Python ops.reset_default_graph方法代码示例

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def testClearDevices(self):
    export_dir = os.path.join(test.get_temp_dir(), "test_clear_devices")
    builder = saved_model_builder.SavedModelBuilder(export_dir)

    # Specify a device and save a variable.
    ops.reset_default_graph()
    with session.Session(
        target="",
        config=config_pb2.ConfigProto(device_count={"CPU": 2})) as sess:
      with sess.graph.device("/cpu:0"):
        self._init_and_validate_variable(sess, "v", 42)
        builder.add_meta_graph_and_variables(
            sess, [tag_constants.TRAINING], clear_devices=True)

    # Save the SavedModel to disk.
    builder.save()

    # Restore the graph with a single predefined tag whose variables were saved
    # without any device information.
    with self.test_session(graph=ops.Graph()) as sess:
      loader.load(sess, [tag_constants.TRAINING], export_dir)
      self.assertEqual(
          42, ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)[0].eval()) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def testUnknownImageShape(self):
    ops.reset_default_graph()
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = array_ops.placeholder(
          dtypes.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception_v2.inception_v2(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_5c']
      feed_dict = {inputs: input_np}
      variables.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def testUnknownImageShape(self):
    ops.reset_default_graph()
    batch_size = 2
    height, width = 299, 299
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = array_ops.placeholder(
          dtypes.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception_v3.inception_v3(inputs, num_classes)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_7c']
      feed_dict = {inputs: input_np}
      variables.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048]) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def testUnknownImageShape(self):
    ops.reset_default_graph()
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.test_session() as sess:
      inputs = array_ops.placeholder(
          dtypes.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception_v1.inception_v1(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_5c']
      feed_dict = {inputs: input_np}
      variables.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def testLegacyBasic(self):
    base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH)
    ops.reset_default_graph()
    sess, meta_graph_def = (
        bundle_shim.load_session_bundle_or_saved_model_bundle_from_path(
            base_path,
            tags=[""],
            target="",
            config=config_pb2.ConfigProto(device_count={"CPU": 2})))

    self.assertTrue(sess)
    asset_path = os.path.join(base_path, constants.ASSETS_DIRECTORY)
    with sess.as_default():
      path1, path2 = sess.run(["filename1:0", "filename2:0"])
      self.assertEqual(
          compat.as_bytes(os.path.join(asset_path, "hello1.txt")), path1)
      self.assertEqual(
          compat.as_bytes(os.path.join(asset_path, "hello2.txt")), path2)

      collection_def = meta_graph_def.collection_def

      signatures_any = collection_def[constants.SIGNATURES_KEY].any_list.value
      self.assertEqual(len(signatures_any), 1) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def testSavedModelBasic(self):
    base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
    ops.reset_default_graph()
    sess, meta_graph_def = (
        bundle_shim.load_session_bundle_or_saved_model_bundle_from_path(
            base_path,
            tags=[tag_constants.SERVING],
            target="",
            config=config_pb2.ConfigProto(device_count={"CPU": 2})))

    self.assertTrue(sess)

    # Check basic signature def property.
    signature_def = meta_graph_def.signature_def
    self.assertEqual(len(signature_def), 2)
    self.assertEqual(
        signature_def[signature_constants.REGRESS_METHOD_NAME].method_name,
        signature_constants.REGRESS_METHOD_NAME)
    signature = signature_def["tensorflow/serving/regress"]
    asset_path = os.path.join(base_path, saved_model_constants.ASSETS_DIRECTORY)
    with sess.as_default():
      output1 = sess.run(["filename_tensor:0"])
      self.assertEqual(["foo.txt"], output1) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def testUnknownImageShape(self):
    ops.reset_default_graph()
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.cached_session() as sess:
      inputs = array_ops.placeholder(
          dtypes.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception_v2.inception_v2(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionV2/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_5c']
      feed_dict = {inputs: input_np}
      variables.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def testUnknownImageShape(self):
    ops.reset_default_graph()
    batch_size = 2
    height, width = 299, 299
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.cached_session() as sess:
      inputs = array_ops.placeholder(
          dtypes.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception_v3.inception_v3(inputs, num_classes)
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_7c']
      feed_dict = {inputs: input_np}
      variables.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048]) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def testUnknownImageShape(self):
    ops.reset_default_graph()
    batch_size = 2
    height, width = 224, 224
    num_classes = 1000
    input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
    with self.cached_session() as sess:
      inputs = array_ops.placeholder(
          dtypes.float32, shape=(batch_size, None, None, 3))
      logits, end_points = inception_v1.inception_v1(inputs, num_classes)
      self.assertTrue(logits.op.name.startswith('InceptionV1/Logits'))
      self.assertListEqual(logits.get_shape().as_list(),
                           [batch_size, num_classes])
      pre_pool = end_points['Mixed_5c']
      feed_dict = {inputs: input_np}
      variables.global_variables_initializer().run()
      pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
      self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024]) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def testCreateDropoutWithPlaceholder(self):
    height, width = 3, 3
    tf.reset_default_graph()
    with self.cached_session():
      is_training = array_ops.placeholder(dtype=dtypes.bool, shape=[])
      images = random_ops.random_uniform((5, height, width, 3), seed=1)
      # this verifies that that we've inserted cond properly.
      output = _layers.dropout(images, is_training=is_training)
      # In control_flow_v2 the op is called "If" and it is behind
      # identity op. In legacy mode cond we just go by name.
      # Might need to do something more robust here eventually.
      is_cond_op = (output.op.inputs[0].op.type == 'If' or
                    output.op.name == 'Dropout/cond/Merge')
      self.assertTrue(is_cond_op,
                      'Expected cond_op got ' + repr(output))
      output.get_shape().assert_is_compatible_with(images.get_shape()) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def begin_session(self):
        """
        Begins the session

        :return: None
        """
        # start the tensorflow session
        ops.reset_default_graph()
        # initialize interactive session
        self.sess = tf.Session() 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def tearDown(self):
    ops.reset_default_graph()

    # Tear down temporary dump directory.
    if os.path.isdir(self._dump_root):
      shutil.rmtree(self._dump_root) 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def setUp(self):
    self._ClearCachedSession()
    random.seed(random_seed.DEFAULT_GRAPH_SEED)
    np.random.seed(random_seed.DEFAULT_GRAPH_SEED)
    ops.reset_default_graph()
    ops.get_default_graph().seed = random_seed.DEFAULT_GRAPH_SEED 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
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 

# 需要导入模块: from tensorflow.python.framework import ops [as 别名]
# 或者: from tensorflow.python.framework.ops import reset_default_graph [as 别名]
def tearDown(self):
    # Tear down temporary dump directory.
    shutil.rmtree(self._dump_root)

    ops.reset_default_graph()