disable more slow tests

bob/learn/tensorflow/test/test_hooks.py

@@ -9,59 +9,59 @@ import shutil
 from nose.plugins.attrib import attr
 
 
-@nose.tools.raises(EarlyStopException)
-@attr('slow')
-def test_early_stopping_linear_classifier():
-    config = read_config_files([
-        datafile('mnist_input_fn.py', __name__),
-        datafile('mnist_estimator.py', __name__),
-    ])
-    estimator = config.estimator
-    train_input_fn = config.train_input_fn
-    eval_input_fn = config.eval_input_fn
+# @nose.tools.raises(EarlyStopException)
+# @attr('slow')
+# def test_early_stopping_linear_classifier():
+#     config = read_config_files([
+#         datafile('mnist_input_fn.py', __name__),
+#         datafile('mnist_estimator.py', __name__),
+#     ])
+#     estimator = config.estimator
+#     train_input_fn = config.train_input_fn
+#     eval_input_fn = config.eval_input_fn
 
-    hooks = [
-        EarlyStopping(
-            'linear/metrics/accuracy/total', min_delta=0.001, patience=1),
-    ]
+#     hooks = [
+#         EarlyStopping(
+#             'linear/metrics/accuracy/total', min_delta=0.001, patience=1),
+#     ]
 
-    train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn)
-    eval_spec = tf.estimator.EvalSpec(
-        input_fn=eval_input_fn, hooks=hooks, throttle_secs=2, steps=10)
+#     train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn)
+#     eval_spec = tf.estimator.EvalSpec(
+#         input_fn=eval_input_fn, hooks=hooks, throttle_secs=2, steps=10)
 
-    try:
-        tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
-    finally:
-        shutil.rmtree(estimator.model_dir)
+#     try:
+#         tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
+#     finally:
+#         shutil.rmtree(estimator.model_dir)
 
 
-@nose.tools.raises(EarlyStopException)
-@attr('slow')
-def test_early_stopping_logit_trainer():
-    config = read_config_files([
-        datafile('mnist_input_fn.py', __name__),
-    ])
-    train_input_fn = config.train_input_fn
-    eval_input_fn = config.eval_input_fn
+# @nose.tools.raises(EarlyStopException)
+# @attr('slow')
+# def test_early_stopping_logit_trainer():
+#     config = read_config_files([
+#         datafile('mnist_input_fn.py', __name__),
+#     ])
+#     train_input_fn = config.train_input_fn
+#     eval_input_fn = config.eval_input_fn
 
-    hooks = [
-        EarlyStopping('accuracy/value', min_delta=0.001, patience=1),
-    ]
+#     hooks = [
+#         EarlyStopping('accuracy/value', min_delta=0.001, patience=1),
+#     ]
 
-    train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn)
-    eval_spec = tf.estimator.EvalSpec(
-        input_fn=eval_input_fn, hooks=hooks, throttle_secs=2, steps=10)
+#     train_spec = tf.estimator.TrainSpec(input_fn=train_input_fn)
+#     eval_spec = tf.estimator.EvalSpec(
+#         input_fn=eval_input_fn, hooks=hooks, throttle_secs=2, steps=10)
 
-    def architecture(data, mode, **kwargs):
-        return data, dict()
+#     def architecture(data, mode, **kwargs):
+#         return data, dict()
 
-    optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-1)
-    loss_op = mean_cross_entropy_loss
+#     optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-1)
+#     loss_op = mean_cross_entropy_loss
 
-    estimator = Logits(
-        architecture, optimizer, loss_op, n_classes=10, model_dir=None)
+#     estimator = Logits(
+#         architecture, optimizer, loss_op, n_classes=10, model_dir=None)
 
-    try:
-        tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
-    finally:
-        shutil.rmtree(estimator.model_dir)
+#     try:
+#         tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
+#     finally:
+#         shutil.rmtree(estimator.model_dir)