Python tensorflow.Graph方法代码示例

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def load_graph(frozen_graph_filename):
    # We load the protobuf file from the disk and parse it to retrieve the
    # unserialized graph_def
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    # Then, we can use again a convenient built-in function to import a graph_def into the
    # current default Graph
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(
            graph_def,
            input_map=None,
            return_elements=None,
            name="",
            op_dict=None,
            producer_op_list=None
        )
    return graph 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def test_mnist_tutorial_pytorch(self):
        import tensorflow as tf
        from cleverhans_tutorials import mnist_tutorial_pytorch

        # Run the MNIST tutorial on a dataset of reduced size
        with tf.Graph().as_default():
            np.random.seed(42)
            report = mnist_tutorial_pytorch.mnist_tutorial(
                nb_epochs=2,
                train_end=5000,
                test_end=333,
            )

        # Check accuracy values contained in the AccuracyReport object
        self.assertGreater(report.clean_train_clean_eval, 0.9)
        self.assertLess(report.clean_train_adv_eval, 0.10) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def __init__(self, params_descriptor, MWF=False, stft_backend="auto", multiprocess=True):
        """ Default constructor.

        :param params_descriptor: Descriptor for TF params to be used.
        :param MWF: (Optional) True if MWF should be used, False otherwise.
        """

        self._params = load_configuration(params_descriptor)
        self._sample_rate = self._params['sample_rate']
        self._MWF = MWF
        self._tf_graph = tf.Graph()
        self._predictor = None
        self._input_provider = None
        self._builder = None
        self._features = None
        self._session = None
        self._pool = Pool() if multiprocess else None
        self._tasks = []
        self._params["stft_backend"] = get_backend(stft_backend) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 299, 299
    endpoints = ['Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3',
                 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3',
                 'MaxPool_5a_3x3', 'Mixed_5b', 'Mixed_6a',
                 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_resnet_v2_base(
            inputs, final_endpoint=endpoint)
        if endpoint != 'PreAuxLogits':
          self.assertTrue(out_tensor.op.name.startswith(
              'InceptionResnetV2/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_0',
                 'Conv2d_1_depthwise', 'Conv2d_1_pointwise',
                 'Conv2d_2_depthwise', 'Conv2d_2_pointwise',
                 'Conv2d_3_depthwise', 'Conv2d_3_pointwise',
                 'Conv2d_4_depthwise', 'Conv2d_4_pointwise',
                 'Conv2d_5_depthwise', 'Conv2d_5_pointwise',
                 'Conv2d_6_depthwise', 'Conv2d_6_pointwise',
                 'Conv2d_7_depthwise', 'Conv2d_7_pointwise',
                 'Conv2d_8_depthwise', 'Conv2d_8_pointwise',
                 'Conv2d_9_depthwise', 'Conv2d_9_pointwise',
                 'Conv2d_10_depthwise', 'Conv2d_10_pointwise',
                 'Conv2d_11_depthwise', 'Conv2d_11_pointwise',
                 'Conv2d_12_depthwise', 'Conv2d_12_pointwise',
                 'Conv2d_13_depthwise', 'Conv2d_13_pointwise']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = mobilenet_v1.mobilenet_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'MobilenetV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def testBuildOnlyUpToFinalEndpoint(self):
    batch_size = 5
    height, width = 299, 299
    all_endpoints = [
        'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'Mixed_3a',
        'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d',
        'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
        'Mixed_6e', 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a',
        'Mixed_7b', 'Mixed_7c', 'Mixed_7d']
    for index, endpoint in enumerate(all_endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v4_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV4/' + endpoint))
        self.assertItemsEqual(all_endpoints[:index+1], end_points) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def testBuildOnlyUptoFinalEndpoint(self):
    batch_size = 5
    height, width = 224, 224
    endpoints = ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1',
                 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c',
                 'MaxPool_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d',
                 'Mixed_4e', 'Mixed_4f', 'MaxPool_5a_2x2', 'Mixed_5b',
                 'Mixed_5c']
    for index, endpoint in enumerate(endpoints):
      with tf.Graph().as_default():
        inputs = tf.random_uniform((batch_size, height, width, 3))
        out_tensor, end_points = inception.inception_v1_base(
            inputs, final_endpoint=endpoint)
        self.assertTrue(out_tensor.op.name.startswith(
            'InceptionV1/' + endpoint))
        self.assertItemsEqual(endpoints[:index+1], end_points) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def testCreateLogisticClassifier(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = LogisticClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      clone = clones[0]
      self.assertEqual(len(slim.get_variables()), 2)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, 'CPU:0')
        self.assertDeviceEqual(v.value().device, 'CPU:0')
      self.assertEqual(clone.outputs.op.name,
                       'LogisticClassifier/fully_connected/Sigmoid')
      self.assertEqual(clone.scope, '')
      self.assertDeviceEqual(clone.device, 'GPU:0')
      self.assertEqual(len(slim.losses.get_losses()), 1)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(update_ops, []) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def testCreateSingleclone(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      clone = clones[0]
      self.assertEqual(len(slim.get_variables()), 5)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, 'CPU:0')
        self.assertDeviceEqual(v.value().device, 'CPU:0')
      self.assertEqual(clone.outputs.op.name,
                       'BatchNormClassifier/fully_connected/Sigmoid')
      self.assertEqual(clone.scope, '')
      self.assertDeviceEqual(clone.device, 'GPU:0')
      self.assertEqual(len(slim.losses.get_losses()), 1)
      update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
      self.assertEqual(len(update_ops), 2) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def testCreateOnecloneWithPS(self):
    g = tf.Graph()
    with g.as_default():
      tf.set_random_seed(0)
      tf_inputs = tf.constant(self._inputs, dtype=tf.float32)
      tf_labels = tf.constant(self._labels, dtype=tf.float32)

      model_fn = BatchNormClassifier
      clone_args = (tf_inputs, tf_labels)
      deploy_config = model_deploy.DeploymentConfig(num_clones=1,
                                                    num_ps_tasks=1)

      self.assertEqual(slim.get_variables(), [])
      clones = model_deploy.create_clones(deploy_config, model_fn, clone_args)
      self.assertEqual(len(clones), 1)
      clone = clones[0]
      self.assertEqual(clone.outputs.op.name,
                       'BatchNormClassifier/fully_connected/Sigmoid')
      self.assertDeviceEqual(clone.device, '/job:worker/device:GPU:0')
      self.assertEqual(clone.scope, '')
      self.assertEqual(len(slim.get_variables()), 5)
      for v in slim.get_variables():
        self.assertDeviceEqual(v.device, '/job:ps/task:0/CPU:0')
        self.assertDeviceEqual(v.device, v.value().device) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def testNoSummariesOnGPU(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      model = model_deploy.deploy(
          deploy_config, ModelFn,
          optimizer=tf.train.GradientDescentOptimizer(1.0))
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def testNoSummariesOnGPUForEvals(self):
    with tf.Graph().as_default():
      deploy_config = model_deploy.DeploymentConfig(num_clones=2)

      # clone function creates a fully_connected layer with a regularizer loss.
      def ModelFn():
        inputs = tf.constant(1.0, shape=(10, 20), dtype=tf.float32)
        reg = tf.contrib.layers.l2_regularizer(0.001)
        tf.contrib.layers.fully_connected(inputs, 30, weights_regularizer=reg)

      # No optimizer here, it's an eval.
      model = model_deploy.deploy(deploy_config, ModelFn)
      # The model summary op should have a few summary inputs and all of them
      # should be on the CPU.
      self.assertTrue(model.summary_op.op.inputs)
      for inp in  model.summary_op.op.inputs:
        self.assertEqual('/device:CPU:0', inp.device) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def main(_):
  if not FLAGS.output_file:
    raise ValueError('You must supply the path to save to with --output_file')
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default() as graph:
    dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train',
                                          FLAGS.dataset_dir)
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=FLAGS.is_training)
    if hasattr(network_fn, 'default_image_size'):
      image_size = network_fn.default_image_size
    else:
      image_size = FLAGS.default_image_size
    placeholder = tf.placeholder(name='input', dtype=tf.float32,
                                 shape=[1, image_size, image_size, 3])
    network_fn(placeholder)
    graph_def = graph.as_graph_def()
    with gfile.GFile(FLAGS.output_file, 'wb') as f:
      f.write(graph_def.SerializeToString()) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def main(_):
  """Train a word2vec model."""
  if not FLAGS.train_data or not FLAGS.eval_data or not FLAGS.save_path:
    print("--train_data --eval_data and --save_path must be specified.")
    sys.exit(1)
  opts = Options()
  with tf.Graph().as_default(), tf.Session() as session:
    with tf.device("/cpu:0"):
      model = Word2Vec(opts, session)
      model.read_analogies() # Read analogy questions
    for _ in xrange(opts.epochs_to_train):
      model.train()  # Process one epoch
      model.eval()  # Eval analogies.
    # Perform a final save.
    model.saver.save(session, os.path.join(opts.save_path, "model.ckpt"),
                     global_step=model.global_step)
    if FLAGS.interactive:
      # E.g.,
      # [0]: model.analogy(b'france', b'paris', b'russia')
      # [1]: model.nearby([b'proton', b'elephant', b'maxwell'])
      _start_shell(locals()) 

# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import Graph [as 别名]
def testAttachDataReader(self):
    """Checks that train['run'] and 'annotations' call AttachDataReader."""
    test_name = 'attach-data-reader'

    with tf.Graph().as_default():
      builder, target = self.getBuilderAndTarget(test_name)
      train = builder.add_training_from_config(target)
      anno = builder.add_annotation(test_name)

      # AttachDataReader should be called between GetSession and ReleaseSession.
      self.checkOpOrder('train', train['run'],
                        ['GetSession', 'AttachDataReader', 'ReleaseSession'])

      # A similar contract applies to the annotations.
      self.checkOpOrder('annotations', anno['annotations'],
                        ['GetSession', 'AttachDataReader', 'ReleaseSession'])