def _save_first_checkpoint(keras_model, estimator, custom_objects,
keras_weights):
"""Save first checkpoint for the keras Estimator.
Args:
keras_model: an instance of compiled keras model.
estimator: keras estimator.
custom_objects: Dictionary for custom objects.
keras_weights: A flat list of Numpy arrays for weights of given keras_model.
Returns:
The model_fn for a keras Estimator.
"""
# Load weights and save to checkpoint if there is no checkpoint
latest_path = saver_lib.latest_checkpoint(estimator.model_dir)
if not latest_path:
with ops.Graph().as_default():
random_seed.set_random_seed(estimator.config.tf_random_seed)
training_util.create_global_step()
model = _clone_and_build_model(model_fn_lib.ModeKeys.TRAIN, keras_model,
custom_objects)
# save to checkpoint
with session.Session(config=estimator._session_config) as sess:
if keras_weights:
model.set_weights(keras_weights)
# Make update ops and initialize all variables.
if not model.train_function:
# pylint: disable=protected-access
model._make_train_function()
K._initialize_variables(sess)
# pylint: enable=protected-access
saver = saver_lib.Saver()
saver.save(sess, os.path.join(estimator.model_dir, 'keras_model.ckpt'))
def _assert_all_close(self, expected, actual):
if not context.executing_eagerly():
with self.cached_session() as sess:
K._initialize_variables(sess)
self.assertAllClose(expected, actual)
else:
self.assertAllClose(expected, actual)
def test_bad_kernel_approximation(self, initializer, scale, exact_kernel_fn):
"""Approximation is bad when output dimension is small."""
# Two distinct inputs.
x = constant_op.constant([[1.0, -1.0, 0.5]])
y = constant_op.constant([[-1.0, 1.0, 1.0]])
small_output_dim = 10
random_seed.set_random_seed(1234)
# Initialize layer.
rff_layer = kernel_layers.RandomFourierFeatures(
output_dim=small_output_dim,
kernel_initializer=initializer,
scale=scale,
name='random_fourier_features')
# Apply layer to both inputs.
output_x = math.sqrt(2.0 / small_output_dim) * rff_layer.apply(x)
output_y = math.sqrt(2.0 / small_output_dim) * rff_layer.apply(y)
# The inner products of the outputs (on inputs x and y) approximates the
# real value of the RBF kernel but poorly since the output dimension of the
# layer is small.
exact_kernel_value = exact_kernel_fn(x, y)
approx_kernel_value = kernelized_utils.inner_product(output_x, output_y)
abs_error = math_ops.abs(exact_kernel_value - approx_kernel_value)
if not context.executing_eagerly():
with self.cached_session() as sess:
keras_backend._initialize_variables(sess)
abs_error_eval = sess.run([abs_error])
self.assertGreater(abs_error_eval[0][0], 0.05)
self.assertLess(abs_error_eval[0][0], 0.5)
else:
self.assertGreater(abs_error, 0.05)
self.assertLess(abs_error, 0.5)
def test_bad_kernel_approximation(self, initializer, scale, exact_kernel_fn):
"""Approximation is bad when output dimension is small."""
# Two distinct inputs.
x = constant_op.constant([[1.0, -1.0, 0.5]])
y = constant_op.constant([[-1.0, 1.0, 1.0]])
small_output_dim = 10
random_seed.set_random_seed(1234)
# Initialize layer.
rff_layer = kernel_layers.RandomFourierFeatures(
output_dim=small_output_dim,
kernel_initializer=initializer,
scale=scale,
name='random_fourier_features')
# Apply layer to both inputs.
output_x = math.sqrt(2.0 / small_output_dim) * rff_layer(x)
output_y = math.sqrt(2.0 / small_output_dim) * rff_layer(y)
# The inner products of the outputs (on inputs x and y) approximates the
# real value of the RBF kernel but poorly since the output dimension of the
# layer is small.
exact_kernel_value = exact_kernel_fn(x, y)
approx_kernel_value = kernelized_utils.inner_product(output_x, output_y)
abs_error = math_ops.abs(exact_kernel_value - approx_kernel_value)
if not context.executing_eagerly():
with self.cached_session() as sess:
keras_backend._initialize_variables(sess)
abs_error_eval = sess.run([abs_error])
self.assertGreater(abs_error_eval[0][0], 0.05)
self.assertLess(abs_error_eval[0][0], 0.5)
else:
self.assertGreater(abs_error, 0.05)
self.assertLess(abs_error, 0.5)
def _assert_all_close(self, expected, actual):
if not context.executing_eagerly():
with self.cached_session() as sess:
K._initialize_variables(sess)
self.assertAllClose(expected, actual)
else:
self.assertAllClose(expected, actual)
def _assert_all_close(self, expected, actual, atol=0.001):
if not context.executing_eagerly():
with self.cached_session() as sess:
keras_backend._initialize_variables(sess)
self.assertAllClose(expected, actual, atol=atol)
else:
self.assertAllClose(expected, actual, atol=atol)
def _save_first_checkpoint(keras_model, estimator, custom_objects,
keras_weights):
"""Save first checkpoint for the keras Estimator.
Args:
keras_model: an instance of compiled keras model.
estimator: keras estimator.
custom_objects: Dictionary for custom objects.
keras_weights: A flat list of Numpy arrays for weights of given keras_model.
Returns:
The model_fn for a keras Estimator.
"""
# Load weights and save to checkpoint if there is no checkpoint
latest_path = saver_lib.latest_checkpoint(estimator.model_dir)
if not latest_path:
with ops.Graph().as_default():
random_seed.set_random_seed(estimator.config.tf_random_seed)
training_util.create_global_step()
model = _clone_and_build_model(model_fn_lib.ModeKeys.TRAIN,
keras_model, custom_objects)
# save to checkpoint
with session.Session(config=estimator._session_config) as sess:
if keras_weights:
model.set_weights(keras_weights)
# Make update ops and initialize all variables.
if not model.train_function:
# pylint: disable=protected-access
model._make_train_function()
K._initialize_variables(sess)
# pylint: enable=protected-access
saver = saver_lib.Saver()
saver.save(
sess, os.path.join(estimator.model_dir,
'keras_model.ckpt'))
def _assert_all_close(self, expected, actual, atol=0.001):
if not context.executing_eagerly():
with self.cached_session() as sess:
keras_backend._initialize_variables(sess)
self.assertAllClose(expected, actual, atol=atol)
else:
self.assertAllClose(expected, actual, atol=atol)
def _save_first_checkpoint(keras_model, custom_objects, config,
save_object_ckpt):
"""Save first checkpoint for the keras Estimator.
Args:
keras_model: an instance of compiled keras model.
custom_objects: Dictionary for custom objects.
config: Estimator config.
save_object_ckpt: Whether to save an object-based checkpoint.
Returns:
The path where keras model checkpoint is saved.
"""
# save checkpoint into subdirectory to allow warm start
keras_model_dir = os.path.join(config.model_dir, 'keras')
# Load weights and save to checkpoint if there is no checkpoint
latest_path = tf.train.latest_checkpoint(keras_model_dir)
if not latest_path:
keras_weights = None
if _any_weight_initialized(keras_model):
keras_weights = keras_model.get_weights()
if not tf.compat.v1.gfile.IsDirectory(keras_model_dir):
tf.compat.v1.gfile.MakeDirs(keras_model_dir)
with tf.Graph().as_default():
tf.compat.v1.random.set_random_seed(config.tf_random_seed)
tf.compat.v1.train.create_global_step()
model = _clone_and_build_model(ModeKeys.TRAIN, keras_model,
custom_objects)
# Init the train_function outside of the context of session. This is due
# to the fact that train function will update the graph by adding backprop
# parts. This will potentially trying to update the node in forward graph
# which will fail if it is done within same session.
# Always create the train_function here since the model is just cloned.
# See https://github.com/tensorflow/tensorflow/issues/27750 for details.
model._make_train_function() # pylint: disable=protected-access
# save to checkpoint
with tf.compat.v1.Session(config=config.session_config) as sess:
if keras_weights:
model.set_weights(keras_weights)
# model._make_train_function() will potentially create the optimizer
# variable, which will require another variable initialization.
K._initialize_variables(sess) # pylint: disable=protected-access
if save_object_ckpt:
model._track_trackable( # pylint: disable=protected-access
tf.compat.v1.train.get_global_step(),
'estimator_global_step')
latest_path = os.path.join(keras_model_dir,
'keras_model.ckpt')
model.save_weights(latest_path)
else:
saver = tf.compat.v1.train.Saver()
latest_path = os.path.join(keras_model_dir,
'keras_model.ckpt')
saver.save(sess, latest_path)
return latest_path
def init_restore_or_wait_for_variables():
"""Initialize or restore variables or wait for variables to be initialized."""
session = K._get_session() # pylint: disable=protected-access
worker_context = dc_context.get_current_worker_context()
if not worker_context or worker_context.experimental_should_init:
# TODO(yuefengz): if checkpoints exist, restore from checkpoint.
K._initialize_variables(session) # pylint: disable=protected-access
else:
_wait_for_variable_initialization(session)
def init_restore_or_wait_for_variables():
"""Initialize or restore variables or wait for variables to be initialized."""
session = K._get_session() # pylint: disable=protected-access
if not multi_worker_util.has_worker_context(
) or multi_worker_util.should_load_checkpoint():
# TODO(yuefengz): if checkpoints exist, restore from checkpoint.
K._initialize_variables(session) # pylint: disable=protected-access
else:
_wait_for_variable_initialization(session)
def init_restore_or_wait_for_variables():
"""Initialize or restore variables or wait for variables to be initialized."""
session = K._get_session() # pylint: disable=protected-access
worker_context = dc_context.get_current_worker_context()
if not worker_context or worker_context.should_init:
# TODO(yuefengz): if checkpoints exit, restore from checkpoint.
K._initialize_variables(session) # pylint: disable=protected-access
else:
_wait_for_variable_initialization(session)
def _save_first_checkpoint(keras_model, custom_objects, config,
save_object_ckpt):
"""Save first checkpoint for the keras Estimator.
Args:
keras_model: an instance of compiled keras model.
custom_objects: Dictionary for custom objects.
config: Estimator config.
save_object_ckpt: Whether to save an object-based checkpoint.
Returns:
The path where keras model checkpoint is saved.
"""
# save checkpoint into subdirectory to allow warm start
keras_model_dir = os.path.join(config.model_dir, 'keras')
# Load weights and save to checkpoint if there is no checkpoint
latest_path = checkpoint_management.latest_checkpoint(keras_model_dir)
if not latest_path:
keras_weights = None
if _any_weight_initialized(keras_model):
keras_weights = keras_model.get_weights()
if not gfile.IsDirectory(keras_model_dir):
gfile.MakeDirs(keras_model_dir)
with ops.Graph().as_default():
random_seed.set_random_seed(config.tf_random_seed)
training_util.create_global_step()
model = _clone_and_build_model(ModeKeys.TRAIN, keras_model,
custom_objects)
# save to checkpoint
with session.Session(config=config.session_config) as sess:
if keras_weights:
model.set_weights(keras_weights)
# Make update ops and initialize all variables.
if not model.train_function:
# pylint: disable=protected-access
model._make_train_function()
K._initialize_variables(sess)
# pylint: enable=protected-access
if save_object_ckpt:
model._track_trackable( # pylint: disable=protected-access
training_util.get_global_step(),
'estimator_global_step')
latest_path = os.path.join(keras_model_dir,
'keras_model.ckpt')
model.save_weights(latest_path)
else:
saver = saver_lib.Saver()
latest_path = os.path.join(keras_model_dir,
'keras_model.ckpt')
saver.save(sess, latest_path)
return latest_path
def _save_first_checkpoint(keras_model, custom_objects, config):
"""Save first checkpoint for the keras Estimator.
Args:
keras_model: an instance of compiled keras model.
custom_objects: Dictionary for custom objects.
config: Estimator config.
Returns:
The path where keras model checkpoint is saved.
"""
# save checkpoint into subdirectory to allow warm start
keras_model_dir = os.path.join(config.model_dir, 'keras')
# Load weights and save to checkpoint if there is no checkpoint
latest_path = checkpoint_management.latest_checkpoint(keras_model_dir)
if not latest_path:
keras_weights = None
if _any_weight_initialized(keras_model):
keras_weights = keras_model.get_weights()
if not gfile.IsDirectory(keras_model_dir):
gfile.MakeDirs(keras_model_dir)
with ops.Graph().as_default():
random_seed.set_random_seed(config.tf_random_seed)
training_util.create_global_step()
model = _clone_and_build_model(model_fn_lib.ModeKeys.TRAIN,
keras_model, custom_objects)
# save to checkpoint
with session.Session(config=config.session_config) as sess:
if keras_weights:
model.set_weights(keras_weights)
# Make update ops and initialize all variables.
if not model.train_function:
# pylint: disable=protected-access
model._make_train_function()
# We are using global variables collection here because:
# estimator runs eager mode under context.graph_mode() context manager
# When we try to get all the TF optimizer variables using
# optimizer.variables() we try to return variables that belong to the
# current graph. This check (variable.op.graph is current_graph) will
# error as the context is graph mode but variables are eager.
# TODO(psv): investigate this and see if we can remove the usage of
# collection here.
K._initialize_variables(
sess, variables_module.global_variables())
# pylint: enable=protected-access
saver = saver_lib.Saver()
latest_path = os.path.join(keras_model_dir, 'keras_model.ckpt')
saver.save(sess, latest_path)
return latest_path
def _save_first_checkpoint(keras_model, custom_objects, config):
"""Save first checkpoint for the keras Estimator.
Args:
keras_model: an instance of compiled keras model.
custom_objects: Dictionary for custom objects.
config: Estimator config.
Returns:
The path where keras model checkpoint is saved.
"""
# save checkpoint into subdirectory to allow warm start
keras_model_dir = os.path.join(config.model_dir, 'keras')
# Load weights and save to checkpoint if there is no checkpoint
latest_path = checkpoint_management.latest_checkpoint(keras_model_dir)
if not latest_path:
keras_weights = None
if _any_weight_initialized(keras_model):
keras_weights = keras_model.get_weights()
if not gfile.IsDirectory(keras_model_dir):
gfile.MakeDirs(keras_model_dir)
with ops.Graph().as_default():
random_seed.set_random_seed(config.tf_random_seed)
training_util.create_global_step()
model = _clone_and_build_model(model_fn_lib.ModeKeys.TRAIN, keras_model,
custom_objects)
# save to checkpoint
with session.Session(config=config.session_config) as sess:
if keras_weights:
model.set_weights(keras_weights)
# Make update ops and initialize all variables.
if not model.train_function:
# pylint: disable=protected-access
model._make_train_function()
K._initialize_variables(sess)
# pylint: enable=protected-access
saver = saver_lib.Saver()
latest_path = os.path.join(keras_model_dir, 'keras_model.ckpt')
saver.save(sess, latest_path)
return latest_path
def _build(self, shape):
"""Initialize TP, FP, TN, and FN tensors, given the shape of the data."""
if self.multi_label:
if shape.ndims != 2:
raise ValueError(
'`y_true` must have rank=2 when `multi_label` is '
'True. Found rank %s.' % shape.ndims)
variable_shape = tensor_shape.TensorShape(
[tensor_shape.Dimension(self.num_thresholds), shape[1]])
else:
variable_shape = tensor_shape.TensorShape(
[tensor_shape.Dimension(self.num_thresholds)])
# Create metric variables
self.true_positives = self.add_weight(
'true_positives',
shape=variable_shape,
initializer=init_ops.zeros_initializer)
self.true_negatives = self.add_weight(
'true_negatives',
shape=variable_shape,
initializer=init_ops.zeros_initializer)
self.false_positives = self.add_weight(
'false_positives',
shape=variable_shape,
initializer=init_ops.zeros_initializer)
self.false_negatives = self.add_weight(
'false_negatives',
shape=variable_shape,
initializer=init_ops.zeros_initializer)
if self.multi_label:
with ops.init_scope():
# This should only be necessary for handling v1 behavior. In v2, AUC
# should be initialized outside of any tf.functions, and therefore in
# eager mode.
if not context.executing_eagerly():
K._initialize_variables(K._get_session()) # pylint: disable=protected-access
self._built = True
def init_restore_or_wait_for_variables():
"""Initialize or restore variables or wait for variables to be initialized."""
backend._initialize_variables(backend._get_session()) # pylint: disable=protected-access
