Python v1.global_variables_initializer方法代码示例

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def testAppendGradientsWithLossScaleWithAutoScaleDisabled(self):
    v = tf.Variable(0)
    training_ops = []
    get_apply_gradients_ops_func = lambda: [tf.assign(v, v + 1)]
    loss_scale_params = variable_mgr_util.AutoLossScaleParams(
        enable_auto_loss_scale=False,  # no auto loss scale.
        loss_scale=tf.Variable(4),
        loss_scale_normal_steps=tf.Variable(10),
        inc_loss_scale_every_n=10,
        is_chief=True)
    variable_mgr_util.append_gradients_with_loss_scale(
        training_ops,
        get_apply_gradients_ops_func,
        loss_scale_params,
        grad_has_inf_nan=True)

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      sess.run(training_ops)
      self.assertEqual(sess.run(v), 1)
      self.assertEqual(sess.run(loss_scale_params.loss_scale), 4)
      self.assertEqual(sess.run(loss_scale_params.loss_scale_normal_steps), 10) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def testAppendGradientsWithLossScaleForNonChiefWorker(self):
    v = tf.Variable(0)
    training_ops = []
    get_apply_gradients_ops_func = lambda: [tf.assign(v, v + 1)]
    loss_scale_params = variable_mgr_util.AutoLossScaleParams(
        enable_auto_loss_scale=True,
        loss_scale=tf.Variable(4),
        loss_scale_normal_steps=tf.Variable(10),
        inc_loss_scale_every_n=10,
        is_chief=False)  # Non-chief
    variable_mgr_util.append_gradients_with_loss_scale(
        training_ops,
        get_apply_gradients_ops_func,
        loss_scale_params,
        grad_has_inf_nan=False)

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      sess.run(training_ops)
      self.assertEqual(sess.run(v), 1)
      self.assertEqual(sess.run(loss_scale_params.loss_scale), 4)
      self.assertEqual(sess.run(loss_scale_params.loss_scale_normal_steps), 10) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def testAppendGradientsWithLossScaleWithtNan(self):
    v = tf.Variable(0)
    training_ops = []
    get_apply_gradients_ops_func = lambda: [tf.assign(v, v + 1)]
    loss_scale_params = variable_mgr_util.AutoLossScaleParams(
        enable_auto_loss_scale=True,
        loss_scale=tf.Variable(4, dtype=tf.float32),
        loss_scale_normal_steps=tf.Variable(10),
        inc_loss_scale_every_n=10,
        is_chief=True)
    variable_mgr_util.append_gradients_with_loss_scale(
        training_ops,
        get_apply_gradients_ops_func,
        loss_scale_params,
        grad_has_inf_nan=tf.constant(True))

    with self.test_session() as sess:
      sess.run(tf.global_variables_initializer())
      sess.run(training_ops)
      self.assertEqual(sess.run(v), 0)  # Skip updating for v.
      # halve loss_scale and reset local_scale_normal_steps.
      self.assertEqual(sess.run(loss_scale_params.loss_scale), 2)
      self.assertEqual(sess.run(loss_scale_params.loss_scale_normal_steps), 0) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def evaluate(self, env_fn, hparams, sampling_temp):
    with tf.Graph().as_default():
      with tf.name_scope("rl_eval"):
        eval_env = env_fn(in_graph=True)
        (collect_memory, _, collect_init) = _define_collect(
            eval_env,
            hparams,
            "ppo_eval",
            eval_phase=True,
            frame_stack_size=self.frame_stack_size,
            force_beginning_resets=False,
            sampling_temp=sampling_temp,
            distributional_size=self._distributional_size,
        )
        model_saver = tf.train.Saver(
            tf.global_variables(hparams.policy_network + "/.*")
            # tf.global_variables("clean_scope.*")  # Needed for sharing params.
        )

        with tf.Session() as sess:
          sess.run(tf.global_variables_initializer())
          collect_init(sess)
          trainer_lib.restore_checkpoint(self.agent_model_dir, model_saver,
                                         sess)
          sess.run(collect_memory) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def __init__(self, hparams, action_space, observation_space, policy_dir):
    assert hparams.base_algo == "ppo"
    ppo_hparams = trainer_lib.create_hparams(hparams.base_algo_params)

    frame_stack_shape = (1, hparams.frame_stack_size) + observation_space.shape
    self._frame_stack = np.zeros(frame_stack_shape, dtype=np.uint8)

    with tf.Graph().as_default():
      self.obs_t = tf.placeholder(shape=self.frame_stack_shape, dtype=np.uint8)
      self.logits_t, self.value_function_t = get_policy(
          self.obs_t, ppo_hparams, action_space
      )
      model_saver = tf.train.Saver(
          tf.global_variables(scope=ppo_hparams.policy_network + "/.*")  # pylint: disable=unexpected-keyword-arg
      )
      self.sess = tf.Session()
      self.sess.run(tf.global_variables_initializer())
      trainer_lib.restore_checkpoint(policy_dir, model_saver,
                                     self.sess) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def __init__(
      self, batch_size, observation_space, action_space, policy_hparams,
      policy_dir, sampling_temp
  ):
    super(PolicyAgent, self).__init__(
        batch_size, observation_space, action_space
    )
    self._sampling_temp = sampling_temp
    with tf.Graph().as_default():
      self._observations_t = tf.placeholder(
          shape=((batch_size,) + self.observation_space.shape),
          dtype=self.observation_space.dtype
      )
      (logits, self._values_t) = rl.get_policy(
          self._observations_t, policy_hparams, self.action_space
      )
      actions = common_layers.sample_with_temperature(logits, sampling_temp)
      self._probs_t = tf.nn.softmax(logits / sampling_temp)
      self._actions_t = tf.cast(actions, tf.int32)
      model_saver = tf.train.Saver(
          tf.global_variables(policy_hparams.policy_network + "/.*")  # pylint: disable=unexpected-keyword-arg
      )
      self._sess = tf.Session()
      self._sess.run(tf.global_variables_initializer())
      trainer_lib.restore_checkpoint(policy_dir, model_saver, self._sess) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def testAccuracyTopKMetric(self):
    predictions = np.random.randint(1, 5, size=(12, 12, 12, 1))
    targets = np.random.randint(1, 5, size=(12, 12, 12, 1))
    expected = np.mean((predictions == targets).astype(float))
    with self.test_session() as session:
      predicted = tf.one_hot(predictions, depth=5, dtype=tf.float32)
      scores1, _ = metrics.padded_accuracy_topk(
          predicted, tf.constant(targets, dtype=tf.int32), k=1)
      scores2, _ = metrics.padded_accuracy_topk(
          predicted, tf.constant(targets, dtype=tf.int32), k=7)
      a1 = tf.reduce_mean(scores1)
      a2 = tf.reduce_mean(scores2)
      session.run(tf.global_variables_initializer())
      actual1, actual2 = session.run([a1, a2])
    self.assertAlmostEqual(actual1, expected)
    self.assertAlmostEqual(actual2, 1.0) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def testSequenceEditDistanceMetric(self):
    predictions = np.array([[3, 4, 5, 1, 0, 0],
                            [2, 1, 3, 4, 0, 0],
                            [2, 1, 3, 4, 0, 0]])
    # Targets are just a bit different:
    #  - first sequence has a different prediction
    #  - second sequence has a different prediction and one extra step
    #  - third sequence is identical
    targets = np.array([[5, 4, 5, 1, 0, 0],
                        [2, 5, 3, 4, 1, 0],
                        [2, 1, 3, 4, 0, 0]])
    # Reshape to match expected input format by metric fns.
    predictions = np.reshape(predictions, [3, 6, 1, 1])
    targets = np.reshape(targets, [3, 6, 1, 1])
    with self.test_session() as session:
      scores, weight = metrics.sequence_edit_distance(
          tf.one_hot(predictions, depth=6, dtype=tf.float32),
          tf.constant(targets, dtype=tf.int32))
      session.run(tf.global_variables_initializer())
      actual_scores, actual_weight = session.run([scores, weight])
    self.assertAlmostEqual(actual_scores, 3.0 / 13)
    self.assertEqual(actual_weight, 13) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def testNegativeLogPerplexityMaskedAssert(self):
    predictions = np.random.randint(4, size=(12, 12, 12, 1))
    targets = np.random.randint(4, size=(12, 12, 12, 1))
    features = {}

    with self.assertRaisesRegexp(
        ValueError,
        'masked_neg_log_perplexity requires targets_mask feature'):
      with self.test_session() as session:
        scores, _ = metrics.padded_neg_log_perplexity_with_masking(
            tf.one_hot(predictions, depth=4, dtype=tf.float32),
            tf.constant(targets, dtype=tf.int32),
            features)
        a = tf.reduce_mean(scores)
        session.run(tf.global_variables_initializer())
        _ = session.run(a) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def testSigmoidAccuracyOneHot(self):
    logits = np.array([
        [-1., 1.],
        [1., -1.],
        [-1., 1.],
        [1., -1.]
    ])
    labels = np.array([
        [0, 1],
        [1, 0],
        [1, 0],
        [0, 1]
    ])
    logits = np.expand_dims(np.expand_dims(logits, 1), 1)
    labels = np.expand_dims(np.expand_dims(labels, 1), 1)

    with self.test_session() as session:
      score, _ = metrics.sigmoid_accuracy_one_hot(logits, labels)
      session.run(tf.global_variables_initializer())
      session.run(tf.local_variables_initializer())
      s = session.run(score)
    self.assertEqual(s, 0.5) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def testSigmoidPrecisionOneHot(self):
    logits = np.array([
        [-1., 1.],
        [1., -1.],
        [1., -1.],
        [1., -1.]
    ])
    labels = np.array([
        [0, 1],
        [0, 1],
        [0, 1],
        [0, 1]
    ])
    logits = np.expand_dims(np.expand_dims(logits, 1), 1)
    labels = np.expand_dims(np.expand_dims(labels, 1), 1)

    with self.test_session() as session:
      score, _ = metrics.sigmoid_precision_one_hot(logits, labels)
      session.run(tf.global_variables_initializer())
      session.run(tf.local_variables_initializer())
      s = session.run(score)
    self.assertEqual(s, 0.25) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def testSigmoidRecallOneHot(self):
    logits = np.array([
        [-1., 1.],
        [1., -1.],
        [1., -1.],
        [1., -1.]
    ])
    labels = np.array([
        [0, 1],
        [0, 1],
        [0, 1],
        [0, 1]
    ])
    logits = np.expand_dims(np.expand_dims(logits, 1), 1)
    labels = np.expand_dims(np.expand_dims(labels, 1), 1)

    with self.test_session() as session:
      score, _ = metrics.sigmoid_recall_one_hot(logits, labels)
      session.run(tf.global_variables_initializer())
      session.run(tf.local_variables_initializer())
      s = session.run(score)
    self.assertEqual(s, 0.25) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def testSigmoidCrossEntropyOneHot(self):
    logits = np.array([
        [-1., 1.],
        [1., -1.],
        [1., -1.],
        [1., -1.]
    ])
    labels = np.array([
        [0, 1],
        [1, 0],
        [0, 0],
        [0, 1]
    ])
    logits = np.expand_dims(np.expand_dims(logits, 1), 1)
    labels = np.expand_dims(np.expand_dims(labels, 1), 1)

    with self.test_session() as session:
      score, _ = metrics.sigmoid_cross_entropy_one_hot(logits, labels)
      session.run(tf.global_variables_initializer())
      session.run(tf.local_variables_initializer())
      s = session.run(score)
    self.assertAlmostEqual(s, 0.688, places=3) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def testRocAuc(self):
    logits = np.array([
        [-1., 1.],
        [1., -1.],
        [1., -1.],
        [1., -1.]
    ])
    labels = np.array([
        [1],
        [0],
        [1],
        [0]
    ])
    logits = np.expand_dims(np.expand_dims(logits, 1), 1)
    labels = np.expand_dims(np.expand_dims(labels, 1), 1)

    with self.test_session() as session:
      score, _ = metrics.roc_auc(logits, labels)
      session.run(tf.global_variables_initializer())
      session.run(tf.local_variables_initializer())
      s = session.run(score)
    self.assertAlmostEqual(s, 0.750, places=3) 

# 需要导入模块: from tensorflow.compat import v1 [as 别名]
# 或者: from tensorflow.compat.v1 import global_variables_initializer [as 别名]
def testMultilabelMatch3(self):
    predictions = np.random.randint(1, 5, size=(100, 1, 1, 1))
    targets = np.random.randint(1, 5, size=(100, 10, 1, 1))
    weights = np.random.randint(0, 2, size=(100, 1, 1, 1))
    targets *= weights

    predictions_repeat = np.repeat(predictions, 10, axis=1)
    expected = (predictions_repeat == targets).astype(float)
    expected = np.sum(expected, axis=(1, 2, 3))
    expected = np.minimum(expected / 3.0, 1.)
    expected = np.sum(expected * weights[:, 0, 0, 0]) / weights.shape[0]
    with self.test_session() as session:
      scores, weights_ = metrics.multilabel_accuracy_match3(
          tf.one_hot(predictions, depth=5, dtype=tf.float32),
          tf.constant(targets, dtype=tf.int32))
      a, a_op = tf.metrics.mean(scores, weights_)
      session.run(tf.local_variables_initializer())
      session.run(tf.global_variables_initializer())
      _ = session.run(a_op)
      actual = session.run(a)
    self.assertAlmostEqual(actual, expected, places=6)