def testMultiClassWithInvalidNClass(self):
try:
target_column_lib.multi_class_target(n_classes=1)
self.fail("Softmax with no n_classes did not raise error.")
except ValueError:
# Expected
pass
def testMultiClassWithInvalidNClass(self):
try:
target_column_lib.multi_class_target(n_classes=1)
self.fail("Softmax with no n_classes did not raise error.")
except ValueError:
# Expected
pass
def _getModelFnOpsForMode(self, mode):
"""Helper for testGetRnnModelFn{Train,Eval,Infer}()."""
num_units = [4]
seq_columns = [
feature_column.real_valued_column(
'inputs', dimension=1)
]
features = {
'inputs': constant_op.constant([1., 2., 3.]),
}
labels = constant_op.constant([1., 0., 1.])
model_fn = ssre._get_rnn_model_fn(
cell_type='basic_rnn',
target_column=target_column_lib.multi_class_target(n_classes=2),
optimizer='SGD',
num_unroll=2,
num_units=num_units,
num_threads=1,
queue_capacity=10,
batch_size=1,
# Only CLASSIFICATION yields eval metrics to test for.
problem_type=constants.ProblemType.CLASSIFICATION,
sequence_feature_columns=seq_columns,
context_feature_columns=None,
learning_rate=0.1)
model_fn_ops = model_fn(features=features, labels=labels, mode=mode)
return model_fn_ops
def _getModelFnOpsForMode(self, mode):
"""Helper for testGetRnnModelFn{Train,Eval,Infer}()."""
num_units = [4]
seq_columns = [
feature_column.real_valued_column(
'inputs', dimension=1)
]
features = {
'inputs': constant_op.constant([1., 2., 3.]),
}
labels = constant_op.constant([1., 0., 1.])
model_fn = ssre._get_rnn_model_fn(
cell_type='basic_rnn',
target_column=target_column_lib.multi_class_target(n_classes=2),
optimizer='SGD',
num_unroll=2,
num_units=num_units,
num_threads=1,
queue_capacity=10,
batch_size=1,
# Only CLASSIFICATION yields eval metrics to test for.
problem_type=constants.ProblemType.CLASSIFICATION,
sequence_feature_columns=seq_columns,
context_feature_columns=None,
learning_rate=0.1)
model_fn_ops = model_fn(features=features, labels=labels, mode=mode)
return model_fn_ops
def testMultiClass(self):
target_column = target_column_lib.multi_class_target(n_classes=3)
with ops.Graph().as_default(), session.Session() as sess:
logits = constant_op.constant([[1., 0., 0.]])
labels = constant_op.constant([2])
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(
1.5514446, sess.run(target_column.loss(logits, labels, {})))
def testMultiClass(self):
target_column = target_column_lib.multi_class_target(n_classes=3)
with ops.Graph().as_default(), session.Session() as sess:
logits = constant_op.constant([[1., 0., 0.]])
labels = constant_op.constant([2])
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(1.5514446,
sess.run(target_column.loss(logits, labels, {})))
def testBinaryClassification(self):
target_column = target_column_lib.multi_class_target(n_classes=2)
with ops.Graph().as_default(), session.Session() as sess:
logits = constant_op.constant([[1.], [1.]])
labels = constant_op.constant([[1.], [0.]])
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(0.81326175,
sess.run(
target_column.loss(logits, labels, {})),
delta=1e-6)
def testBinaryClassification(self):
target_column = target_column_lib.multi_class_target(n_classes=2)
with ops.Graph().as_default(), session.Session() as sess:
logits = constant_op.constant([[1.], [1.]])
labels = constant_op.constant([[1.], [0.]])
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(
0.81326175,
sess.run(target_column.loss(logits, labels, {})),
delta=1e-6)
def testMultiClassWithWeight(self):
target_column = target_column_lib.multi_class_target(
n_classes=3, weight_column_name="label_weight")
with ops.Graph().as_default(), session.Session() as sess:
features = {"label_weight": constant_op.constant([0.1])}
logits = constant_op.constant([[1., 0., 0.]])
labels = constant_op.constant([2])
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(
1.5514446, sess.run(target_column.loss(logits, labels, features)))
def testMultiClassWithWeight(self):
target_column = target_column_lib.multi_class_target(
n_classes=3, weight_column_name="label_weight")
with ops.Graph().as_default(), session.Session() as sess:
features = {"label_weight": constant_op.constant([0.1])}
logits = constant_op.constant([[1., 0., 0.]])
labels = constant_op.constant([2])
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(
1.5514446,
sess.run(target_column.loss(logits, labels, features)))
def testBinaryEvalMetrics(self):
target_column = target_column_lib.multi_class_target(n_classes=2)
with ops.Graph().as_default(), session.Session() as sess:
logits = constant_op.constant([[1.], [1.], [-1.]])
labels = constant_op.constant([[1.], [0.], [1.]])
eval_dict = target_column.get_eval_ops({}, logits, labels)
# TODO(zakaria): test all metrics
accuracy_op, update_op = eval_dict["accuracy/threshold_0.500000_mean"]
sess.run(variables.global_variables_initializer())
sess.run(variables.local_variables_initializer())
sess.run(update_op)
self.assertAlmostEqual(1.0 / 3, sess.run(accuracy_op))
def testBinaryClassificationWithWeights(self):
target_column = target_column_lib.multi_class_target(
n_classes=2, weight_column_name="label_weight")
with ops.Graph().as_default(), session.Session() as sess:
features = {"label_weight": constant_op.constant([[1.], [0.]])}
logits = constant_op.constant([[1.], [1.]])
labels = constant_op.constant([[1.], [0.]])
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(
.31326166,
sess.run(target_column.loss(logits, labels, features)),
delta=1e-6)
def testMultiClassEvalMetrics(self):
target_column = target_column_lib.multi_class_target(n_classes=3)
with ops.Graph().as_default(), session.Session() as sess:
logits = constant_op.constant([[1., 0., 0.]])
labels = constant_op.constant([2])
eval_dict = target_column.get_eval_ops({}, logits, labels)
loss_op, update_op = eval_dict["loss"]
sess.run(variables.global_variables_initializer())
sess.run(variables.local_variables_initializer())
sess.run(update_op)
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(1.5514446, sess.run(loss_op))
def testMultiClassEvalMetrics(self):
target_column = target_column_lib.multi_class_target(n_classes=3)
with ops.Graph().as_default(), session.Session() as sess:
logits = constant_op.constant([[1., 0., 0.]])
labels = constant_op.constant([2])
eval_dict = target_column.get_eval_ops({}, logits, labels)
loss_op, update_op = eval_dict["loss"]
sess.run(variables.global_variables_initializer())
sess.run(variables.local_variables_initializer())
sess.run(update_op)
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(1.5514446, sess.run(loss_op))
def testBinaryEvalMetrics(self):
target_column = target_column_lib.multi_class_target(n_classes=2)
with ops.Graph().as_default(), session.Session() as sess:
logits = constant_op.constant([[1.], [1.], [-1.]])
labels = constant_op.constant([[1.], [0.], [1.]])
eval_dict = target_column.get_eval_ops({}, logits, labels)
# TODO(zakaria): test all metrics
accuracy_op, update_op = eval_dict[
"accuracy/threshold_0.500000_mean"]
sess.run(variables.global_variables_initializer())
sess.run(variables.local_variables_initializer())
sess.run(update_op)
self.assertAlmostEqual(1.0 / 3, sess.run(accuracy_op))
def testBinaryClassificationWithWeights(self):
target_column = target_column_lib.multi_class_target(
n_classes=2, weight_column_name="label_weight")
with ops.Graph().as_default(), session.Session() as sess:
features = {"label_weight": constant_op.constant([[1.], [0.]])}
logits = constant_op.constant([[1.], [1.]])
labels = constant_op.constant([[1.], [0.]])
# logloss: z:label, x:logit
# z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
self.assertAlmostEqual(
.31326166,
sess.run(target_column.loss(logits, labels, features)),
delta=1e-6)
def _GetModelFnOpsForMode(self, mode):
"""Helper for testGetDynamicRnnModelFn{Train,Eval,Infer}()."""
model_fn = dynamic_rnn_estimator._get_dynamic_rnn_model_fn(
self.rnn_cell,
target_column=target_column_lib.multi_class_target(n_classes=2),
# Only CLASSIFICATION yields eval metrics to test for.
problem_type=dynamic_rnn_estimator.ProblemType.CLASSIFICATION,
prediction_type=dynamic_rnn_estimator.PredictionType.MULTIPLE_VALUE,
optimizer='SGD',
sequence_feature_columns=self.sequence_feature_columns,
context_feature_columns=self.context_feature_columns,
learning_rate=0.1)
labels = self.GetClassificationTargetsOrNone(mode)
model_fn_ops = model_fn(
features=self.GetColumnsToTensors(), labels=labels, mode=mode)
return model_fn_ops
def _GetModelFnOpsForMode(self, mode):
"""Helper for testGetDynamicRnnModelFn{Train,Eval,Infer}()."""
model_fn = dynamic_rnn_estimator._get_dynamic_rnn_model_fn(
self.rnn_cell,
target_column=target_column_lib.multi_class_target(n_classes=2),
# Only CLASSIFICATION yields eval metrics to test for.
problem_type=dynamic_rnn_estimator.ProblemType.CLASSIFICATION,
prediction_type=dynamic_rnn_estimator.PredictionType.MULTIPLE_VALUE,
optimizer='SGD',
sequence_feature_columns=self.sequence_feature_columns,
context_feature_columns=self.context_feature_columns,
learning_rate=0.1)
labels = self.GetClassificationTargetsOrNone(mode)
model_fn_ops = model_fn(
features=self.GetColumnsToTensors(), labels=labels, mode=mode)
return model_fn_ops
