def testInitFromCheckpointWithScopes(self):
model_dir = tempfile.mkdtemp(prefix=os.path.join(
self.get_temp_dir(), 'init_from_checkpoint_with_scopes'))
init_value0 = np.asarray(
[1.0, 3.0, 9.0], dtype=np.float32).reshape((1, 3, 1))
init_value1 = np.asarray(
[2.0, 4.0, 6.0, 8.0], dtype=np.float32).reshape((2, 1, 2))
var_names_to_values = {'layer0/v0': init_value0, 'layer1/v1': init_value1}
with self.test_session() as sess:
model_path = self.create_checkpoint_from_values(var_names_to_values,
model_dir)
with variable_scope.variable_scope('my_model/my_layer0'):
var0 = variables_lib2.variable('my_var0', shape=init_value0.shape)
with variable_scope.variable_scope('my_model/my_layer1'):
var1 = variables_lib2.variable('my_var1', shape=init_value1.shape)
vars_to_restore = {'layer0/v0': var0, 'layer1/v1': var1}
op, feed_dict = variables_lib2.assign_from_checkpoint(model_path,
vars_to_restore)
# Initialize the variables.
sess.run(variables_lib.global_variables_initializer())
# Perform the assignment.
sess.run(op, feed_dict)
# Request and test the variable values:
self.assertAllEqual(init_value0, var0.eval())
self.assertAllEqual(init_value1, var1.eval())
def testLoadExistingVariables(self):
model_dir = tempfile.mkdtemp(prefix=os.path.join(self.get_temp_dir(),
'load_existing_variables'))
init_value0 = 10.0
init_value1 = 20.0
var_names_to_values = {'v0': init_value0, 'v1': init_value1}
with self.test_session() as sess:
model_path = self.create_checkpoint_from_values(var_names_to_values,
model_dir)
var0 = variables_lib2.variable('my_var0', shape=[])
var1 = variables_lib2.variable('my_var1', shape=[])
vars_to_restore = {'v0': var0, 'v1': var1}
op, feed_dict = variables_lib2.assign_from_checkpoint(model_path,
vars_to_restore)
# Initialize the variables.
sess.run(variables_lib.global_variables_initializer())
# Perform the assignment.
sess.run(op, feed_dict)
# Request and test the variable values:
self.assertEqual(init_value0, var0.eval())
self.assertEqual(init_value1, var1.eval())
def testRaisesValueErrorIfAVariableIsntFound(self):
model_dir = tempfile.mkdtemp(prefix=os.path.join(
self.get_temp_dir(), 'raises_value_error_if_var_isnt_found'))
init_value0 = 10.0
init_value1 = 20.0
var_names_to_values = {'v0': init_value0, 'v1': init_value1}
with self.test_session():
model_path = self.create_checkpoint_from_values(var_names_to_values,
model_dir)
var0 = variables_lib2.variable('my_var0', shape=[])
var1 = variables_lib2.variable('my_var1', shape=[])
vars_to_restore = {'v0_fake': var0, 'v1': var1}
with self.assertRaises(ValueError):
variables_lib2.assign_from_checkpoint(model_path, vars_to_restore)
def testTrainWithInitFromCheckpoint(self):
logdir1 = os.path.join(tempfile.mkdtemp(prefix=self.get_temp_dir()),
'tmp_logs1')
logdir2 = os.path.join(tempfile.mkdtemp(prefix=self.get_temp_dir()),
'tmp_logs2')
# First, train the model one step (make sure the error is high).
with ops.Graph().as_default():
random_seed.set_random_seed(0)
train_op = self.create_train_op()
loss = learning.train(train_op, logdir1, number_of_steps=1)
self.assertGreater(loss, .5)
# Next, train the model to convergence.
with ops.Graph().as_default():
random_seed.set_random_seed(1)
train_op = self.create_train_op()
loss = learning.train(train_op,
logdir1,
number_of_steps=300,
log_every_n_steps=10)
self.assertIsNotNone(loss)
self.assertLess(loss, .02)
# Finally, advance the model a single step and validate that the loss is
# still low.
with ops.Graph().as_default():
random_seed.set_random_seed(2)
train_op = self.create_train_op()
model_variables = variables_lib.global_variables()
model_path = os.path.join(logdir1, 'model.ckpt-300')
init_op = variables_lib.global_variables_initializer()
op, init_feed_dict = variables_lib2.assign_from_checkpoint(
model_path, model_variables)
def InitAssignFn(sess):
sess.run(op, init_feed_dict)
loss = learning.train(train_op,
logdir2,
number_of_steps=1,
init_op=init_op,
init_fn=InitAssignFn)
self.assertIsNotNone(loss)
self.assertLess(loss, .02)
def testTrainWithInitFromCheckpoint(self):
logdir1 = os.path.join(
tempfile.mkdtemp(prefix=self.get_temp_dir()), 'tmp_logs1')
logdir2 = os.path.join(
tempfile.mkdtemp(prefix=self.get_temp_dir()), 'tmp_logs2')
# First, train the model one step (make sure the error is high).
with ops.Graph().as_default():
random_seed.set_random_seed(0)
train_op = self.create_train_op()
loss = learning.train(train_op, logdir1, number_of_steps=1)
self.assertGreater(loss, .5)
# Next, train the model to convergence.
with ops.Graph().as_default():
random_seed.set_random_seed(1)
train_op = self.create_train_op()
loss = learning.train(
train_op, logdir1, number_of_steps=300, log_every_n_steps=10)
self.assertIsNotNone(loss)
self.assertLess(loss, .02)
# Finally, advance the model a single step and validate that the loss is
# still low.
with ops.Graph().as_default():
random_seed.set_random_seed(2)
train_op = self.create_train_op()
model_variables = variables_lib.global_variables()
model_path = os.path.join(logdir1, 'model.ckpt-300')
init_op = variables_lib.global_variables_initializer()
op, init_feed_dict = variables_lib2.assign_from_checkpoint(
model_path, model_variables)
def InitAssignFn(sess):
sess.run(op, init_feed_dict)
loss = learning.train(
train_op,
logdir2,
number_of_steps=1,
init_op=init_op,
init_fn=InitAssignFn)
self.assertIsNotNone(loss)
self.assertLess(loss, .02)