def testWithEpochLimit(self):
predictions_limited = input.limit_epochs(self._predictions, num_epochs=1)
labels_limited = input.limit_epochs(self._labels, num_epochs=1)
value_op, update_op = metric_ops.streaming_accuracy(
predictions_limited, labels_limited)
init_op = control_flow_ops.group(variables.global_variables_initializer(),
variables.local_variables_initializer())
# Create checkpoint and log directories:
chkpt_dir = os.path.join(self.get_temp_dir(), 'tmp_logs/')
gfile.MakeDirs(chkpt_dir)
logdir = os.path.join(self.get_temp_dir(), 'tmp_logs2/')
gfile.MakeDirs(logdir)
# Save initialized variables to a checkpoint directory:
saver = saver_lib.Saver()
with self.test_session() as sess:
init_op.run()
saver.save(sess, os.path.join(chkpt_dir, 'chkpt'))
# Now, run the evaluation loop:
accuracy_value = evaluation.evaluation_loop(
'', chkpt_dir, logdir, eval_op=update_op, final_op=value_op,
max_number_of_evaluations=1, num_evals=10000)
self.assertAlmostEqual(accuracy_value, self._expected_accuracy)
def _fn():
x = constant_op.constant(points)
if batch_size == num_points:
return input_lib.limit_epochs(x, num_epochs=num_epochs), None
if randomize:
indices = random_ops.random_uniform(
constant_op.constant([batch_size]),
minval=0,
maxval=num_points - 1,
dtype=dtypes.int32,
seed=10)
else:
# We need to cycle through the indices sequentially. We create a queue
# to maintain the list of indices.
q = data_flow_ops.FIFOQueue(num_points, dtypes.int32, ())
# Conditionally initialize the Queue.
def _init_q():
with ops.control_dependencies(
[q.enqueue_many(math_ops.range(num_points))]):
return control_flow_ops.no_op()
init_q = control_flow_ops.cond(q.size() <= 0, _init_q,
control_flow_ops.no_op)
with ops.control_dependencies([init_q]):
offsets = q.dequeue_many(batch_size)
with ops.control_dependencies([q.enqueue_many(offsets)]):
indices = array_ops.identity(offsets)
batch = array_ops.gather(x, indices)
return (input_lib.limit_epochs(batch, num_epochs=num_epochs), None)
def _fn():
x = constant_op.constant(points)
if batch_size == num_points:
return input_lib.limit_epochs(x, num_epochs=num_epochs), None
indices = random_ops.random_uniform(
constant_op.constant([batch_size]),
minval=0,
maxval=num_points - 1,
dtype=dtypes.int32,
seed=10)
batch = array_ops.gather(x, indices)
return (input_lib.limit_epochs(batch, num_epochs=num_epochs), None)
def _input_fn(num_epochs=None):
features = {
'age':
input_lib.limit_epochs(
constant_op.constant([[0.8], [0.15], [0.]]),
num_epochs=num_epochs),
'language':
sparse_tensor.SparseTensor(
values=input_lib.limit_epochs(
['en', 'fr', 'zh'], num_epochs=num_epochs),
indices=[[0, 0], [0, 1], [2, 0]],
dense_shape=[3, 2])
}
return features, constant_op.constant([1., 0., 0.2], dtype=dtypes.float32)
def _input_fn(num_epochs=None):
features = {
'age':
input_lib.limit_epochs(
constant_op.constant(((50,), (20,), (10,))),
num_epochs=num_epochs),
'language':
sparse_tensor.SparseTensor(
values=input_lib.limit_epochs(
('en', 'fr', 'zh'), num_epochs=num_epochs),
indices=((0, 0), (0, 1), (2, 0)),
dense_shape=(3, 2))
}
return features, constant_op.constant(
((0.8,), (0.,), (0.2,)), dtype=dtypes.float32)
def _eval_input_fn():
feature_map = parsing_ops.parse_example(
input_lib.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features = linear_testing_utils.queue_parsed_features(feature_map)
labels = features.pop('y')
return features, labels
def _predict_input_fn():
feature_map = parsing_ops.parse_example(
input_lib.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features = _queue_parsed_features(feature_map)
features.pop('y')
return features, None
def boston_input_fn(num_epochs=None):
boston = base.load_boston()
features = input_lib.limit_epochs(array_ops.reshape(
constant_op.constant(boston.data), [-1, _BOSTON_INPUT_DIM]),
num_epochs=num_epochs)
labels = array_ops.reshape(constant_op.constant(boston.target), [-1, 1])
return features, labels
def _eval_input_fn():
feature_map = parsing_ops.parse_example(
input_lib.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features = _queue_parsed_features(feature_map)
labels = features.pop('y')
return features, labels
def _predict_input_fn():
feature_map = parsing_ops.parse_example(
input_lib.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features = linear_testing_utils.queue_parsed_features(feature_map)
features.pop('y')
return features, None
def boston_input_fn(num_epochs=None):
boston = base.load_boston()
features = input_lib.limit_epochs(
array_ops.reshape(
constant_op.constant(boston.data), [-1, _BOSTON_INPUT_DIM]),
num_epochs=num_epochs)
labels = array_ops.reshape(constant_op.constant(boston.target), [-1, 1])
return features, labels
def _input_fn(num_epochs=None):
# Create 4 rows, one of them (y = x), three of them (y=Not(x))
labels = constant_op.constant([[1.], [0.], [0.], [0.]])
features = {
'x':
input_lib.limit_epochs(
array_ops.ones(shape=[4, 1], dtype=dtypes.float32),
num_epochs=num_epochs),
}
return features, labels
def testTrainWithEpochLimit(self):
logdir = os.path.join(tempfile.mkdtemp(prefix=self.get_temp_dir()),
'tmp_logs')
with ops.Graph().as_default():
random_seed.set_random_seed(0)
tf_inputs = constant_op.constant(self._inputs, dtype=dtypes.float32)
tf_labels = constant_op.constant(self._labels, dtype=dtypes.float32)
tf_inputs_limited = input_lib.limit_epochs(tf_inputs, num_epochs=300)
tf_labels_limited = input_lib.limit_epochs(tf_labels, num_epochs=300)
tf_predictions = LogisticClassifier(tf_inputs_limited)
loss_ops.log_loss(tf_predictions, tf_labels_limited)
total_loss = loss_ops.get_total_loss()
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)
train_op = learning.create_train_op(total_loss, optimizer)
loss = learning.train(train_op, logdir, log_every_n_steps=10)
self.assertIsNotNone(loss)
self.assertLess(loss, .015)
self.assertTrue(os.path.isfile('{}/model.ckpt-300.index'.format(logdir)))
self.assertTrue(os.path.isfile('{}/model.ckpt-300.data-00000-of-00001'.format(logdir)))
def testTrainWithEpochLimit(self):
logdir = tempfile.mkdtemp('tmp_logs')
with ops.Graph().as_default():
random_seed.set_random_seed(0)
tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
tf_labels = tf.constant(self._labels, dtype=tf.float32)
tf_inputs_limited = input_lib.limit_epochs(tf_inputs, num_epochs=300)
tf_labels_limited = input_lib.limit_epochs(tf_labels, num_epochs=300)
tf_predictions = LogisticClassifier(tf_inputs_limited)
loss_ops.log_loss(tf_labels_limited, tf_predictions)
total_loss = loss_ops.get_total_loss()
optimizer = gradient_descent.GradientDescentOptimizer(learning_rate=1.0)
train_op = learning.create_train_op(total_loss, optimizer)
loss = learning.train(train_op, logdir, log_every_n_steps=10)
self.assertIsNotNone(loss)
self.assertLess(loss, .015)
self.assertTrue(os.path.isfile('{}/model.ckpt-300.index'.format(logdir)))
self.assertTrue(
os.path.isfile('{}/model.ckpt-300.data-00000-of-00001'.format(logdir)))
def _input_fn():
return (input_lib.limit_epochs(constant_op.constant(points),
num_epochs=1), None)
def _predict_input_fn():
features = parsing_ops.parse_example(
input_lib.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features.pop('label')
return features, None
def _input_fn():
return (input_lib.limit_epochs(
constant_op.constant(points), num_epochs=1), None)
def _predict_input_fn():
features = parsing_ops.parse_example(
input_lib.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
features.pop('label')
return features, None
def _eval_input_fn():
features = parsing_ops.parse_example(
input_lib.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
labels = features.pop('label')
return features, labels
def _input_fn(instances):
features = {
'features':
input_lib.limit_epochs(tf.constant(instances), num_epochs=1)
}
return features, None
def _eval_input_fn():
features = parsing_ops.parse_example(
input_lib.limit_epochs(serialized_examples, num_epochs=1),
feature_spec)
labels = features.pop('label')
return features, labels
