Python tensorflow.python.framework.ops 模块，reset_default_graph() 实例源码
我们从Python开源项目中，提取了以下41个代码示例，用于说明如何使用tensorflow.python.framework.ops.reset_default_graph()。
def begin_session(self):
"""
Begins the session
:return: None
"""
# start the tensorflow session
ops.reset_default_graph()
# initialize interactive session
self.sess = tf.Session()
def fit(self, X, y):
y = np.array(y)
unique_y = np.unique(y)
for yi in unique_y:
# Neural network models for user T192 originally did not converge due to parameter initialization
# Use a different seed to choose different initial parameters. The models seem to converge with seed 2016.
if yi == 'T192':
np.random.seed(2016)
Xi = X[y == yi]
target_input = events2keynames(min(Xi, key=len)[:, 0])
if self.align == 'drop':
target_input, _ = character_keys_only(target_input)
self.target_inputs[yi] = target_input
Xi = np.array([fixedtext_features(x, self.target_inputs[yi], align=self.align) for x in Xi])
self.models[yi] = self.model_factory()
if self.feature_normalization == 'stddev':
self.duration_mins[yi] = Xi[:, 0::2].mean() - Xi[:, 0::2].std()
self.duration_maxs[yi] = Xi[:, 0::2].mean() + Xi[:, 0::2].std()
self.latency_mins[yi] = Xi[:, 1::2].mean() - Xi[:, 1::2].std()
self.latency_maxs[yi] = Xi[:, 1::2].mean() + Xi[:, 1::2].std()
elif self.feature_normalization == 'minmax':
self.duration_mins[yi] = Xi[:, 0::2].min()
self.duration_maxs[yi] = Xi[:, 0::2].max()
self.latency_mins[yi] = Xi[:, 1::2].min()
self.latency_maxs[yi] = Xi[:, 1::2].max()
Xi = self.normalize(Xi, yi)
from tensorflow.python.framework import ops
ops.reset_default_graph()
self.models[yi].fit(Xi)
return
def clear_session():
"""Destroys the current TF graph and creates a new one.
Useful to avoid clutter from old models / layers.
"""
global _SESSION
global _GRAPH_LEARNING_PHASES # pylint: disable=global-variable-not-assigned
ops.reset_default_graph()
reset_uids()
_SESSION = None
phase = array_ops.placeholder(dtype='bool', name='keras_learning_phase')
_GRAPH_LEARNING_PHASES = {}
_GRAPH_LEARNING_PHASES[ops.get_default_graph()] = phase
def __setstate__(self, state):
from tensorflow.python.framework import ops
ops.reset_default_graph() # we need to destroy the default graph before re_init or checkpoint won't restore.
self.__init__(state['hyperparams'], state['dO'], state['dU'])
self.policy.scale = state['scale']
self.policy.bias = state['bias']
self.policy.x_idx = state['x_idx']
self.policy.chol_pol_covar = state['chol_pol_covar']
self.tf_iter = state['tf_iter']
with tempfile.NamedTemporaryFile('w+b', delete=True) as f:
f.write(state['wts'])
f.seek(0)
self.restore_model(f.name)
def load_policy(cls, policy_dict_path, tf_generator, network_config=None):
"""
For when we only need to load a policy for the forward pass. For instance, to run on the robot from
a checkpointed policy.
"""
from tensorflow.python.framework import ops
ops.reset_default_graph() # we need to destroy the default graph before re_init or checkpoint won't restore.
pol_dict = pickle.load(open(policy_dict_path, "rb"))
tf_map = tf_generator(dim_input=pol_dict['deg_obs'], dim_output=pol_dict['deg_action'],
batch_size=1, network_config=network_config)
sess = tf.Session()
init_op = tf.initialize_all_variables()
sess.run(init_op)
saver = tf.train.Saver()
check_file = pol_dict['checkpoint_path_tf']
saver.restore(sess, check_file)
device_string = pol_dict['device_string']
cls_init = cls(pol_dict['deg_action'], tf_map.get_input_tensor(), tf_map.get_output_op(), np.zeros((1,)),
sess, device_string)
cls_init.chol_pol_covar = pol_dict['chol_pol_covar']
cls_init.scale = pol_dict['scale']
cls_init.bias = pol_dict['bias']
cls_init.x_idx = pol_dict['x_idx']
return cls_init
def load_policy(cls, policy_dict_path, tf_generator, network_config=None):
"""
For when we only need to load a policy for the forward pass. For instance, to run on the robot from
a checkpointed policy.
"""
from tensorflow.python.framework import ops
ops.reset_default_graph() # we need to destroy the default graph before re_init or checkpoint won't restore.
pol_dict = pickle.load(open(policy_dict_path, "rb"))
tf_map = tf_generator(dim_input=pol_dict['deg_obs'], dim_output=pol_dict['deg_action'],
batch_size=1, network_config=network_config)
sess = tf.Session()
init_op = tf.initialize_all_variables()
sess.run(init_op)
saver = tf.train.Saver()
check_file = pol_dict['checkpoint_path_tf']
saver.restore(sess, check_file)
device_string = pol_dict['device_string']
cls_init = cls(pol_dict['deg_action'], tf_map.get_input_tensor(), tf_map.get_output_op(), np.zeros((1,)),
sess, device_string)
cls_init.chol_pol_covar = pol_dict['chol_pol_covar']
cls_init.scale = pol_dict['scale']
cls_init.bias = pol_dict['bias']
cls_init.x_idx = pol_dict['x_idx']
return cls_init
def reset_state():
# Reset all random seeds, as well as TensorFlow default graph
random.seed(0)
np.random.seed(0)
import tensorflow as tf
from tensorflow.python.framework import ops
tf.set_random_seed(0)
ops.reset_default_graph()
def reset_state():
# Reset all random seeds, as well as TensorFlow default graph
random.seed(0)
np.random.seed(0)
import tensorflow as tf
from tensorflow.python.framework import ops
tf.set_random_seed(0)
ops.reset_default_graph()
def __setstate__(self, state):
from tensorflow.python.framework import ops
ops.reset_default_graph() # we need to destroy the default graph before re_init or checkpoint won't restore.
self.__init__(state['hyperparams'], state['dO'], state['dU'])
self.policy.scale = state['scale']
self.policy.bias = state['bias']
self.tf_iter = state['tf_iter']
saver = tf.train.Saver()
check_file = self.checkpoint_file
saver.restore(self.sess, check_file)
def load_policy(cls, policy_dict_path, tf_generator, network_config=None):
"""
For when we only need to load a policy for the forward pass. For instance, to run on the robot from
a checkpointed policy.
"""
from tensorflow.python.framework import ops
ops.reset_default_graph() # we need to destroy the default graph before re_init or checkpoint won't restore.
pol_dict = pickle.load(open(policy_dict_path, "rb"))
tf_map = tf_generator(dim_input=pol_dict['deg_obs'], dim_output=pol_dict['deg_action'],
batch_size=1, network_config=network_config)
sess = tf.Session()
init_op = tf.initialize_all_variables()
sess.run(init_op)
saver = tf.train.Saver()
check_file = pol_dict['checkpoint_path_tf']
saver.restore(sess, check_file)
device_string = pol_dict['device_string']
cls_init = cls(pol_dict['deg_action'], tf_map.get_input_tensor(), tf_map.get_output_op(), np.zeros((1,)),
sess, device_string)
cls_init.chol_pol_covar = pol_dict['chol_pol_covar']
cls_init.scale = pol_dict['scale']
cls_init.bias = pol_dict['bias']
cls_init.x_idx = pol_dict['x_idx']
return cls_init
def test_copy_assert(self):
ops.reset_default_graph()
a = constant_op.constant(1)
b = constant_op.constant(1)
eq = math_ops.equal(a, b)
assert_op = control_flow_ops.Assert(eq, [a, b])
with ops.control_dependencies([assert_op]):
_ = math_ops.add(a, b)
sgv = ge.make_view([assert_op, eq.op, a.op, b.op])
copier = ge.Transformer()
_, info = copier(sgv, sgv.graph, "", "")
new_assert_op = info.transformed(assert_op)
self.assertIsNotNone(new_assert_op)
def test_graph_replace(self):
ops.reset_default_graph()
a = constant_op.constant(1.0, name="a")
b = variables.Variable(1.0, name="b")
eps = constant_op.constant(0.001, name="eps")
c = array_ops.identity(a + b + eps, name="c")
a_new = constant_op.constant(2.0, name="a_new")
c_new = ge.graph_replace(c, {a: a_new})
with session.Session() as sess:
sess.run(variables.global_variables_initializer())
c_val, c_new_val = sess.run([c, c_new])
self.assertNear(c_val, 2.001, ERROR_TOLERANCE)
self.assertNear(c_new_val, 3.001, ERROR_TOLERANCE)
def test_graph_replace_dict(self):
ops.reset_default_graph()
a = constant_op.constant(1.0, name="a")
b = variables.Variable(1.0, name="b")
eps = constant_op.constant(0.001, name="eps")
c = array_ops.identity(a + b + eps, name="c")
a_new = constant_op.constant(2.0, name="a_new")
c_new = ge.graph_replace({"c": c}, {a: a_new})
self.assertTrue(isinstance(c_new, dict))
with session.Session() as sess:
sess.run(variables.global_variables_initializer())
c_val, c_new_val = sess.run([c, c_new])
self.assertTrue(isinstance(c_new_val, dict))
self.assertNear(c_val, 2.001, ERROR_TOLERANCE)
self.assertNear(c_new_val["c"], 3.001, ERROR_TOLERANCE)
def test_graph_replace_ordered_dict(self):
ops.reset_default_graph()
a = constant_op.constant(1.0, name="a")
b = variables.Variable(1.0, name="b")
eps = constant_op.constant(0.001, name="eps")
c = array_ops.identity(a + b + eps, name="c")
a_new = constant_op.constant(2.0, name="a_new")
c_new = ge.graph_replace(collections.OrderedDict({"c": c}), {a: a_new})
self.assertTrue(isinstance(c_new, collections.OrderedDict))
def test_graph_replace_named_tuple(self):
ops.reset_default_graph()
a = constant_op.constant(1.0, name="a")
b = variables.Variable(1.0, name="b")
eps = constant_op.constant(0.001, name="eps")
c = array_ops.identity(a + b + eps, name="c")
a_new = constant_op.constant(2.0, name="a_new")
one_tensor = collections.namedtuple("OneTensor", ["t"])
c_new = ge.graph_replace(one_tensor(c), {a: a_new})
self.assertTrue(isinstance(c_new, one_tensor))
def testOutputSizeRandomSizesAndStridesValidPadding(self):
np.random.seed(0)
max_image_size = 10
for _ in range(10):
num_filters = 1
input_size = [
1, np.random.randint(1, max_image_size),
np.random.randint(1, max_image_size), 1
]
filter_size = [
np.random.randint(1, input_size[1] + 1),
np.random.randint(1, input_size[2] + 1)
]
stride = [np.random.randint(1, 3), np.random.randint(1, 3)]
ops.reset_default_graph()
graph = ops.Graph()
with graph.as_default():
images = random_ops.random_uniform(input_size, seed=1)
transpose = layers_lib.conv2d_transpose(
images, num_filters, filter_size, stride=stride, padding='VALID')
conv = layers_lib.conv2d(
transpose, num_filters, filter_size, stride=stride, padding='VALID')
with self.test_session(graph=graph) as sess:
sess.run(variables_lib.global_variables_initializer())
self.assertListEqual(list(conv.eval().shape), input_size)
def setUp(self):
ops.reset_default_graph()
def setUp(self):
ops.reset_default_graph()
def setUp(self):
ops.reset_default_graph()
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
self._batch_size = 4
self._num_classes = 3
self._np_predictions = np.matrix(('0.1 0.2 0.7;'
'0.6 0.2 0.2;'
'0.0 0.9 0.1;'
'0.2 0.0 0.8'))
self._np_labels = [0, 0, 0, 0]
def setUp(self):
ops.reset_default_graph()
def setUp(self):
ops.reset_default_graph()
def setUp(self):
ops.reset_default_graph()
def setUp(self):
ops.reset_default_graph()
def setUp(self):
ops.reset_default_graph()
def setUp(self):
ops.reset_default_graph()
def setUp(self):
ops.reset_default_graph()
def setUp(self):
np.random.seed(1)
ops.reset_default_graph()
def setUp(self):
ops.reset_default_graph()
def close(self):
# If training, save the RAM memory to file
if self._is_training:
self._saver.save(self._session, self._PARAMETERS_FILE_PATH)
temp_copy = []
while self._previous_observations:
temp_copy.append(self._previous_observations.pop())
while len(temp_copy) > MAX_OBSERVATIONS_IN_FILE:
file_copy = []
for _ in range(0, MAX_OBSERVATIONS_IN_FILE):
file_copy.append(temp_copy.pop())
np.save('obs_' + str(self._number_files) + '.npy', file_copy)
self._number_files += 1
del file_copy
np.save('obs_' + str(self._number_files) + '.npy', temp_copy)
print('\nTotal number of saved file containing transitions:',
self._number_files + 1)
del temp_copy
# Close the session and clear TensorFlow's graphs
ops.reset_default_graph()
self._session.close()
# Plot the graph of the average Implied/Realized reward ratio
MA = 50 # moving average parameter
if len(self._implied_realized_reward_ratio) > MA:
plt.figure()
plt.subplot(111)
title = plt.title(('History of implied/realized reward ratio'),
fontsize="x-large")
line = self._implied_realized_reward_ratio.copy()
average = [np.mean(line[i:i+MA]) for i in range(len(line)-MA)]
dt = [i for i in range(len(line))]
plt.plot(dt, line, 'r',
label='Individual observations')
plt.plot(dt[MA:], average, 'b',
label='50-observation moving average')
plt.axis([0, len(line), np.min(average)*0.5, np.max(average)*2.])
title.set_y(1.0)
plt.legend()
plt.show()