConfig(),
model_type=BaseModel.Types.CLASSIFIER,
metrics=[],
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=LogLossConfig(),
optimizer=AdadeltaConfig(),
model_type=BaseModel.Types.CLASSIFIER,
metrics=[],
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 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=LogLossConfig(),
optimizer=AdadeltaConfig(),
model_type=BaseModel.Types.CLASSIFIER,
metrics=[],
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()
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=LogLossConfig(),
optimizer=AdadeltaConfig(decay_type='exponential_decay'),
model_type=BaseModel.Types.CLASSIFIER,
metrics=[],
summaries='all',
name='test')
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